The 100-year life course approach is a human lifespan and policy framework that rethinks education, work, and social policy in response to longer life expectancy, rapid technological change, and non-linear career pathways. Rather than assuming a traditional three-stage life model—education in youth, work in midlife, retirement in later years—this approach recognizes that many individuals may live and work productively for close to a century, often across multiple careers, roles, and life transitions.

This approach emphasizes planned, periodic reinvestment in learning, skills, and adaptability throughout adulthood, treating mid-career transitions, reskilling, and reinvention as normal and necessary. It provides the policy logic that underpins a learning society, informing the design of age-inclusive education systems, workforce strategies, and social supports that enable individuals to remain engaged, capable, and contributory across a longer life course. Policies and institutions are designed to support adults at every age—whether returning to education at 40, reskilling at 55, or contributing knowledge and mentoring at 70 and beyond. This approach aligns closely with workforce resilience, social mobility, age-inclusive practices, and intergenerational knowledge exchange.

Key features of a 100-year life course framework:

• Multi-stage lives: Individuals move through recurring cycles of learning, work, caregiving, and reinvention rather than fixed, linear stages.
• Lifelong learning systems: Education providers, employers, and communities offer flexible, modular, and stackable learning opportunities aligned to different life stages.
• Extended working lives: Longer careers require periodic reinvestment in skills, health, and adaptability, rather than early exit from the workforce.
• Mid-career transitions are the norm: Career shifts, not just promotions, are expected and supported through accessible learning pathways.
• Shared responsibility: Individuals, employers, educational institutions, and governments collectively invest in ongoing skill development and learning access.

In the context of rapid automation and AI-driven change, the 100-year life course model underscores the importance of continuous capability renewal, ensuring that individuals can remain engaged, productive, and adaptable throughout longer lives.

See also: Learning Society | Learn & Work Ecosystem Library

A work culture in which employees are expected to work long hours—often 9 a.m. to 9 p.m., six days a week—emphasizing extreme dedication and constant productivity as the path to success. Common in tech, startups, and fast-paced industries, it can lead to burnout, health risks, reduced creativity, and declining overall productivity. While initially framed as a marker of commitment, this culture often fosters resentment and work-life imbalance.

The term originated in China with companies like Alibaba, but has become shorthand for extreme work expectations and a broader “hustle culture,” in which long hours and constant productivity are celebrated as markers of dedication and loyalty. Economic pressures, layoffs, AI adoption, and hybrid work policies in the U.S. may drive adoption of similar high-hour work cultures, though evidence suggests these strategies are often counterproductive.

Common characteristics:

• Long workweeks and extended daily hours.
• Pressure to continuously deliver results and meet aggressive performance metrics.
• Social and organizational reinforcement of “hardcore” dedication.
• Often linked to “toxic productivity,” where workers feel compelled to work regardless of personal well-being.
• Encourages visible effort over efficiency or creativity.

Research indicates that while 996 culture may initially foster a sense of unity and shared purpose, it often leads to resentment and diminished engagement over time. Other concerns:

• Declines in health, motivation, creativity, and overall productivity after approximately 50–55 hours per week.
• Increased risk of burnout, cardiovascular issues, and reduced work satisfaction.
• Exclusionary effects, particularly for employees with caregiving responsibilities, disproportionately affecting women.
• Can erode long-term organizational performance as quality of work diminishes and turnover rises.

Conversely, alternative models, such as reduced or flexible workweeks, have been shown to improve productivity, health, and employee satisfaction.

Academic advising is the collaborative process by which students engage with a member of their institution (professor, mentor, or advisor) to receive direction or advice on academic or personal decisions. The purpose of this process is to counsel or inform students, so they get the most of their college experience. Advising includes establishing educational goals or milestones based on the student’s interests and intentions.

Academic freedom is the principle that scholars, educators, and researchers have the right to teach, research, discuss, and publish ideas without undue interference, censorship, or restriction from institutions, governments, or other external stakeholders. It supports intellectual inquiry, innovation, and the pursuit of knowledge, even on controversial or politically sensitive topics, while operating within frameworks of ethical responsibility and accountability.

Academic freedom encompasses several interrelated elements. Faculty and educators have the freedom to design curriculum, choose teaching methods, and present diverse viewpoints without institutional suppression or risk of dismissal. Scholars can pursue research and experimentation, including on challenging or contentious subjects, subject to ethical and legal oversight. Faculty and students can openly discuss and disseminate ideas in academic and public forums. At the same time, academic freedom is exercised alongside responsibilities, including research integrity, peer review, and oversight by Institutional Review Boards (IRBs) or ethics committees, which help ensure research is ethical without unnecessarily restricting inquiry.

While academic freedom is recognized internationally through university charters, national laws, and agreements such as the UNESCO Recommendation concerning the Status of Higher-Education Teaching Personnel (1997), its scope, legal protections, and enforcement vary widely across countries. Some countries provide strong statutory safeguards, while others impose restrictions on topics, speech, or research agendas. Even where protections exist, academic freedom can face pressures from government or institutional oversight, such as review of faculty syllabi or restrictions on teaching certain subjects.

Examples of academic freedom in practice include faculty conducting research on politically sensitive topics without risk of dismissal, universities hosting public debates on controversial social or scientific issues, academic journals publishing findings that challenge established theories, and IRB oversight ensuring ethical standards in research on human subjects.

International organizations such as UNESCO and the International Association of Universities advocate for the protection of academic freedom worldwide, though legal and cultural frameworks differ. Institutions everywhere navigate contemporary challenges, including political or ideological review of course content, balancing freedom with responsibilities to prevent harm or misinformation, and maintaining independence in research and teaching while complying with ethical and regulatory requirements.

As defined in the Digital Skills for Today’s Workforce Act, accelerated learning program models mean evidence-informed strategies that support adult learners and workers in obtaining in-demand skills and credentials in an accelerated fashion. They include integrated education and training, bridge programs, and work-based learning programs.

A federal policy model established under the One Big Beautiful Bill Act (OBBBA) in 2025 that links eligibility for federal student aid to the post-graduation earnings of program completers. The framework extends the gainful employment concept—historically applied only to for-profit and career training programs—to all Title IV–eligible degree and nondegree programs. Higher education programs must demonstrate that their graduates earn more than peers without equivalent educational attainment (high school diploma or bachelor’s degree, depending on program level) to maintain eligibility for federal loans.

Related Terms: Gainful Employment Rule | Title IV Eligibility | Return on Investment (ROI) in Education | Workforce Pell | Accountability in Higher Education

See Topic Brief: Accountability Framework in Higher Education | Learn & Work Ecosystem Library

According to the U.S. Department of Education, accreditation is the process of assessment meant to improve academic quality and institutional accountability by an established set of standards to ensure a basic level of quality. Accreditation covers both the initial and ongoing approval of an educational institution or program. Entire schools or institutions can be accredited (referred to as institutional accreditation), as can individual schools, programs, or departments (referred to as specialized or programmatic accreditation). Accreditation can be conducted on the national, state, or private organizational levels. The accrediting agency establishes an agreed-on set of standards, evaluates organizations or institutions, and then re-evaluates the provider on a set schedule—typically, every five or ten years.

A comprehensive repository that bridges the worlds of education and employment by capturing an individual’s formal and informal educational experiences, professional development achievements, soft skills, and industry-specific training programs. The approach provides a more complete picture of an individual’s abilities and experiences than is available with traditional academic transcripts and resumes. Employers may use the Wallet to verify and assess credentials, skills, and achievements — this occurs by jobseekers uploading and tagging in multiple formats demonstrations of their skills directly to employers (e.g., a video of a sales presentation or adding a publication to their Wallet).

Actionable intelligence refers to actions that are informed by analytics. Analytics without action is considered analysis. Actionable intelligence makes outcomes visible — to see, critique, and adapt to various types of context.  A comparison of each follows:

• Actionable Intelligence —Useful information that helps people make informed decisions and take action. Example: getting a weather report that tells you to bring an umbrella before it rains.
• Analysis —The process of carefully studying information to understand it better. Example: looking at a weather map to figure out why a storm is coming.
• Analytics —Using data, math, and technology to find patterns and make predictions. Example: tracking past weather patterns to predict when the next big storm will happen.

A goal of many organizations developing solutions to improve the learn-and-work ecosystem is that their solutions (actions) are founded in actionable intelligence; i.e., actions informed by analytics. Without a description of the analytics that stand behind actions underway, it is not possible to determine whether they are founded in actionable intelligence.

A manufacturing process that creates three-dimensional objects by adding material layer by layer, guided by digital 3D model data and building parts directly from materials such as plastics, metals, or resins. This technology is widely used in 3D printing for rapid prototyping, custom tooling, and the production of end-use parts. It is especially used in industry sectors such as aerospace, automotive, healthcare, and consumer products, where it enables complex designs, lightweight structures, and low-volume or personalized production.

By contrast, traditional subtractive methods remove material from a solid block.

Refers to a range of adult education and literacy programs including Adult Basic Education, Adult Secondary Education, English for Speakers of Other Languages, Family Literacy, Skills Development, Workforce Development, and other programs which assist undereducated and/or disadvantaged adults to function effectively. Adult education programs typically focus on numeracy, literacy, high school equivalency, digital literacy, workplace readiness training, and wraparound services. Programs are often set up to help adults with particular needs; e.g., workforce readiness to get a new job, learning how to use a computer, or learning to speak English.

There are an estimated 44 million adults with low basic skills in the U.S.; federally funded adult education assists about 1.5 million annually to earn a high school equivalency, increase basic and employability skills, or improve their English language proficiency (Adult Education and Family Literacy Act, Title II of the Workforce Innovation and Opportunity Act).

A fast-growing sector of adult education is English for speakers of other languages (ESOL), also referred to as English as a second language (ESL) or English language learners (ELL). These courses assist immigrants in acquiring English language skills and acclimating to the culture of English-speaking countries like the U.S., Canada, Australia, and New Zealand.

Adult learners are known by a variety of names: nontraditional students, adult students, returning adults, adult returners, mature learners, comebackers. Common characteristics: usually 25 or older; delayed entering college for at least one year following high school; usually employed full-time; often have a family and dependents to support; may have started college as a traditional student but needed to take time off to address other responsibilities; looking to enhance their professional lives or may be switching careers; have more experience than traditional students, having already started a career or served in the military; more mature, independent, and motivated than traditional students.

Created by the College Board, Advanced Placement (AP) is a program that offers college-level courses and exams students can take in high school. Most, but not all, states have a statewide AP credit policy for their public colleges and universities. Most four-year colleges and universities in the U.S.—and many institutions in more than 100 other countries—grant credit or advanced placement (or both) for qualifying AP Exam scores. According to the College Board, options for earning college credit or advanced placement (or both), depend on the policy of the college:

• Credit: Some colleges use AP scores to offer college credit. Students can graduate from college early and save money on tuition by earning credit for qualifying AP scores. A certain number of credits (usually 120 for a bachelor’s degree) are commonly needed, depending on the college major.
• Advanced placement: At some colleges, students can skip certain introductory courses and gain placement in more advanced courses with qualifying AP scores.

Refers to a federal law that prohibits discrimination on the basis of age in programs or activities receiving federal financial assistance.

Refers to a federal law that prohibits age-based employment discrimination against individuals 40 years of age or older in various aspects of employment.

A college or university that welcomes and supports learners of all ages, especially older adults. Recognizing that learning doesn’t stop at a certain age, these institutions offer programs, services, and environments that are inclusive and accessible to people throughout their lives. They typically offer specific programs, courses, and services tailored to the interests and needs of older adults (e.g., opportunities to take classes, engage in research, and participate in campus life) with a goal to provide both access to lifelong learning, and foster inclusion, intergenerational connection, and healthy aging.

Higher education institutions can be designated as an age-friendly university by the Age-Friendly University Global Network. The Network, originally launched in 2012 by Dublin City University (Ireland), is now led by Arizona State University in the U.S. Institutions become members by endorsing the Network’s “Ten Principles of an Age-Friendly University” which serve as a framework for promoting age inclusivity in higher education.

More than 110 institutions worldwide are part of the AFU Global Network. Several U.S. universities have received the AFU including:

• University of Arizona (2024) –promoting positive and healthy aging through various programs and initiatives.
• Wichita State University (2022) –providing learning opportunities for adults at all life stages.
• University of Maine (2022) –engaging older adults in various aspects of university life including research and lifelong learning programs.
• Virginia Commonwealth University (2021) – focusing on inclusivity and intergenerational learning
• University of Massachusetts System (2019 –encompassing multiple campuses across the state.

The AFU Network does not define “older adults” by a specific age:

• No fixed age threshold: There is recognition that educational needs and interests can arise at any age after midlife.
• Programs often target 50+: Many member universities design lifelong learning and engagement programs with participants aged 50, 60, or older in mind, reflecting common retirement ages and life transitions.
• Focus on diversity of experience, rather than chronological age alone: Principles emphasize the varied needs, backgrounds, and goals of older learners—whether returning to school, pursuing personal interests, mentoring others, or seeking social engagement.

Some AFUs offer:

• Workforce re-entry and career pivot programs including retraining and upskilling programs for older adults returning to the workforce, changing careers, or starting businesses. Programs may include digital literacy, nonprofit leadership, entrepreneurship, or industry certifications in growing fields. Some AFUs partner with community colleges or workforce boards to offer training in high-demand areas like: Healthcare (e.g., medical assistant, caregiving); Information technology (e.g., basic coding, IT support); Education (e.g., substitute teaching, early childhood care); Business and nonprofit leadership; Entrepreneurship (especially social entrepreneurship).
• Social and Intergenerational Engagement to include student mentoring, volunteerism, or intergenerational programs, which benefit both older learners and younger students.
• Accessible services such as dedicated staff or ombudspersons for older learners, accessible transportation and campus navigation help, health and wellness programs tailored to older adults, and tech support for online learning platforms.

Alternative names for programs include:

• Lifelong Learning Hub
• Encore Career Programs
• Second Acts

See Initiative: Age-Friendly University Global Network (AFU) | Learn & Work Ecosystem Library

Refers to a work environment that actively supports and values employees of all ages, ensuring equal opportunities and preventing age-related bias.

According to the World Health Organization, ageism refers to the stereotypes (how we think), prejudice (how we feel), and discrimination (how we act) towards others or oneself based on age. While ageism can affect individuals of any age, it is most commonly associated with older adults. It can manifest in subtle biases or overt discriminatory practices and has significant negative impacts on individuals and society in the learn-and-work ecosystem.

Refers to a system or program that autonomously performs tasks for a user or another system. Agents are more sophisticated than AI assistants.

AI chatbot assistants use conversational AI techniques such as natural language processing (NLP) to understand user questions and automate responses.  These non-agency AI chatbots are ones without available tools, memory, and reasoning. The non-agency chatbots require continuous user input to respond, can produce responses to common prompts that align with user expectations, but perform poorly on questions unique to the user and their data. Since these chatbots do not hold memory, they cannot learn from their mistakes if their responses are unsatisfactory.

By contrast, AI agents can learn to adapt to user expectations over time. The results include more personalized experiences and comprehensive responses. Agents can complete complex tasks by creating subtasks without human intervention and consider different plans. These plans can be self-corrected and updated as needed. AI agents assess their tools and use their available resources to fill in information gaps. Since AI agents often do not have the full knowledge base needed to take on all subtasks within a complex goal, they use their available tools to include external data sets, web searches, APIs, and other agents. After the agent obtains missing information via these means, the agent can update its knowledge base and reassess its plan of action, self-correct, solve complex tasks in various enterprise contexts such as software design, IT automation, code-generation tools, and conversational assistants. The agent uses advanced natural language processing techniques of large language models (LLMs) to comprehend and respond to user inputs step-by-step and determine when to call on external tools.

Refers to the use of artificial intelligence systems to provide personalized coaching, guidance, feedback, and skill development support to individuals in workplace or learning environments. AI coaches typically operate through conversational interfaces—such as chat, voice interaction, or digital avatars—and provide on-demand assistance with professional development, goal setting, communication skills, performance improvement, and behavioral change.

AI coaching systems analyze user inputs and may draw on organizational data, leadership models, competency frameworks, or training resources to deliver tailored recommendations and practice exercises. Common uses include role-playing workplace conversations, providing feedback on communication or presentation skills, reinforcing leadership behaviors, and nudging users toward performance goals.

Organizations are increasingly integrating AI coaching into learning and development strategies because it can scale coaching access across large workforces. Unlike traditional coaching—often reserved for senior leaders due to cost—AI coaching can provide continuous development support to frontline employees, managers, and professionals across the organization.

In large organizations, AI coaches may interact daily with thousands of employees. As a result, they can also reinforce leadership principles, workplace norms, and organizational values, effectively acting as digital ambassadors of company culture within workplace learning systems.

Most current approaches emphasize AI-augmented coaching, in which AI complements rather than replaces human coaches. In these models, AI systems support everyday skill development and practice, while human coaches focus on high-stakes leadership challenges, complex interpersonal dynamics, and transformational development.

As AI capabilities advance, AI coaching is expected to become an increasingly common component of employer learning ecosystems.

Related terms:

• AI-Assisted Coaching – A coaching model in which AI tools support or enhance human coaching. AI systems may analyze conversation data, generate reflection prompts, recommend development resources, or provide practice simulations, while a human coach leads the core coaching relationship.
• AI-Augmented Coaching – An approach that integrates AI systems with human coaching to extend the reach and effectiveness of coaching programs. In AI-augmented models, AI coaches often provide continuous, on-demand skill-building support, while human coaches focus on complex leadership challenges, strategic development, and high-touch interpersonal work.

See Topic Brief: AI Coaching for Employees | Learn & Work Ecosystem Library

Refers to AI systems that autonomously simplify and organize tasks and processes for organizations.  Within the next two years, digital workers are projected to become an integral part of how many companies operate globally. Currently, digital workers are being used as an AI-powered sales development representative to handle the entire outbound sales development process for a company; an AI-powered caller to handle both inbound calls and consented outbound calls at a company; and an integration of these and other AI agents. The vision is that digital workers will:

• Scale existing teams at companies, using AI workers to augment human sales development representatives to allow them to handle a larger volume of prospects and communications.
• Replace human sales development representatives entirely, freeing employees to move into more advanced roles such as junior account executives.
• Reduce hiring needs, such as hiring fewer people when people retire because digital workers can handle much of the load.
• Empower account executives to use the digital worker directly to generate their own pipeline, reducing the need for a separate sales development team.

Projected benefits of digital workers:

• 24/7 operation, efficiency, and scalability (enhanced productivity).
• Personalization and tone matching (AI workers are designed to align with brand and individual communication styles, making their outreach feel authentic and personalized).
• Integration among AI Workers (enables multi-channel communication strategies).
• Freeing up human workforce (allows humans to focus on high-value activities).
• Continuous Improvement (AI workers are improved continuously based on user feedback and performance data).

Challenges of digital workers include:

• Ethical considerations (whether to disclose the AI nature of these digital workers to customers
• Potential saturation (potentially oversaturating communication channels).
• Integration with existing systems (ensuring seamless integration with a company’s existing tools can be complex and require customization).
• Data privacy/security (sensitive customer and prospect data requires sound security measures and compliance with data protection regulations).
• Training/adoption (learning curve for human teams to effectively manage and leverage AI tools).
• Performance monitoring/quality control (continuous monitoring to ensure AI workers are performing as expected, not making errors that could damage customer relationships).
• Scalability of personalization (when the volume of interactions increase, maintaining personalization and avoiding generic-sounding communications).

Refers to the growing set of organizations, benchmarks, standards, and research efforts used to test and assess AI systems. As AI tools become more widely used in workplaces, education systems, employer hiring processes, and public services, there is increasing interest in ways to evaluate how these technologies perform in real-world settings. AI evaluation efforts examine issues such as model accuracy, reliability, safety, bias, transparency, and how people actually interact with AI tools in practice.

Unlike traditional sectors where “product testing” is centralized or regulated through well-established institutions, the infrastructure for evaluating AI is still emerging. Evaluation activities are currently carried out by a variety of actors, including technology companies that test their own systems, academic researchers studying real-world performance and impacts, independent benchmarking organizations that develop standardized tests for comparing models, and government agencies developing frameworks and guidance for responsible AI deployment.

Several types of organizations participate in this emerging evaluation ecosystem. For example:

• Benchmarking organizations: Develop standardized tests for comparing machine learning models and tracking technological progress.
• Government standards bodies: Publish frameworks to help organizations evaluate and manage risks associated with AI systems.
• Academic research centers: Study how AI performs in real-world environments, including how users interact with these systems and how outcomes vary across contexts.
• Independent research and policy organizations: Examine broader social, economic, and governance implications of AI deployment.

As AI becomes more integrated into education advising systems, hiring platforms, workplace productivity tools, and learning technologies, the development of credible and transparent evaluation systems is increasingly viewed as essential. These efforts help organizations understand how AI functions in practice, identify potential risks, and support more responsible and effective deployment of AI in education, workforce development, and other parts of the learn-and-work ecosystem.

See Topic Brief: AI Evaluation Ecosystem | Learn & Work Ecosystem Library

AI Hiring Discrimination Lawsuits are legal claims brought by job applicants, employees, advocacy organizations, or government enforcement agencies that allege a hiring-related decision (e.g., screening, ranking, testing, interviewing, or selection) produced or was driven by an artificial-intelligence or algorithmic system that unlawfully discriminated against people on the basis of protected characteristics (race, sex, age, disability, national origin, etc.).

These claims typically assert violations of existing civil-rights and employment statutes (e.g., U.S. civil rights laws, including Title VII of the Civil Rights Act of 1964, the Age Discrimination in Employment Act (ADEA), and the Americans with Disabilities Act (ADA)by showing either intentional disparate treatment or a disparate-impact (neutral practices that disproportionately harm protected groups) caused or amplified by an AI tool.

See Topic Brief: AI Hiring Discrimination Lawsuits | Learn & Work Ecosystem Library

See Glossary Term: AI Hiring Discrimination, Lawsuits & Accountability | Learn & Work Ecosystem Library

Refers to the emerging legal, ethical, and regulatory actions addressing the use of artificial intelligence (AI) in employment screening and selection processes that result in bias or disparate impact against protected groups. These cases test how longstanding civil rights laws—such as Title VII of the Civil Rights Act and the Americans with Disabilities Act (ADA)—apply to automated hiring tools.

As employers increasingly rely on AI to process massive volumes of applications, job seekers have begun challenging algorithmic systems they believe discriminate based on race, gender, age, or disability. Notable cases include 2024–2025 filings against vendors such as Aon, HireVue, and Intuit, alleging biased or inaccessible AI tools. Federal agencies including the Equal Employment Opportunity Commission (EEOC) and Federal Trade Commission (FTC) have asserted that both employers and technology vendors can be held liable for discriminatory AI outcomes.

These developments mark a new phase in employment law where accountability extends beyond human decision-makers to the digital systems they deploy. The movement underscores the importance of explainable AI, regular bias audits, and transparent vendor oversight to prevent automated discrimination at scale.

See Glossary Term: Explainable AI | Learn & Work Ecosystem Library

See Topic Brief: AI Hiring Discrimination Lawsuits | Learn & Work Ecosystem Library

AI literacy refers to the foundational knowledge and skills required to understand, use, and critically evaluate artificial intelligence (AI) systems. It typically includes basic awareness of how AI works, where it is used, its benefits and limitations, and its ethical and social implications. AI literacy emphasizes competent and informed use of AI tools. In most frameworks, AI literacy focuses on:

• Understanding what AI is and is not
• Knowing how AI systems are trained and deployed
• Recognizing bias, limitations, and ethical concerns
• Using AI responsibly and appropriately
• AI literacy largely assumes that users are interacting with AI as intended.

Adversarial literacy is an emerging concept that builds on AI literacy—but goes further. It emphasizes the ability to actively interrogate, challenge, and stress-test AI systems. It involves understanding how AI systems can be manipulated, misled, or produce harmful outcomes—and developing the skills to recognize, expose, and respond to those vulnerabilities. Rather than asking “How do I use AI well?”, adversarial literacy asks:

“How can AI fail, be exploited, or mislead—and how do I detect that?”

Adversarial literacy includes competencies such as:

• Recognizing adversarial inputs, prompts, or examples designed to manipulate AI behavior
• Probing AI systems to reveal bias, hallucinations, or unsafe outputs
• Evaluating AI responses for reliability, intent, and context sensitivity
• Engaging in ethical “red-teaming” or adversarial questioning to surface system weaknesses
• Understanding that AI systems are contestable, not authoritative

The term adversarial literacy is increasingly used by scholars and others to describe a paradigm shift, i.e. the emergence of several AI-driven shifts:

• AI systems are no longer passive tools: Generative and predictive systems actively shape information environments—and can be manipulated intentionally or unintentionally.
• Trust is no longer sufficient: AI outputs may sound authoritative while being incorrect, biased, or strategically misleading, requiring users to adopt a skeptical stance.
• Power and agency are at stake: Adversarial literacy equips individuals (earners, workers, citizens) to resist the over-reliance on opaque systems and reclaim human judgment.
• Education must move from use to critique: Teaching people how to prompt (chat with) AI is insufficient; they must also learn how to break, test, and question it safely and ethically.

See Glossary Term: Opacity (in Artificial Intelligence) | Opaque AI | Learn & Work Ecosystem Library

See Glossary Term: Information Literacy | Learn & Work Ecosystem Library

See Topic Brief: Converging Terms:  Digital Literacy & Information Literacy | Learn & Work Ecosystem Library

Refers to emerging management tools that visually map the roles, titles, and organizational positions held by AI systems, bots, digital agents, and digital workers within a company. In some organizations, these charts integrate both human employees and AI-powered systems to illustrate how work is allocated, who (or what) performs specific functions, and how tasks are distributed across humans and AI. Companies using AI Org Charts may assign job titles to AI agents (e.g., “AI Research Analyst,” “Digital Customer Service Agent,” “AI Compliance Officer”) as these systems take on increasingly defined roles within workflows. Some organizations refer to these AI agents as “digital workers” or “digital employees,” reflecting their status as persistent, assigned entities within operational structures.

AI Org Charts may assist companies to:

• Clarify where AI systems are embedded in business processes.
• Assign oversight responsibility for AI functions.
• Provide transparency for regulators, auditors, employees, and workforce planning.
• Support transparency about the division of labor between humans and machines.

There is a shift in the use of terms to depict the “bot” workforce. Many vendors and employers are using the term “digital worker” or “digital teammate” rather than “bot” or “AI agent” to normalize the presence of AI as part of the workforce.

According to iCIMS (provider of talent acquisition software), AI recruiting is the use of artificial intelligence to streamline and re-automate the recruitment process. AI and machine learning can be used to make manual processes automatic, suggest top candidates for the roles that suit them best, and reduce bias and “gut feeling” when selecting candidates.

An artificial intelligence system (bot or AI avatar) deployed to attend a meeting or event in place of a human, typically to observe, record, summarize, or interact on their behalf. The “emissary” may be pre-programmed with instructions or agenda points, or be a real-time agent capable of basic interactions or just recording. The AI may appear as a name or video avatar in a Zoom/Teams screen and may or may not clearly indicate it is not human. The AI listens or records the meeting, may generate a summary or transcript afterward, and may or may not participate (e.g., say “noted,” “I’ll follow up.”  The human host of the meeting can typically recognize this is an AI representative but may or may not disclose this substitution to all other participants.

This is an emerging and controversial practice in AI-mediated communication that currently lacks a standardized term—but several terms are circulating in technology, business, and educational communities to describe the practice:

• AI Representative | AI Emissary
  • Not standard terms
  • AI Representative implies delegation (like a spokesperson)
  • AI Emissary has a formal, more diplomatic tone
  • Both suggest the AI is attending on behalf of a human, not pretending to be one
• Digital Double
  • Popular in speculative tech and AI circles
  • Suggests a full virtual representation of a person
  • Can imply real-time interaction or decision-making capability
  • Closer to Digital Twin or Avatar with Agency
• Ghost Attendee or Proxy Bot
  • Colloquial terms, sometimes used pejoratively
  • Emphasizes the ethical ambiguity—present but not present
  • Proxy bot may refer to a simple note-taking tool
• Shadow Presence
  • Describes situations where a bot is present in a digital meeting space (or even listens via device microphone), but its presence is not openly acknowledged
  • Often unintentional but raises ethical flags
• Synthetic Identity
  • An AI-generated name, avatar, or persona used to represent a bot in professional settings
  • Can be used transparently (e.g., “Fireflies.ai bot”) or misleadingly (e.g., a human-sounding name with no disclosure it’s AI)
• Meeting Clone or AI Meeting Attendee
  • Used in some productivity/startup platforms
  • Describes tools like Otter.ai, Rewind, Fathom, and Zoom AI Companion that show up with your name and join calls

Citation Note: This glossary entry was developed by the Learn & Work Ecosystem Library with support from ChatGPT (OpenAI, 2025) and is based on an original synthesis of emerging terminology from journalism, technical documentation, and academic discourse. The terms reflect evolving usage in the fields of AI, workplace automation, and digital communication, and are not yet standardized across sectors.

A tool that uses artificial intelligence to create resumes for individuals, from entry-level to executive level employment searches. The tool can write text, check the entire document for errors, and format the resume. The tool includes templates, writing tips, and automated features. Algorithms within the AI resume generator enables analysis of large amounts of data in order to provide individuals with tailormade content and design suggestions based on the user’s requests.

Since Applicant Tracking Systems (ATS) are increasingly used for first-stage resume review by many employers, there is growing pressure by job applicants to submit machine-readable professional resumes.

Examples of 17 of the best free resume builders recommended by C. Forsey at HubSpot’s  Marketing, Sales & Services blogs (April 2024):

• Zety: Best for Expert Resume Creation Tips
• Resume Genius: Best for Easy and Fast Resume Creation
• Wepik: Best for Customizing Pre-Made Resumes
• My Perfect Resume: Best for Guided Resume Creation Help
• Standard Resume: Best for Active LinkedIn Users
• Kickresume: Best for Quick and AI-Assisted Resume Creation
• Canva: Best for Design Creativity and Expression
• Pixpa: Best for Creating Online Resume Websites
• Indeed: Best for In-Platform Job Seekers
• com: Best for Minimalist Resume Creation
• Novoresume: Best for ATS-Friendly Resume Building
• VisualCV: Best for Multimedia Resumes
• CakeResume: Best for Resumes With an Online Portfolio
• Resume Now: Best for Time-Saving Resume Creation
• ResumeNerd: Best for Resume Writing Help
• Jofibo: Best for Comprehensive Guides
• Hloom: Best for Resume Templates

The AI sandwich is a conceptual framework and newer term that describes how humans and artificial intelligence systems work together in a structured workflow. In this model, humans initiate and frame a task (the top layer), AI systems perform analysis or generate outputs (the middle layer), and humans then review, interpret, and apply the results (the bottom layer).

The term emphasizes that effective AI use depends on human judgment at both the beginning and end of a process, rather than full automation. It is commonly used to illustrate human-in-the-loop approaches and the growing role of AI as a collaborative tool in decision-making, learning, and work.

AI Study Companions and Intelligent Tutoring Systems (ITS) are both tools that use technology to help people learn, but they differ in how they are built and how they work with learners.

AI Study Companions are newer tools that use conversational artificial intelligence—like ChatGPT—to help students study. They answer questions, explain topics, and adjust to a learner’s needs based on what the learner says or uploads. These tools can feel like chatting with a helpful tutor and are easy to access on phones or laptops. Examples include ChatGPT Study Mode, Khanmigo, Duolingo Max.

Intelligent Tutoring Systems (ITS) are older, research-based systems built to teach specific subjects, often like a traditional tutor would. They follow a structured path, give feedback based on right or wrong answers, and track a learner’s progress over time. Examples include Carnegie Learning (for math), AutoTutor, ASSISTments.

A comparison of the tools:

Both tools aim to improve how people learn—especially by offering personalized help outside the classroom. AI study companions are becoming more common in everyday learning because they are easy to use and widely available. ITS tools remain important in formal education and research, especially where proven learning outcomes are needed.

Related Terms: Personalized Learning, Learning Technologies, Educational AI, Intelligent Learning Tools

Refers to the strategic practice in which large human capital management (HCM) or human resources (HR) technology platforms acquire AI-driven recruiting and conversational platforms—such as chatbots, scheduling assistants, or candidate engagement tools—to create integrated, end-to-end talent acquisition suites. This trend streamlines high-volume frontline hiring of workers and professional recruitment, while also advancing the development of AI agents within enterprise HR ecosystems.

The context for this increasing practice includes:

• Organizations in sectors such as healthcare, retail, transportation, hospitality, airlines, and entertainment often face persistent high-volume hiring needs.
• AI-powered conversational platforms (e.g., Paradox, HireVue, Phenom) automate candidate screening, interview scheduling, and communications, easing recruiter workloads.
• When enterprise platforms like Workday, Oracle, or SAP acquire these tools, they can strengthen their market position, expand offerings to mid-market and enterprise employers, and accelerate their capabilities in skills-based hiring and AI agent development.
• Industry analysts view this consolidation as part of a broader shift toward “platform convergence” in HR technology, where fragmented point solutions are absorbed into comprehensive HCM systems.

Examples of this practice:

• Workday’s acquisition of Paradox (announced 2025, expected 2026), integrating conversational AI into its recruiting suite to support end-to-end, AI-powered hiring.
• iCIMS’ acquisition of Altru (2020), a video storytelling platform, expanding its candidate engagement capabilities.
• Phenom’s acquisition of Talentcube (2020), adding video recruiting to its AI talent experience platform.

See related terms:

• Skills-Based Hiring
• HR-IT Integration
• Talent Acquisition Platform
• Human Capital Management (HCM)

Refers to a body-worn device (e.g., smart glasses, smart rings and watches, badges, or audio devices) that integrates artificial intelligence (AI) to collect data, interpret context, and provide real-time insights or assistance to the user. AI wearables may support functions such as learning, productivity tracking, health monitoring, navigation, language translation, and workplace performance support.

Within the learn-and-work ecosystem, AI wearables raise important considerations related to privacy, data governance, accessibility, human-AI interaction, and the evolving boundaries between personal technology and institutional systems.

Nontraditional and digital credentials are offered through a higher education institution’s partnerships with approved third-party vendors. These alternative credentials may be viewed as pathways to obtain attainable and accessible education. Such courses or modules may be used as supplemental materials to instruction provided within the higher education institution’s graded, organized courses or offered as a stand-alone program. Digital badge awards do not typically come with letter grades upon completion, nor add or subtract to an enrolled student’s grade point average (GPA), nor produce a GPA for non-enrolled students.  An institution’s Transfer Credit typically addresses whether academic credit may be earned within these platforms.

Alternative credentials are competencies, skills, and learning outcomes derived from assessment-based, non-degree activities that align to specific, timely needs in the workforce. Different types of alternative credentials include but are not limited to: (1) Digital Badge—verified indicator of accomplishment, skill, knowledge, experience, etc. that can be earned in a variety of learning environments. Digital badges are awarded based on competency, not necessarily the completion of a program. The badge itself is an icon that can be displayed on a website, profile, email signature or anywhere else on the Internet. (2) Verified Certificate—awarded to indicate completion of an online course, especially a MOOC. Students must complete all program requirements and then verify their identity before receiving the credential. Course sequences are a form of verified certificates that indicate a pathway of courses for learning a specific topic.(3) Microcredential—highly specific, competency-based degree or certification. Microcredentials are often created and chosen to align a student’s needs with instructional goals. The credential is earned upon the completion of certain activities, tasks, projects, and/or assessments.

The American Council on Education (ACE) defines alternative providers as an organization that is not a public or private institution of higher education that delivers postsecondary content and/or provides skills training and support services that connects learner to the labor market, either independently or in partnership with colleges and universities.

Refers to the method and practice of teaching adult learners, emphasizing approaches that recognize adults’ prior experience, self-direction, and readiness to learn. The term derives from the Greek words andra (“man” or “adult”) and ago (‘to lead”). It was first coined in 1833 by German educator Alexander Knapp to describe Plato’s theory of education. However, andragogy is most closely associated with Malcolm Knowles in the 1970s, who significantly shaped the modern field of adult education.  Andragogy emphasizes that adults learn differently from children. Adults are typically self-directed, goal-oriented, and bring valuable prior experiences to their learning. In this model, educators act as facilitators rather than lecturers, helping adult learners connect new knowledge to their own experiences and apply it to real-world goals. Key features include:

• Learner-centered approach: Instruction is built around learners’ needs, interests, and goals, with flexibility in both content and delivery.
• Flexible delivery: Learning can occur synchronously or asynchronously, allowing adults to balance education with work and personal commitments.
• Reflective assessment: Evaluation emphasizes reflection, self-assessment, and the practical application of learning.
• Feedback-driven improvement: Learners’ feedback is actively gathered and used to refine instructional methods and materials.

See Topic Brief: Changes in Teaching and Learning in the 21st Century—Pedagogy, Andragogy, and Heutagogy | Learn & Work Ecosystem Library

Applicant Tracking System (ATS) is an all-in-one human resource (HR) software that automates the hiring process. It helps HR teams manage every part of recruitment (from job posting to onboarding): (1) stores job candidate information, including resumés, cover letters, references, and other recruitment and hiring data that HR teams can easily access and organize; (2) tracks job candidates and their application status throughout the hiring pipeline; (3) weeds out unqualified candidates and recommends the best fit for a position based on the parameters set by HR and only those on the shortlist are moved to the next stage of the hiring process; (4) automates time-consuming administrative tasks such as manually screening applicants, reading resumés, scheduling interviews, and sending notifications and emails to job candidates and employees.

An Application Programming Interface (API) is the set of instructions (rules) and protocols that enable software programs to talk with one another and share data. API codes are written in computer programming languages that allow software components to communicate with one another.

A liberal arts degree is a bachelor’s degree earned in certain liberal arts majors in the humanities, social sciences, natural sciences, and fine arts. A liberal arts degree does not typically focus on a career-specific curriculum as many other college majors do, such as computer science, marketing, engineering, and nursing. Growing employer interest in skills-based hiring is spurring higher education institutions to build into the liberal arts curricula digital and other skills that can help graduates compete for a first job. Applied liberal arts refers to approaches to integrating skills content into liberal arts degrees. Approaches include: 1) offering an Interdisciplinary Bachelor of Arts degree, often known as Applied Liberal Arts (ALA); 2) offering a Bachelor of Science in Applied Liberal Studies as a way of combining the liberal arts foundation with more specialized areas of interest; and 3) integrating skills content (e.g., digital badges, microcredentials) into majors in the liberal arts. The aim of these approaches is to ensure solid grounding in the liberal arts coupled with courses pertinent to the workplace such as project management, data visualization and analysis, design thinking, conflict resolution, public speaking, leadership, dialogue and intercultural exploration, health and wellness, social innovation, personal finance, and entrepreneurship. Skills such as these can be embedded into humanities, math, science, fine arts, and social sciences disciplines – and when this occurs, these are often referred to as applied liberal arts.

Alternative terms: Applied Liberal Studies, Liberal & Applied Studies

As the youth apprenticeship field in the U.S. expands in scale and scope, so too has the expansion of organizations to support the development and successful implementation of apprenticeship initiatives. The term, “apprenticeship intermediary,” has been coined to refer to the function and focus of intermediary organizations that serve the needs of students, educational institutions, communities, and workforce partners in the apprenticeship field.

Financial incentives—typically offered by state governments or workforce development agencies—that reimburse employers for a portion of the costs associated with training or employing apprentices. These rebates may cover expenses such as mentor time, training materials, related instruction, or wages during the training period. The goal is to reduce the financial barrier for employers to participate in apprenticeship programs and to promote investment in workforce development.

A person, association, committee, or organization that operates a Registered Apprenticeship (RA) program. The sponsor takes on the legal responsibility of ensuring the program operates in compliance with federal and state regulations. Sponsor roles typically cover recruitment, screening, and hiring apprentices—or working with employers to do so. They develop formal agreements with apprentices identifying the length of the program, skills to be learned, the wages to be paid at different points in time, the development of the equal employment opportunity plan, and the required classroom instruction; and work with state apprenticeship agencies (SAAs) or the U.S. Department of Labor (DOL) to make sure that their registered programs meet state and federal requirements.

Apprenticeship programs can be sponsored by:

• Employers or a consortium of businesses
• Workforce intermediary (e.g., industry association or a labor-management organization)
• Labor groups
• Employer associations
• Unions
• Agencies
• Colleges
• Committees
• Third-party entities that take on administrative duties related to sponsorship.

To register an apprenticeship, a sponsor submits an application to the applicable registration agency (DOL or appropriate SAA). The application includes a work process schedule that describes the competencies the apprentice will learn and how on the-job training and related instruction will teach those competencies. The application also includes a schedule of wage increases for the apprentice, description of safety measures, and other assurances related to program administration and recordkeeping.

See Topic Brief: Apprenticeship / Apprenticeships | Learn & Work Ecosystem Library

Apprenticeship is an industry-driven, high-quality career pathway that combines classroom instruction with on-the-job experience. Through apprenticeships, employers can prepare their future workforce, and individuals can obtain paid work experience while earning a nationally recognized, portable credential. Employers can choose to register their programs with the U.S. Department of Labor to show prospective job seekers that their apprenticeship program meets national quality standards.

In addition to the United States, apprenticeship systems are widely used around the world—including in countries such as Germany, Switzerland, Australia, and the United Kingdom—where they are typically integrated into national education and training systems to serve as central components of workforce development.

See Topic Brief: Apprenticeship / Apprenticeships | Learn & Work Ecosystem Library

See Topic Brief: Employer‑Operated Apprenticeship Programs | Learn & Work Ecosystem Library

Articulated credit is a process that aligns high school courses with college courses or programs (usually occupational pathways) to allow students to start a college technical major while still in high school and continue in a participating postsecondary institution. Articulated credit courses are approved by the higher education institution as equivalent to introductory-level college courses.  Articulated credit is different from Advanced Placement (AP) credit, which requires passing an end-of-course exam.

Articulated college credit is a process through which high school students can earn college credit by successfully completing certain high school courses. These courses cover learning outcomes, skills, and abilities comparable to those taught in college courses. Establishing articulated college credit programs is a collaborative effort between high schools and colleges. The process typically includes:

1. Course Alignment: High schools collaborate with higher education institutions to align certain high school courses with college-level content. Courses cover similar learning outcomes, skills, and knowledge.
2. Assessment: Students complete the high school course and demonstrate mastery of the material usually through exams, projects, or other assessments.
3. Credit Award: If the student meets the criteria set by the college, they receive college credit. The amount of credit awarded varies by institution and course/program.
4. Transcript/Verification: The college credits earned through articulated credit transfer appear on the student’s college transcript.
5. Benefits: Articulated credit allows students to explore college-level material, potentially save time and money, and gain a head start on their college education and career paths.

Transferring occurs from one educational institution to another. According to the National Center for Education Statistics, over a million students have transferred among colleges since 2015. Students can transfer from a community college or two-year program to a four-year college or university to graduate with both an associate and bachelor’s degree (this is called reverse transfer). Students can transfer in between all types of institutions – private, public, large, small, community, and research. Students can also transfer college credits from a high school dual-credit program to a two- or four-year program, and can use those credits toward their degree. Transfer includes the transition of credits from one institution to another, while still maintaining the value of those credits. Course articulation is an important part of that. Course articulation is the process of comparing the content of courses that are transferred between postsecondary institutions – one institution matches its courses or requirements to coursework completed at another institution. Transfer systems can be set up within states or systems. To make this process easier, some schools offer guaranteed transfer credit acceptance if students transfer from pre-approved schools.

Refers to the laws, executive directives, guidelines, and strategic frameworks enacted by individual state governments in the United States to govern the development, procurement, deployment, and oversight of AI technologies. Not all states have implemented regulations /policy, but those that do typically address transparency, data privacy, algorithmic accountability, cybersecurity, civil rights protections, ethical use, and the responsible application of AI in public services such as education, workforce development, healthcare, and transportation. State policies may include requirements for risk assessments, impact disclosures, governance structures, procurement standards, and restrictions on high-risk or harmful uses of AI.

State AI regulations operate within a broader national framework shaped by federal AI policies—such as those issued by the White House, the United States Congress, and federal agencies including the National Institute of Standards and Technology (NIST). Federal policy establishes nationwide principles related to AI safety, national security, civil rights, and sector-specific oversight, while states develop complementary or more targeted rules that address local needs and contexts.

See: Learn & Work Ecosystem Library: Glossary / Artificial Intelligence (AI) Regulations / Policy – U.S. Government | Learn & Work Ecosystem Library

See: Learn & Work Ecosystem Library: Topic Brief: Regulation/Policy in Artificial Intelligence (AI) | Learn & Work Ecosystem Library

Refers to the laws, executive actions, regulatory guidance, and national strategies established by the United States government to ensure the safe, ethical, and responsible development and use of AI across the country. Federal AI policy sets nationwide principles and requirements related to civil rights protections, data privacy, national security, AI safety and risk management, transparency, consumer protection, and oversight in key sectors such as healthcare, finance, education, and transportation. Components of federal policy may include agency-specific standards, procurement rules for trustworthy AI, reporting and impact assessment requirements, and governance structures such as federal AI councils or interagency oversight bodies.

Federal policy establishes overarching guardrails and national coordination within which states may adopt more specific or context-based AI regulations. The U.S. approach is developing alongside AI governance efforts in other nations and regions—including the European Union, the United Kingdom, Canada, and member countries of the Organisation for Economic Co-operation and Development (OECD)—which are creating regulatory frameworks that emphasize safety, accountability, human rights protections, and international alignment. Together, these national and international approaches reflect a growing global effort to promote responsible and harmonized AI governance.

See Learn & Work Ecosystem Library: Glossary /  Artificial Intelligence (AI) Regulations / Policy – States | Learn & Work Ecosystem Library

See Learn & Work Ecosystem Library: Topic Brief / Regulation/Policy in Artificial Intelligence (AI) | Learn & Work Ecosystem Library

Artificial intelligence (AI) refers to the ability of a computer or computer-controlled robot to imitate human brain functions.  Machines use the application of computer science through algorithms to process large data sets to perform tasks typically associated with intelligent beings, such as the ability to reason, discover meaning, generalize, synthesize, and learn from past experiences.  Through rapid advances in computer processing, speed, and memory capacity, AI is used for more and more sophisticated applications such as medical diagnosis; computer search engines; voice, face, and handwriting recognition; and chatbots.

Generative AI refers to AI able to generate text, images, or other media in response to prompts. Generative AI models process large data sets of natural language, code language, and images to create new content in these forms (natural language, code language, images) and other data forms. Examples include ChatGPT, Bing Chat, and Bard. Many applications are using generative AI in the fields of art, marketing, writing, software development, product design, healthcare, finance, gaming, fashion, and education (in teaching, learning, student support services, and administrative supports).

AI prompts are any form of text, question, information, or coding that communicate to AI what response(s) are being sought.

Other terms for AI include machine learning (ML) and deep learning.

Refers to an institution of higher learning with an undergraduate population that is at least 10% Asian American and Native American Pacific Islander. This designation is defined under the Higher Education Act (HEA) and is granted by the U.S. Department of Education to support eligible colleges and universities in improving and expanding their capacity to serve Asian American and Native American Pacific Islander learners. There are approximately 200 AANAPISIs in 27 states and territories, mostly clustered on the west coast, in Hawaii and the Pacific territories, as well as New York.

In badging, refers to representations that share information about a badge belonging to one earner. Assertions are packaged for transmission as JSON objects with a set of mandatory and optional properties. The assertion typically includes information about who earned the badge, what the badge represents, and who issued the badge. The assertion for a badge includes data items required by the Open Badges Specification: unique ID, recipient, badge URL, verification data, issue date. Optional data items include a badge image with assertion data baked into it, evidence URL, expire date, and stored location in a hosted file or JSON Web signature.

The process of evaluating or measuring an individual’s skills, knowledge, or performance against predetermined criteria or standards. Assessment gathers information about what someone knows, understands, or can do to make informed decisions about their progress, abilities, or areas for improvement. Common methods:

• Written exams/tests that assess knowledge and understanding via questions or prompts presented in written form.
• Oral exams/presentations that assess verbal communication skills, comprehension, and ability to articulate ideas effectively, commonly taking the form of individual presentations, group discussions, or oral exams.
• Projects/assignments that require individuals to apply their knowledge and skills to real-world tasks or scenarios.
• Practical assessments that assess hands-on skills and abilities such as laboratory experiments, artistic performances, or technical demonstrations. They are common in science, art, engineering, and career and technical education.
• Portfolios compile samples of an individual’s work to demonstrate achievement and competency in specific areas. They often include written essays, artwork, and design projects.
• Observations assess an individual’s performance or behavior in real-life situations, such as classroom activities, clinical settings, or workplace tasks.
• Standardized tests assess knowledge, critical thinking, or other formal assessments with uniform administration and scoring procedures.

Assessments often vary within K-12 schools, colleges and universities, and the workplace.

• Elementary and secondary schools focus on foundational knowledge and skills across various subjects. They may include standardized tests mandated by state or national education authorities, as well as teacher-created assessments tailored to specific curriculum standards.
• Colleges and universities focus on more specialized, deeper understanding, critical thinking, and application of knowledge within specific disciplines or majors. Assessment methods include exams, research papers, presentations, and practical demonstrations.
• Employers focus on practical skills, problem-solving abilities, and job-specific competencies relevant to the position. Employers use a variety of methods such as job simulations, interviews, skills assessments, and performance evaluations to assess candidates during the hiring process and evaluate employees’ ongoing performance and development including assessment to guide upskilling/reskilling needs.

Refers to an assessment conducted when a badge issuer captures an earner’s applications for a badge through the issuer’s website. The earner typically submits evidence in support of the application. The issuer then assesses by comparing the evidence to the badge criteria defined when the badge was created. If an application for a badge is successful, the issuer awards it to the earner, creating an assertion and typically contacting the earner.

According to the UN Refugee Agency, when people flee their own country and seek sanctuary in another country, they apply for asylum. This is the right to be recognized as a refugee and receive legal protection and material assistance. An asylum seeker must demonstrate that their fear of persecution in their home country is well-founded.

Related terms: Refugee, internally displaced person, stateless person

A way of measuring learning by asking learners or workers to demonstrate what they know by doing real-world tasks. It focuses on applying skills and knowledge in meaningful, practical ways — not just answering multiple-choice questions or filling in blanks on a test exam.

Examples of authentic assessment include:

• A student in a science class designs and conducts an experiment, then explains the results.
• A culinary student prepares a meal and presents it to a panel for feedback.
• A student builds a website for a local nonprofit as part of a tech class.
• A job applicant participates in a simulation to show how they would handle a customer service issue.
• A nursing student performs a mock patient check-up to show clinical reasoning and care skills.

Authentic and “traditional” assessment can differ in several ways:

• Authentic assessment
  • Focuses on real-world tasks and applications
  • Open-ended, often project-based
  • Emphasizes problem-solving and creativity
  • Often includes self-assessment or peer review
  • Can take more time to complete and grade
• Traditional Assessment
  • Focuses on tests, quizzes, short answers
  • Usually has one right answer
  • Emphasizes memorization and recall
  • Usually scored by a teacher or machine
  • Quick to administer and score

 Related terms:

• Performance-based assessment
• Real-world assessment
• Applied learning evaluation
• Work-integrated assessment
• Portfolio assessment (when students collect and reflect on their work)
• Project-based assessment

While the term is most common in education—especially K–12 and higher education—the ideas behind authentic assessment are increasingly used in workplace training, hiring, and professional development. Employers may not always use the term “authentic assessment,” but they use similar methods. Examples include:

• Work samples during the hiring process
• Skills demonstrations for certification
• Simulation-based interviews
• Portfolios or case challenges as part of job applications

Workplace settings often use terms like these, to reflect the concept of testing what people can do, not just what they know:

• Skills assessment
• Job simulations
• Practical tests
• Performance tasks

As defined by the Velocity Network Foundation, involves the process of verifying the identity of users, third-party applications, and Credential Agents within a network. It ensures that only authorized entities can access and interact with the network’s services and data.

With the advent of degree audit software, the software can be used to audit coursework students have completed and automatically award degrees and certificates if they have completed the required coursework. This is practiced as a method for increasing degree and certificate completion,

Refer to the processes used by employers to verify the identity, credentials, criminal history, and other relevant background information of job candidates, employees, contractors, and vendors. These practices are essential for protecting workplace safety, ensuring regulatory compliance, and reducing the risk of legal, financial, and reputational harm.

Common areas of risk management include:

• Criminal background checks
• Education and credential verification
• Employment history verification
• Drug testing and fingerprinting
• Professional license and certification verification
• Global sanctions and compliance checks
• Identity and driver record verification
• Executive and contractor screening

These services are particularly critical in highly regulated industries such as healthcare, finance, transportation, education, and government. Accurate screening helps prevent credential fraud, supports trust in the workforce, and ensures alignment with federal, state, and industry-specific regulations.

 Related terms:

• Credential Verification
• Identity Verification
• Compliance and Regulatory Oversight
• Workforce Risk Management
• Employer Due Diligence

Examples of providers include: Checkr, Cisive, First Advantage, HireRight, Sterling Infosystems, and TazWorks, among others.

See Topic:  Comprehensive Background Screening and Risk Management, Especially in Highly Regulated Industry Sectors | Learn & Work Ecosystem Library

The process of embedding assertion data into a badge image.

Term to denote a low-stakes, exploratory environment or event in which participants are invited to experiment with digital credentials / badges (e.g., designing, issuing, using, exploring platforms, linking to employment). The “sandbox” / “sandpit” metaphor implies a safe space for trial, learning, and iteration, without pressure or a full commitment. Because it’s experimental, exploratory, and collaborative, the Sandpit can help reduce barriers for organizations or educators to “dip a toe” into digital credentialing rather than launching a full program immediately.

The Badge Sandpit can be used to determine:

• What works (or doesn’t) in badge design (criteria, evidence, metadata).
• How badges can integrate with learning-pathway systems.
• How to attach badges (or “skills credentials”) to employment / recruiting workflows.
• The social / community side (peer learning, shared experimentation).

Alternate terms: innovation labs, badge design workshops, design hackathons.

Badges are tools to represent someone’s achievements, certifications, or abilities. There are several types of badges such as digital badges, skills badges, open badges, and competency badges. A badge is usually digital and has underlying metadata that represents a shareable learner achievement and/or credential earned. Open Badges are digital badges that contain embedded metadata about skills and achievements. They are shareable across the web. Competency badges represent single or sets of competencies with defined market value in professional or academic settings. Competency badges are usually offered through microcredential or degree programs. A skill badge is earned through completion of a series of tasks or labs, and then a final assessment or challenge to test a learner’s skills. A certification badge validates an individual’s knowledge and understanding.

A phenomenon in which the influence of a project—often a grant-funded initiative—continues long after the project formally ends. Begetting occurs when ideas, tools, networks, language, or conceptual frameworks seeded by an initiative reappear in later work, new collaborations, policies, or next-generation tools, often in ways not planned or foreseen. Rather than program continuation or formal scaling, begetting reflects the ongoing life of an initiative through its “offspring” across the learn-and-work ecosystem.

Begetting is related to, but distinct from, concepts such as sustainability, scaling, diffusion, knowledge spillover, and knowledge transfer. Sustainability typically implies the intentional continuation of a specific program or activity, while scaling focuses on growth or replication at larger levels. Diffusion and spillover describe how ideas spread, often passively, without capturing their legacy trail. Begetting emphasizes emerging influence—how an initiative’s underlying ideas, relationships, or ways of thinking resurface and recombine in new contexts, even when the original project has formally concluded.

This phenomenon occurs across many fields, particularly those in which knowledge transfer, shared standards, or enabling conditions are intentionally established. In scientific research, for example, methods, datasets, or conceptual frameworks are typically designed specifically for replicability and reuse, allowing projects to generate future lines of inquiry. Similar patterns appear in technology development, public policy, healthcare, and the arts. In education and workforce development, begetting is especially visible because projects often shape shared language, networks, and practices that travel with people and institutions, allowing time-limited initiatives to exert longer-term influence.

In an increasingly AI-enabled environment, begetting is likely to occur more frequently and more rapidly. As AI systems capture, summarize, and recombine ideas, tools, networks, language, and conceptual frameworks documented in discoverable literature, the influence of completed projects may persist and resurface in new contexts with greater ease. This dynamic may amplify begetting by extending the reach and longevity of initiatives beyond their original audiences, timelines, and funding cycles.

See: The Power Of Begetting— How Grant Projects Bear Seeds For The Future, Including An AI-Augmented Future | Learn & Work Ecosystem Library

Belonging is a student’s perception that they are accepted, supported, and valued as a full member of the academic community, and that they have legitimate pathways to participate and succeed. Students receive signals—formal and informal—that their experiences, identities, and contributions matter.

A strong sense of belonging is associated with higher levels of academic engagement, persistence, well-being, and degree completion, particularly for students from historically underrepresented or nontraditional backgrounds. Across multiple decades of research, findings show that belonging can:

• Improve retention and persistence, especially in the first year of college.
• Buffer against imposter syndrome.
• Strengthen academic motivation and behavior to seek help.
• Enhance mental health and resilience.
• Act as the bridge between support structures and actual student outcomes: services exist, but belonging determines whether students use them.
• Have a particular impact for first-generation students, adult learners, students of color, part-time students, and commuters.

Ways higher education can foster belonging include:

• Faculty and Staff Practices
  • Use inclusive pedagogy (transparent assignments, flexible pathways, varied participation modes)
  • Learn and use students’ names and preferred forms of address
  • Normalize struggle and growth in academic spaces
  • Embed belonging-affirming messages in syllabi and coursework
• Early and Continuous Touchpoints
  • First-year seminars and onboarding that emphasize “you belong here” rather than just compliance
  • Proactive advising and check-ins before problems escalate
  • Early alert systems paired with human follow-up, not just automated flags
• Structured Peer Connection
  • Use of cohort models, learning communities, or guided study groups
  • Peer mentoring that includes adult learners and commuters
  • Intentional design for hybrid and online students, not afterthoughts for these student groups
• Campus Climate and Systems Design
  • Clear, consistent communication across offices
  • Curricula that reflect diverse perspectives and lived experiences
  • Institutional storytelling that highlights multiple definitions of success
  • Simplified processes that reduce friction and stigma
  • Policies that assume students want to succeed (e.g., flexible attendance, compassionate leave)
  • Short, research-backed interventions that reinforce:
    • “People like me succeed here”
    • “Struggle is normal and temporary”
    • “Asking for help is a sign of engagement, not weakness”

The American Library Association (ALA) Bill of Rights and Code of Ethics are foundational documents that guide library services; policies and procedures related to collection development, user access, and intellectual freedom; and values.  Both documents have reflected evolving professional standards responding to societal changes and technological advancements for nearly 90 years. They are used by all types of libraries (public, academic, school, and special libraries) as a guiding framework to ensure free, fair and unbiased access to information—and are widely adopted by libraries and information institutions nationwide.

See: Bill of Rights / Code of Ethics — American Library Association (ALA) | Learn & Work Ecosystem Library

A security process to verify individuals’ identity by using their unique biological traits (fingerprints, facial recognition, eye iris scans, or voice patterns). Unlike traditional authentication methods that rely on passwords or PINs, biometric systems use attributes unique to each person, making them more secure and harder to replicate.

Blockchain is a shared, distributed ledger technology in which records are stored together as blocks of information connected to other similar blocks of information. Blockchain use in educational records rests on the premise that giving learners access to and control over their educational records enables the easier sharing of their knowledge, skills, and work experience with employers and educational providers. This opens avenues to start or further their careers and increase their economic and social mobility.

In Europe, the fishing and aquaculture sectors are known as the “Blue Economy” with the recognition that food-related challenges are central to the planet’s future and intersect many other aspects, such as environmental sustainability, health, workers’ rights, and technologies to improve the supply chain. More than 20 million people are estimated to be employed in aquaculture, and that number is expected to grow significantly.

• manufacturing, mining, and construction
• agriculture, forestry, fishing and hunting
• retail and trade
• hospitality or service
• transportation.

See: Research: Blue-Collar Workers – Pew Research Center 

Regional education service organizations in the United States, commonly found in Colorado and New York. These entities provide shared educational programs and services to multiple school districts within a defined geographic area. BOCES aim to enhance educational efficiency and effectiveness by offering specialized resources, such as career and technical education, special education, and professional development, which might be challenging for individual school districts to provide independently. BOCES operate as collaborative entities, fostering educational partnerships and resource-sharing among participating school districts to address a variety of academic and operational needs.

Seeks to bring coherence to higher education systems across Europe. It established the European Higher Education Area (EHEA) to facilitate student and staff mobility, make higher education more inclusive and accessible, and make higher education in Europe more attractive and competitive worldwide. As part of the EHEA, participating countries agree to: introduce a 3-cycle higher education system consisting of bachelor’s, master’s, and doctoral studies; ensure mutual recognition of qualifications and learning periods abroad completed at other universities; and implement a system of quality assurance, to strengthen the quality and relevance of learning and teaching. Launched with the Bologna Declaration of 1999, the Bologna process is implemented in 49 countries, which define the EHEA. To become a member of the EHEA, countries have to be party to the European Cultural Convention and declare their willingness to pursue and implement the objectives of the Bologna Process in their own systems of higher education.

Refers to workers who return to their former employers after leaving their jobs. Key benefits of this practice include (1) employees can return to work without much onboarding or training, and (2) recruiters can streamline the hiring process.  Considerations for this practice include (1) the importance of evaluating why the employee left and whether concerns have been addressed, and (2) ensuring employers do not unintentionally communicate to current staff that employees must leave to get a raise or promotion.

To facilitate hiring former employees, employers can focus on an off-boarding process in which they make it clear to employees that their work has been valued, and employees would be welcomed back if an opportunity arises.

In the learn-and-work ecosystem, refers to short-term, specialized and intensive training focused on technical skills, often in the Information Technology (IT) sector (for example, Coder, Web Developer, Software Development, Python Programming Skills). Boot camps are often offered by third-party providers as well as by colleges and universities that partner with third-party providers. Many bootcamps partner with major hiring companies such as Microsoft, IBM, and Amazon.

An open standard developed by 1EdTech (formerly IMS Global Learning Consortium) that provides a structured approach to describing, collecting, and exchanging learning activity data at scale. This open standard defines a common vocabulary and data format for learning events, facilitating interoperability among educational tools and platforms. Caliper also specifies an application programming interface (API), known as the Sensor API™, for transmitting event data from instrumented applications to target endpoints for storage, analysis, and use.

Unizin integrates Caliper Analytics into its Unizin Data Platform (UDP) to collect and process learning activity data from various educational tools and platforms. By leveraging the Caliper standard, Unizin ensures that learning activity data from diverse sources can be integrated, analyzed, and utilized to enhance educational outcomes across its member institutions.

See:  https://learnworkecosystemlibrary.com/initiatives/unizin-data-platform-the-caliper-analytics-standard/

See: Unizin | Learn & Work Ecosystem Library

According to iCIMS (provider of talent acquisition software), candidate relationship management (CRM) software helps employers connect with current and future job candidates to help fill positions faster. CRM makes it possible to build pipelines of talent and efficiently market your recruitment brand to the best candidates through automated email marketing, recruiting event functionality, job recommendation portals and more.

Refers to workers in three industry sectors: healthcare, social services, and education and childcare industries. The care workforce in the U.S. is large, accounting for nearly 20% of the total workforce. Healthcare practitioners and technical support occupations make up 26% of the care workforce, healthcare support occupations make up 27%, and education occupations make up 26%. The remainder are working in social sciences and social services.

Education and training requirements vary widely for care workers. Jobs such as childcare workers, home health aides, and human services assistants have fewer requirements and earn lower pay. Physicians, nurses, teachers, social workers, and psychologists require higher levels of education and earn higher annual pay.

There are also key differences by gender and race/ethnicity in the care workforce. The majority of care workers are women; and Black women play a larger role in the care workforce compared to their role in the overall workforce.

Refers to educational programs or skills-based teaching that provide hands-on, realistic experience where students learn technical and employable skills required for specific jobs or fields of work and often specialize in the skilled trades, applied sciences, modern technologies, and career preparation. CTE programs are typically developed with input from industry partners to be responsive to workforce needs.  CTE is also referred to as work-based learning (WBL) or career-connected learning (CCL). CTE is often called vocational education, though CTE is the preferred term more recently in the U.S.

CTE is often offered in middle schools, high schools, community colleges, and other postsecondary institutions and industry certification programs. At the secondary level, CTE is frequently provided by regional centers that serve students from multiple schools or school districts. Many states have regional centers or statewide networks that operate as part of the public-school system.

CTE programs typically offer both academic and career-oriented courses. Many provide students with the opportunity to gain work experience through internships, job shadowing, on-the-job training, and industry-certification opportunities. Depending on their size, configuration, location, and mission, CTE provides a wide range of learning experiences spanning many career tracks, fields, and industries (e.g., automotive technology, construction, plumbing, electrical contracting, agriculture, architecture, culinary arts, fashion design, filmmaking, forestry, engineering, healthcare, personal training, robotics, and veterinary medicine).

Career coaches are experts in career planning, resumé building, negotiation, and interviewing. A career coach helps working professionals and recent graduates make educated decisions about their careers. Career coaches focus on actions, results, and accountability, seeking to inspire and empower their clients to set and achieve career goals.

Career counselors work mostly with college students and recent graduates. They are frequently found in community colleges, universities, nonprofit organizations, and high schools.

A career credential is a recognition of learning or competence earned through postsecondary education, training, or professional development that prepares individuals for employment or advancement in a specific occupation or career pathway. Career credentials may be credit-bearing or noncredit, but are unified by their focus on developing skills, knowledge, and competencies that have clear labor market value.

The term is increasingly used by accreditors and education policymakers—such as the Higher Learning Commission (HLC)—to describe noncredit certificates and similar programs that are intentionally designed for career preparation or advancement. By emphasizing the career relevance rather than the credit status of the credential, the term helps bridge traditional divides between academic and workforce pathways. It reinforces that high-quality noncredit credentials can offer equal or even greater value to learners and employers when aligned with industry needs, transparent outcomes, and recognized quality assurance frameworks.

The Velocity Network Foundation also uses this definition: Career credentials are verifiable records of an individual’s professional qualifications, achievements, and experiences. These credentials can include a wide range of information, such as: educational qualifications, degrees, diplomas, or certifications obtained from educational institutions; professional licenses or certifications, proof of skills or competencies certified by recognized organizations or authorities (e.g., a professional engineering license or a medical board certification); employment history, records of previous jobs, roles, and responsibilities, issued by employers or professional organizations; and skills and competencies, verified assessments or endorsements of specific skills and abilities relevant to an individual’s career.

See Topic Brief: Career Credential | Learn & Work Ecosystem Library

According to Owl Labs’ 2024 State of Hybrid Work report, this term refers to the process of individuals’ preparing for a potential layoff by applying to other jobs, upskilling or reskilling to become a more competitive candidate, and/or diversifying their income stream by taking on side hustle(s). This term has emerged in the volatile economic environment and job market.

Career navigation services help individuals of all ages understand how their personal interests, abilities, and values can help shape their educational and career goals and contribute to their success. A range of providers offer career navigation services including school and college counselors, third-party career counselors (working in-person and online), military transition centers and recruiters, prison centers/offender rehabilitative services, immigration centers, and the U.S. Department of Labor.

Career networking (also known as job seeking through networking) uses an individual’s networks (family contacts, professional colleagues, friends and other personal contacts) in career development.  Career development can include job searching such as learning about career fields, job opportunities, and companies an individual may be interested in working in; and ways to achieve career goals. Technology is increasingly important to career networking; for example, social networks like Meta (formerly Facebook) and Linkedin, and online job-matching platforms like ZipRecruiter, Monster, and Career Builder.

See Topic: Social Capital

An educational or workforce development institution that offers training programs that are designed to help individuals maintain employment and advance within a career field. These centers focus on skills development, reskilling, and upskilling aligned with long-term labor market needs. Common features offered at these centers include ongoing professional or technical training, support for industry-recognized certifications, and partnerships with employers or labor organizations

The term may vary by region or program and is often associated with community colleges, workforce boards, or union-sponsored training initiatives.

As defined by the Velocity Network Foundation, an application that individuals can install on their devices to store and manage their verified professional credentials privately. These wallets enable users to claim and share digital proofs of their employment history, education, skills, and qualifications.

As defined by the Colorado Department of Higher Education, refers to an educational institution that prioritizes practical skills, professional development, and career readiness alongside academic learning. A career-connected college or university campus is committed to empowering students with the knowledge, skills, and resources needed to succeed in their chosen careers and make meaningful contributions to their respective industries.

An educational model that combines classroom learning with on-the-job work experiences.  Learners are introduced to career paths and employment opportunities, especially in the middle skills sector. In CCL programs, learners are provided opportunities to gain minimum relevant work experience, technical/vocational skills, and soft skills that are typically credentialed to verify learning and experiences.

Related terms: Work-based learning (WBL), vocational education and training, career and technical education (CTE).

The caregiving workforce refers to the paid and unpaid individuals who provide health, personal care, supervision, social support, and related services to individuals who require assistance due to age, disability, illness, or developmental needs. This workforce spans the lifespan and includes workers serving children, individuals with disabilities, chronically ill individuals, and older adults.

The caregiving workforce includes direct care workers (such as home health aides, personal care aides, and direct support professionals), licensed health professionals (including nurses and therapists), social workers, case managers, care coordinators, and family caregivers. Services are delivered across diverse settings, including private homes, childcare centers, community-based organizations, healthcare facilities, long-term care settings, and residential programs.

Discussions about the caregiving workforce in the U.S. trace back to the emergence of modern long-term care systems and labor policy debates in the 20th century. The term gained broader economic and social recognition in the late 20th and early 21st centuries alongside research on the “care economy” — a framing that highlights the indispensable labor of caregivers, its contribution to the broader economy, and the disparities in pay, training, and support experienced by many in these roles. This framing has shaped policy conversations about labor rights, workforce development, and social safety nets.

In the U.S. today:

• Family and unpaid caregivers: An estimated 63 million Americans are family caregivers — nearly one in four adults — providing care for children, adults with disabilities, or older adults, a figure that has increased by roughly 45% over the past decade. About 11 million of these caregivers receive some form of compensation through Medicaid, VA, or state programs.
• Direct care workers: The paid caregiving workforce — often described in research as the direct care workforce — includes roughly 5.4 million workers such as home care workers, residential care aides, and nursing assistants, and projections suggest significant growth in demand for these roles in coming years.
• Workforce pressures: Nearly half of direct care workers rely on public assistance due in part to low wages, and job openings in direct care are projected to be very high over the next decade as demand rises with the aging population.

In policy and economic discussions, the caregiving workforce is often associated with the broader “care economy,” reflecting its central role in supporting labor force participation, economic stability, and community well-being. Challenges facing this workforce include labor shortages, low wages in many direct-care occupations, high turnover, inconsistent training and credential requirements, and uneven access to benefits and career advancement pathways.

The eldercare workforce, a subset of the caregiving workforce, defined specifically by its focus on serving older adults, and other subsets include the childcare workforce and the disability services workforce.

See: Eldercare Workforce | Learn & Work Ecosystem Library

A framework used to categorize nearly 4,000 thousand higher education institutions in the United States.  Carnegie Classifications are used in the study of higher education, including in research study design to ensure adequate representation of sampled institutions, students, and/or faculty. First published in 1973, the framework is updated every 3 years to reflect changes among colleges and universities.

There are two types of classifications: (1) Universal classifications are 6 types of organizational groupings and labels given to all degree-granting institutions in the U.S.:  Basic, Undergraduate Instructional Program, Graduate Instructional Program, Enrollment Profile, Undergraduate Profile, Size and Setting. Classifications are based on the data the institutions report to federal sources, including the National Center for Education Statistics and National Science Foundation. (2) Elective includes 2 types that institutions must apply for: Classification for Community Engagement and Classification for Leadership for Public Purpose. These require documentation of institutional policy and practices focusing on areas such as institutional culture and mission, curricular and co-curricular programming, continuous improvement activities, and the recruitment and reward of faculty, staff, and students.

In 2022, the American Council on Education (ACE) began a partnership with the Carnegie Foundation to take stewardship of both the Universal classifications (previously managed by Indiana University-Bloomington), and the Elective classifications. ACE and the Carnegie Foundation are collaborating on the vision and future of the framework including revising the methodology for the Basic classification, adding a Social and Economic Mobility Universal classification, and expanding the suite of Elective classifications.

Refers to the 1EdTech standard that enables consistent format and exchange of information about learning and education competencies, skills, or academic standards in an open, machine-readable format. The CASE Network was launched by the 1EdTech community to enable all 50 U.S. states to use interoperable academic standards and national learning standards. Through CASE, it is possible to electronically exchange outcomes, skills, and competency definitions so that applications, tools, and platforms can access the data. This enables school districts, schools, and other users in the learn-and-work ecosystem to act upon this data and support instruction accurately. The Network has built a central repository of K-12 state academic standards and competencies frameworks (414), and other national learning standards. These are available in 11 categories: English Language Arts; Math; Science; Social Studies; World Languages; Computer Science; Fine Arts; Health; Physical Education; Career/Tech; Other.

Catfishing by employers: when employees report that the position did not match what was described during the hiring process or the job had more responsibilities than anticipated. This can include:

• the role did not live up to the description provided by a recruiter or hiring manager
• the work responsibilities were different than expected
• the company culture was misrepresented
• the compensation or benefits were overstated.

Career catfishing by job candidates: when candidates have exaggerated their qualifications or background to land a job. This can include:

• overstating job responsibilities in previous roles
• overemphasizing skills or technical abilities
• boosting their work experience
• misrepresenting their education or certifications.

Various employer studies have found that catfishing by both employers and job candidates has increased in recent years.

In research, provides evidence of the effectiveness of a program, intervention, or policy change on one or more desired outcomes. Well-designed studies will provide a credible simulation of the program, intervention, or policy change (“solution”) with an unbiased comparison group because it is not possible to observe the difference between outcomes for those receiving a solution, given concerns those outcomes would have happened had the same people not received this solution during the same timeframe.

Type of award conferred by a college, university, or other postsecondary education institution indicating the satisfactory completion of a non-degree program of study. Typically, the course requirements for earning a certificate are less than those for earning a degree. Most certificates require no more than one year of full-time academic effort. A certificate may be for-credit (academic certificate) or non-credit (continuing education certificate). They are not time limited and do not need to be renewed.

There are a number of titles that speak to how the certificate was earned:

• Certificate of Achievement
• Certificate of Participation
• Certificate of Completion
• Assessment-based Certificate
• Course Completion Certificate
• Bootcamp Course Completion Certificate
• Online Course Completion Certificate

Awarded by certification bodies—typically nonprofit organizations, professional associations, industry and trade organizations, or businesses—based on an individual demonstrating, through an examination process, that she/he has acquired the knowledge, skills, and abilities required to perform a specific occupation or job. Depending on the certification body, they may be called industry or professional certifications. Although training may be provided, certifications are not tied to completion of a program of study as are certificates. They are time limited and may be renewed through a re-certification process. Some certifications can be revoked for a violation of a code of ethics (if applicable) or proven incompetence after due process.

A marketplace which ensures that job seekers have verified skills and credentials that align with employer’s job requirements.  Key features of a certified talent marketplace often include:

• Credential Verification – Uses blockchain, AI, or direct partnerships with credentialing bodies to validate worker qualifications.
• Skills-Based Matching – Matches candidates with job opportunities based on verified competencies rather than traditional resumes.
• Workforce Development Integration – Connects learners with upskilling or reskilling opportunities to enhance their employability.
• Employer and Industry Collaboration – Engages employers to ensure job postings align with current skill demands.
• AI-Driven Recommendations – Uses data analytics and machine learning to improve job matches and career pathways.

Alternative names:

• Skills-based Talent Marketplace
• Verified Talent Marketplace
• Credentialed Talent Exchange
• Skills Matching Platform
• Digital Talent Platform

Refers to the practice of a higher education institution merging with another higher education institution facing closure typically due to severe financial problems. The institution being “bought” is often a private accredited institution. The new arrangements enable students from the closing institution to transfer their credits to the new institution and continue their pathway toward credential completion. An example is Northeastern University (Boston) entering into merger arrangements with 14 private, accredited schools as part of a trend of “chain buying” in the private school ecosystem.

See: Mergers & Acquisitions / Consolidations in Higher Education

In credit for prior learning (also known as prior learning assessment, recognition of prior learning, lifelong learning credit, or experiential learning) refers to the assessment in which academic discipline faculty in colleges/universities administer locally developed examinations to determine whether a student can illustrate the learning outcomes of a course.  Challenge exams are known by terms such as institutional exams, credit by exam, departmental exams, or proficiency exams. The assessment process provides academic departments the flexibility to tailor exams to fit specific course curricula, give program faculty confidence the exams reflect an appropriate level of academic rigor, and provides faculty direct control of the assessment process.

Refers to the methods and practices used to help people and organizations move from current ways of working to new ones. It focuses on preparing stakeholders, communicating clearly about what is changing and why, supporting people as they adapt, and ensuring that new approaches become part of regular practice.

In the learn-and-work ecosystem, change management is important because many parts of the system are evolving at the same time. New technologies, shifting workforce skill needs, new types of credentials, and growing expectations for lifelong learning are prompting organizations to rethink how education and employment systems operate. These transitions often involve both the introduction of new tools or systems and changes in institutional practices and expectations. They may also require coordinated changes across multiple organizations, including education providers, employers, workforce agencies, policymakers, and technology platforms.

Responsibility for change management is typically shared among:

• Leaders, who establish priorities and signal commitment to change.
• Implementation teams, who design and guide implementation.
• Managers and practitioners, who integrate new approaches into daily operations.

Change management efforts often fall short when new ideas, tools, or policies are introduced but not fully integrated into everyday practice. Organizations may launch initiatives, pilot programs, or new technologies yet underestimate the time, coordination, and communication required for people to adopt new ways of working. As a result, promising initiatives may advance unevenly or stall before reaching scale. Effective change management helps address these challenges by aligning leadership support, implementation planning, and day-to-day practice across the organizations involved.

Refers to a geographic area that is most commonly measured at the census tract level (a small statistical area, roughly the size of a neighborhood, used by the U.S. Census Bureau to organize demographic data) and where the supply of licensed childcare is significantly insufficient relative to the number of children who potentially need care. A widely used definition for a childcare desert is when there are three or more children for every available licensed childcare slot, or when no licensed providers exist within that geographic boundary. While definitions vary across research and policy contexts, the “three children per slot” threshold has become a commonly cited benchmark for identifying substantial supply shortages.

The term originated in early childhood policy research and is now widely used by advocacy organizations, government agencies, and workforce development stakeholders to quantify geographic inequities in access to childcare services.

Childcare deserts are typically identified using census tract–level population data, licensed provider capacity data, and ratios comparing children (often under age five) to available licensed slots. Definitions may also differ based on age groups included (infant/toddler vs. all children under five), whether public programs (e.g., Head Start, pre-K) are counted as supply, rural distance measures (travel time to providers), and minimum population thresholds for classification. Despite these methodological differences, the core concept highlights structural access gaps rather than individual family circumstances.

The concept of childcare deserts has increasingly shaped public policy discussions and investment strategies. Although federal statutes do not consistently codify “childcare desert” as a formal legal category, major federal programs require states to assess childcare availability and address supply shortages. As a result, many states operationalize childcare desert mapping to guide funding decisions. Federal policy mechanisms influencing responses include:

• Child Care and Development Block Grant (CCDBG / CCDF): Requires state needs assessments examining availability and access; desert mapping often informs subsidy and expansion strategies.
• Preschool Development Grants Birth-Through-Five (PDG B-5): Supports statewide early childhood system planning; supply-gap analysis frequently guides strategic expansion.
• Pandemic-era stabilization funding (e.g., American Rescue Plan investments): Enabled states to prevent provider closures and prioritize expansion in underserved areas.

States increasingly use childcare desert analysis to design targeted interventions, including:

• Start-up and expansion grants for providers in underserved areas
• Capital construction or renovation funding
• Incentives such as higher reimbursement rates for operating in desert regions
• Workforce initiatives addressing staffing shortages that limit provider capacity
• Public-private partnerships to build community-based solutions

In some states, legislation or grant programs explicitly reference “childcare deserts” when prioritizing investments.

Childcare deserts are increasingly recognized as more than early childhood service gaps; they represent critical infrastructure challenges within the broader learn-and-work ecosystem. Limited childcare availability can affect labor force participation, adult learner enrollment and persistence, workforce mobility and reskilling, and regional economic development. As policymakers adopt skills-based workforce strategies and learning mobility frameworks, access to reliable childcare is increasingly framed as enabling infrastructure that supports participation in education, training, and employment pathways.

A term coined by Cassie Kozyrkov in 2025 to refer to individuals who combine human skills—such as creativity, judgment, and ethical reasoning—with AI-enabled tools and capabilities to expand their professional or learning capacity. Like the Greek mythological chimera—a creature made of distinct parts—chimeric talent represents a new type of workforce augmented by AI, defined by a hybrid skill profile including:

• Rapid skill-shifting: Moving quickly into adjacent or even new capability domains because AI reduces the barrier to entry.
• Role shapeshifting: Boundaries among job roles, with workers able to do pieces of many roles.
• AI-augmented proficiency with core human strengths: Workers use new combinations of skills and tools including core human strengths of creativity, critical thinking, empathy, ethics, and communication, augmented with generative AI tools and digital fluency for analysis, content creation, and automation.
• Adaptability and continuous learning: the ability to learn, unlearn, and relearn quickly.
• Cross-functional collaboration: comfort working across traditional disciplinary or departmental boundaries using technology to connect ideas, people, and processes.

Use of this emerging term signals a shift in what talent means in the AI world: it is less about narrowly defined job titles and more about capability configurations and adaptability. It gives educators and employers a way to talk about hybrid profiles and new workforce demands. It also is useful in framing how future workforce preparation should evolve.

Related Terms

• AI‑augmented workforce: Humans augmented by AI tools.
• Hybrid skills: Describes blending technical + human + domain expertise to meet evolving work demands.
• T‑shaped skills (or T-shaped professionals): Describes an individual with deep expertise in one field (vertical bar) + broad capabilities/knowledge across others (horizontal bar). This term has been in use since the 1980s.

See Topic Brief:  Rise of an AI-Powered Workforce | Learn & Work Ecosystem Library

Refers to a school district (K-12) that allows families to actively select from a variety of educational options beyond their assigned neighborhood school. These options may include magnet schools, career academies, dual-enrollment programs, and specialized tracks aligned with students’ interests or career goals. Many choice districts invest in innovation hubs, technology-rich environments, and personalized learning pathways to attract and retain families seeking tailored educational experiences for their children.

An increasingly favored economic model in which resources minimize waste, pollution, and environmental impact using practices such as smarter product design, longer use, and recycling.  This model contrasts with the current linear economic model in which the system extracts raw materials from nature, turns them into products, and discards them as waste. Estimates are that only 7.2% of used materials are currently cycled back into economies after use. This contributes to growing concerns around climate, biodiversity, and pollution – and calls for a circular economic model. A circular economy will place new demands on the learn-and-work ecosystem, especially the preparation of a workforce with skills to design smarter products, longer use of products, and recycling.

Civic Education Centers are academic units or programs within colleges and universities that emphasize the study of Western intellectual traditions, classical texts, civic philosophy, and foundational concepts in political and moral thought. These centers typically focus on curriculum in history, philosophy, literature, and civics, often through reading-based seminars and interdisciplinary coursework.

In recent years, several public universities in the United States have established such centers through state funding or governing-board initiatives. Their stated purposes commonly include strengthening civic literacy, encouraging debate on foundational ideas, and broadening perspectives in undergraduate education. Governance structures, faculty hiring practices, and curricular roles vary across institutions.

The U.S. Department of Education (through the National Center for Education Statistics or NCES) developed a classification of instructional programs (CIP codes) in 1980 to support accurate tracking and reporting of fields of study and program completions.  The CIP taxonomy is typically updated every 10 years. The majority of CIP titles correspond to academic and occupational instructional programs offered for credit at the postsecondary level. These programs result in recognized completion points and awards, including degrees, certificates, and other formal awards.

The CIP also includes instructional programs such as residency programs in various health professions that may lead to advanced professional certification; personal improvement and leisure programs; and programs taught in schools of continuing education and professional development (the majority of the latter group are non-credit).

The CIP is the accepted federal government standard on instructional program classifications and is used in a variety of education information surveys and databases.

States like Minnesota and Texas assign CIP codes to all approved academic programs, facilitating standardized data collection and reporting.

See Topic: Classification of Instructional Programs (CIP) | Learn & Work Ecosystem Library (learnworkecosystemlibrary.com)

Closed-circuit AI models are artificial intelligence (AI) systems designed to operate within restricted, organization-controlled environments. They are trained or configured using limited, curated datasets specific to a defined domain, organization, or set of tasks. These models are intentionally constrained in scope, data access, and behavior to meet requirements related to privacy, compliance, reliability, and operational control, especially by companies that commission closed-circuit models.

Employers often adopt closed-circuit AI models for use cases in human resources, legal review, training systems, and internal knowledge management, particularly where sensitive data, regulatory obligations, or reputational risk are involved. While these models typically offer less breadth than general large language models (LLMs), they provide greater governance, predictability, and alignment with organizational requirements.

Closed-circuit does not mean that the information received from AI is more accurate or transparent; rather, it reflects a design choice that prioritizes control and accountability over open-ended exploration. Many organizations increasingly use hybrid approaches, combining the breadth of LLMs with closed-circuit controls. Employers in regulated and high-risk environments (e.g., healthcare, finance, law) increasingly favor closed-circuit AI deployments due to data sensitivity, compliance requirements, and reputational considerations. Consequently, many organizations limit general-purpose AI tools to exploratory or low-risk tasks, reserving operational decision-making for constrained systems.

See Glossary: Large Language Models (LLMs) | Learn & Work Ecosystem Library

See Topic Brief: AI Architectures in the Workplace: Large Language Models & Closed-Circuit AI Systems | Learn & Work Ecosystem Library

Co-location refers to an institutional arrangement in which two or more distinct organizations—most commonly postsecondary institutions, but also employers, workforce organizations, or community partners—share physical space or operate in close geographic proximity while maintaining separate governance, accreditation, missions, and identities.

In higher education, co-location is often used as a strategic model to improve institutional sustainability, expand access to facilities and services, strengthen transfer and credential pathways, and better align education with regional workforce and economic development needs. Unlike mergers or consolidations, co-location does not eliminate institutional boundaries; instead, it emphasizes partnership, shared infrastructure, and coordinated operations.
Co-location arrangements may include shared classrooms, laboratories, student services, libraries, or administrative functions, as well as opportunities for cross-registration, joint programming, and employer-connected learning experiences. When implemented effectively, co-location can reduce operating costs, increase enrollment, enhance student outcomes, and reinforce regional talent pipelines—while allowing participating institutions to retain their distinct missions and community roles.

An example of co-location is Clinton Community College’s move to the SUNY Plattsburgh campus in New York’s North Country region. Facing enrollment declines and financial challenges, the community college relocated to the university’s campus while preserving its institutional identity. The co-location improved access to facilities and student services, strengthened healthcare and manufacturing workforce programs through proximity to employers and hospitals, enabled cross-registration, and created clearer pathways from certificates and associate degrees to bachelor’s degrees—benefiting students, both institutions, and the regional economy.

See glossary term: Institutional Merger & Integration Models (Higher Education) | Learn & Work Ecosystem Library

A model of work in which outcomes are produced through sustained interaction between humans and intelligent machines, with responsibility and influence distributed across human judgment, decision-making, and machine-driven pattern recognition and execution.

Prior to roughly 2023, most discussions of AI in the workplace framed human–machine interaction in terms of automation, augmentation, or AI assistance, positioning machines primarily as tools that replaced or supported specific human tasks.

With the increasing adoption of generative AI systems, this framing has shifted toward co-production, reflecting the growing reality that work outcomes increasingly emerge from joint human–machine activity (shared agency). This is characterized by humans shaping goals, context, and constraints while machines extend, recombine, and scale linguistic and computational patterns across workflows.

In response to return-to-office mandates by companies, refers to the practice of workers going into the office, making their presence felt, and then leaving for alternative work environments more conducive to their working habits.

As defined by the Collective Impact Forum and FSG in Guide to Evaluating Collective Impact, Collective impact (CI) occurs when a group of actors from different sectors commit to a common agenda for solving a complex social or environmental problem. More than a new way of collaborating, collective impact is a structured approach to problem solving that includes five core conditions:

• Common Agenda: All participants have a shared vision for change, including a common understanding of the problem and a joint approach to solving it through agreed upon actions.
• Shared Measurement System: Collecting data and measuring results consistently across all participants ensures that efforts remain aligned and participants hold each other accountable.
• Continuous Communication: Consistent and open communication is needed across the many players to build trust, assure mutual objectives, and create common motivation.
• Backbone Function:  Creating and managing collective impact requires dedicated staff with specific skills to coordinate participating organizations and agencies.
• Mutually Reinforcing Activities: Participant activities must be differentiated while still being coordinated through a mutually reinforcing plan of action.

College Assistance Migrant Programs (CAMP) are federally funded through the U.S. Department of Education, Office of Migrant Education. The CAMP program was established to identify, recruit, admit, and enroll migrant and seasonal farm worker students and provide them with academic, social, and financial support to enable the completion of their first year of college. Programs are supported at higher education institutions in the U.S. through a grant competition. Typically, CAMP programs:

• Help students transition from secondary school to postsecondary school.
• Fosters the knowledge, skills, and attitudes necessary to succeed in postsecondary school.
• Facilitates students’ development of the academic skills, personal self-confidence, and financial resources necessary to continue in an undergraduate program.
• After their first year of participation, students often have the opportunity to receive ongoing academic support services that facilitate CAMP students’ retention in higher education through graduation and beyond.

Refers to geographical areas in which people live more than a 30-minute drive to a higher education campus, raising concerns about the effects of college location on educational attainment. Research shows that individuals who reside close to a college are more likely to attend, and that higher education deserts affect some learner populations differently than others.

• A study of a set of high school and college data in Texas, found that Black and Hispanic students and those in low-income families who lived more than 30 miles from a public two-year college were significantly less likely to attend college; while white and Asian students in those same communities were slightly more likely than others in the state to complete four-year degrees.
• According to research by Nicholas Hillman, Black and Hispanic students are more likely than those in other groups to live in a college desert.

Apprenticeship programs that are formally linked to postsecondary education institutions and offer academic credit for some or all of the learning completed during the apprenticeship. These models may include degree apprenticeships, which are structured to lead directly to an associate or bachelor’s degree. College-connected apprenticeships create transparency between on-the-job learning and college curricula, allowing apprentices to earn both industry-recognized credentials and academic credentials in a streamlined pathway. They leverage the strengths of both the workplace and classroom, expanding access to education and reducing time and cost to degree completion.

A job description explains the tasks, duties, functions, and responsibilities of a position. A common job description outlines the expectations of a job in a way that is comparable to other similar positions.

According to EDUCAUSE, the Information Technology (IT) services that facilitate educational institutional communication and collaboration needs such as e-mail, calendaring, telephony/Voice over Internet Protocol, video/web conferencing, unified communications, web content management system, web application development and hosting, and media development.

According to EDUCAUSE, the domain refers to the functions and resources in education that enable faculty, staff, and students to communicate and collaborate with others on and off campus. Depending on the institution, services may include:

• Wire and cable infrastructure for data, voice and/or video networks
• Campus data network
• Wireless network
• Video network
• Remote access (VPN)
• Commodity Internet
• Network management (e.g., capacity planning, performance monitoring, change management)
• IPv6
• Unified communications and collaboration
• Dial tone (including services to student housing)
• Telephone services (VoIP or conventional)
• Session Initiation Protocol (SIP) trunking
• Voicemail
• Cellular and paging services
• Telecommunications (consulting, design)
• Video surveillance systems
• Cable TV
• Network, phone, and cable TV delivery and operation in residence halls
• Digital signage systems
• Emergency notification
• E-mail for faculty, staff, or students
• Calendaring
• Communication and collaboration technologies (e.g., video and web conferencing, Listserv, SharePoint, shared web browsing, wiki, Google Docs, MS OneDrive)
• Business process/systems analysis specific to this domain area
• Technology research and development specific to this domain area
• Management responsibilities (HR management, financial planning, project management, vendor contract management, etc.) for staff dedicated to this domain area
• Staff affiliated with these functions (including administrative, clerical)
• Hardware, software, and supplies affiliated with these functions

Services typically not included:

• High-performance research network (e.g., Internet2, National LambdaRail) (generally falls with Research Computing Services)
• Network behavior analysis (generally falls within Information Security)

According to EDUCASE, refers to the measures and controls taken to deny unauthorized persons information derived from telecommunications, and to ensure the authenticity of such telecommunications; includes cryptographic security, transmission security, and emissions security.

Community college baccalaureates are new forms of baccalaureate degrees conferred by community colleges, which have historically awarded the associate degree as their highest credential. About half of the states provide authorization for some or all of their community colleges to award baccalaureate degrees.

A group of individuals who share common interests related to a specific domain and are committed to learning from one another to deepen their knowledge and expertise through collaboration information-sharing.  Often established in education healthcare, and other broad fields, three elements of a COP include: 1) common area of interest – the domain; 2) personal and professional interactions – the way in which individuals pursue their interests within the domain; 3) practice of learning from one another – members via ideas, tools, documents, case studies, frameworks, etc.

In large COPs, various sub-communities are often created to enable members to become more deeply engaged in a particular area of interest,

A COP usually differs from a Work Group. The latter focuses on new products or development of services, whereas the former focuses on furthering groups knowledge in a specific area.

Alternate terms: Learning Networks, Thematic Groups

As used by Achieving the Dream (ATD), refers to the collective well-being and prosperity of a community that is achieved through equitable access to education, economic opportunities, and social mobility. It emphasizes the interconnectedness of individual success and community development, aiming for a holistic improvement in quality of life across various societal measures.

See: Community Vibrancy Framework — Achieving the Dream (ATD) | Learn & Work Ecosystem Library

Refers to all sources of employee earnings, including hourly wages, salaries, overtime, bonuses, commissions, and benefits. Benefits refer to options with monetary value including health insurance, dental insurance, disability insurance, and access/contributions to pensions and retirement savings accounts.

Compensation and pay transparency refer to organizational and policy practices that make information about employee compensation—such as pay ranges, benefits, and total rewards—visible and accessible to workers and job seekers. These practices aim to promote fairness, reduce wage gaps, and enhance trust in workplace practices.

Pay transparency refers to the practice of openly sharing information about employee compensation, often including salaries, wage ranges, benefits, and sometimes bonus structures. This can occur at different levels—from posting salary ranges in job descriptions to making all employee pay public within an organization. The purpose of pay transparency is to promote fairness and equity in pay, reduce wage gaps (e.g., gender or racial disparities), increase trust and clarity in hiring and promotion practices, and help employees better understand how compensation decisions are made. While pay transparency can improve trust and retention, it also requires careful communication and consistent pay practices to prevent misunderstandings or employee dissatisfaction.

A related term (a subset of pay transparency) is compensation transparency which is generally considered an alternate term for pay transparency but can carry a slightly broader meaning. Pay transparency usually focuses on wages and salary ranges, sometimes including bonuses or commissions. Compensation transparency typically encompasses the entire compensation package—salary or wages plus benefits, incentives, retirement contributions, stock options, allowances, and other perks. An employer practicing full compensation transparency would disclose not only what people earn in base pay, but also the monetary value of the total rewards package. The purpose of compensation transparency is to provide employees with a holistic understanding of their total compensation package, support informed career and financial decisions, and encourage alignment between compensation philosophy and organizational values.

There are no federal laws mandating pay or compensation transparency. However, a growing number of state and local jurisdictions have enacted laws primarily focused on pay transparency, requiring employers to disclose salary ranges in job postings or upon request, and in many cases prohibiting questions about salary history. Examples of state laws in effect include:

• New Jersey – The Pay and Benefit Transparency Act took effect June 1, 2025, requiring employers with 10+ employees over 20 weeks to include salary (or range), benefits, and other compensation details in job postings—and to notify current employees of internal promotions
• Illinois – Since January 1, 2025, employers with 15+ employees must disclose salary ranges and benefit descriptions in job postings
• Minnesota – A law signed by the governor mandates that employers with more than 30 employees include salary ranges and a general description of benefits in job postings, effective January 1, 2025.

A measurable, assessable capability of an individual that integrates knowledge, skills, abilities, and dispositions required to successfully perform tasks at a determined level in a defined setting.

A Competency Model defines the competencies needed to identify, evaluate, and recognize effective performance attached to a job role or occupation, usually in the workplace. Models are often used as a guide to design training and learning activities; evaluate individual and team performance; recognize achievements and issue records of achievement; and identify individual, organization and industry sector needs, career planning, and managing talent.

A Competency Framework provides a standardized technical means (a pattern) for consistently describing specified attributes of the competencies in a competency model. The framework is often used as a template by which many template-conforming Competency Models can be created. This is an important distinction since there may be more than one competency framework in use; for example, 1EdTech’s CASE, the University of Washington’s ASN, IEEE’s Sharable Competency Definition; and Credential Engine’s CTDL-ASN (template used by Credential Engine to populate its Registry with Competency Models).

An example of the relationship between a framework and a model is the structure of entries in an address book (the framework) and an actual set of addresses (the model),

See: Skills Framework | Learn & Work Ecosystem Library (learnworkecosystemlibrary.com)

Competency-based learning, or competency-based education, often abbreviated as CBE, is a framework for instruction and assessment focused on students demonstrating defined learning objectives or competencies rather than content completion and curriculum schedules.  Students in such a framework typically work at their own pace, using learned knowledge and skills to demonstrate mastery of a subject. At some institutions of higher education, competency-based education courses begin and end throughout the year, independent of a traditional academic calendar.

Competency-based programs transparently communicate the learning objectives students must achieve to earn degrees and other credentials; enable students with existing knowledge and skills to personalize their educations and accelerate progress towards completion; use technology that enables students to learn anytime, anywhere, at prices they can afford’ and integrate support from faculty, mentors, and coaches that can build confidence needed for success, aimed at creating fair and just educational results.

An online, self-paced professional development program operated by the Competency-Based Education Network (C-BEN) to train educators, workforce leaders, and Human Resource (HR) professionals in competency-based education (CBE). Launched in 2025, CompetentU offers modular courses and a certificate that model CBE principles while providing practical skills in developing competencies, performance-based assessments, and personalized learning pathways. The program combines digital instruction with coaching and peer learning, enabling participants to design, implement, and scale CBE programs across educational and workforce settings.

See: CompetentU – Competency-based Education Network (C‑BEN) | Learn & Work Ecosystem Library

The federal Workforce Innovation and Opportunity Act (WIOA) defines competitive integrated employment (CIE) as work performed on a full- or part-time basis for which an individual with disabilities is:

• Compensated at or above minimum wage and comparable to the customary rate paid by the employer to employees without disabilities performing similar duties and with similar training and experience.
• Receiving the same level of benefits provided to other employees without disabilities in similar positions.
• At a location where the employee interacts with other individuals without disabilities.
• Presented opportunities for advancement similar to other employees without disabilities in similar positions.

The Office of Employment Disability Policy (ODEP) operates the CIE Transformation Hub to collect resources across the federal government that are working to increase the participation of people with disabilities in CIE, such as:

• VOICE (Visionary Opportunities to Increase Competitive Employment)
• Veterans RICE (Returning to Integrated Competitive Employment)
• NEON (National Expansion of Employment Opportunities Network)
• ASPIRE (Advancing State Policy Integration for Recovery and Employment).

ODEP resources provide guidance, policy information, and evidence-based best practices used by people with disabilities and their families, employers, employment service providers, and state agencies. Since 2012, ODEP has provided technical assistance on aligning state policies across multiple service systems.

Seek to capture, record, and communicate learning when and where it happens in a student’s higher education experience. This includes learning from courses, programs, and degrees, as well as experience outside the classroom that helps students develop career-ready skills and abilities (often known as co-curricular learning). A growing list of colleges and universities and third-party intermediaries are working to make CLRs more widely adopted as a way to more accurately and fully validate individuals’ skills and competencies.

Refers to an integrated approach to improving the skills and employment opportunities for workers across a state. Systems align the efforts of various stakeholders (e.g., government agencies, educational institutions, employers, community organizations) to meet the workforce needs of individuals and businesses. The goal is to create a pipeline of skilled workers who can fill in-demand jobs and provide pathways for workers to advance in their careers.

A statewide workforce development system usually has a legal framework grounded in both federal legislation such as the Workforce Innovation and Opportunity Act (WIOA) and state-specific legislation or executive actions. WIOA provides the primary federal funding mechanism for workforce development, giving states the flexibility to design programs that address local economic needs while meeting performance metrics.

Key agencies in systems include the Departments of labor, economic development, and education, and these agencies usually oversee the system at the state level, often in collaboration with regional/local workforce boards. Workforce development systems are generally measured based on employment outcomes, retention rates, wage growth, and employer satisfaction with the workforce.

Key roles of the system:

1. Coordination Integration: Collaboration among state and local agencies, educational institutions (K-12, community colleges, universities), workforce boards, nonprofits, and employers includes centralizing resources such as training programs, career counseling, and job placement services.
2. Education/Training:  Access to upskilling, career/vocational training, apprenticeships, and continuing education to prepare individuals for entry-level jobs and advanced career opportunities.
3. Employer Partnerships: Collaborating with industries to identify current and future workforce needs and tailoring training programs to meet needs. Employers often provide internships, apprenticeships, and other work-based learning opportunities.
4. Workforce Boards: Local and state workforce boards (under Workforce Innovation and Opportunity Act – WIOA) oversee and coordinate training programs, career services, and employment initiatives.
5. Technology/Data Integration: Using data to track labor market trends, analyze workforce needs, and match workers with opportunities. This includes online platforms for job searches, training enrollment, and skills assessments.
6. Access/Equity: Ensuring underserved populations (e.g., veterans, people with disabilities, low-income individuals, people of color) have access to training and employment opportunities, with special focus on overcoming barriers to employment.

 Trends and innovations in systems include:

• Sector-based strategies: Focus on specific industries critical to the state’s economy (e.g., IT, healthcare, manufacturing).
• Apprenticeships and Earn-and-Learn Programs: Enable workers to gain experience while earning wages, addressing skill gaps in real-time.
• Credentialing and Industry Certifications: Emphasize industry-recognized credentials valued by employers and that provide career mobility to workers.
• Digital Access and Virtual Training: Expansion of online resources and virtual training programs.

Examples of state systems include:

• California: The California Workforce Development Board (CWDB) coordinates with local boards and partners to meet the diverse needs of the state’s workforce. The CWDB is known for its sector-based strategies, focusing on in-demand industries like technology, healthcare, and manufacturing. California has specific initiatives like the High Road Training Partnerships (HRTP) that promote job quality, wage increases, and career mobility in key sectors.
• Michigan: Michigan Works connects job seekers with career opportunities and employers with talent. Michigan focuses on regional partnerships and integrated service delivery through its Going PRO Talent Fund, which supports employers in training and developing skilled workers.
• North Carolina: NCWorks aligns education, economic development, and workforce services. It operates with a regional focus but integrates services statewide, ensuring access to career counseling, job training, and placement services. The MyFutureNC initiative is a statewide goal to align the workforce with educational outcomes, targeting postsecondary attainment to ensure that residents are prepared for high-quality jobs.
• Ohio: The OhioMeansJobs system connects employers and job seekers statewide and offers workforce services through local centers. The TechCred program which helps businesses train employees in technology-related skills, emphasizing upskilling and industry certification.
• Texas: Texas Workforce Commission (TWC) partners with community colleges, technical schools, and local workforce development boards to provide training aligned with the needs of industries like energy, healthcare, and technology. TWC emphasizes dual credit programs in high schools, where students can earn college credits and credentials, as well as strong employer partnerships.
• Washington: Washington’s Workforce Training and Education Coordinating Board brings together various stakeholders to develop statewide strategies and programs. The Career Connect Washington initiative links K-12 education, technical training, and employers to help students move into high-demand careers.

A diagram that shows the relationships among ideas to help users understand how ideas are connected. Concept maps are generally composed of two elements: concepts (usually represented by circles, ovals, or boxes and are called nodes); and relationships (usually represented by arrows that connect the concepts; the arrows often include a connecting word or verb and these arrows are called cross-links.  There are four types of common concept maps: (1) spider maps, (2) flowcharts, (2) hierarchy maps, and (3) system maps.

See Relational Mapping (used at Learn & Work Ecosystem Library)

Concurrent or dual enrollment means taking college courses while still in high school. Dual-enrollment courses are taught by college-approved high school teachers in a secondary education environment. Students earn transcripted college credit when they pass the course, based on multiple and varied assessments throughout the course.

The Connecting Credentials Framework is a conceptual framework designed to make the decentralized U.S. credentialing system more coherent and transparent (the U.S. does not have a National Qualifications Framework as many other nations do). The CCF provides a common language and set of reference points to describe the levels of knowledge and skills associated with various credentials—from high school diplomas and industry certifications to college degrees and badges.

The CCF uses a competency-based, outcomes-focused approach, describing eight levels of achievement based on the complexity and scope of knowledge, skills, and abilities demonstrated by learners. It is non-regulatory and voluntary, intended to help stakeholders—including employers, educators, credential providers, and learners—understand and compare the value and rigor of different types of credentials.

The CCF was released in 2015. It was developed by Lumina Foundation, in collaboration with over 80 organizations, as part of the broader Connecting Credentials initiative. It was developed to:

• Address confusion and fragmentation in the U.S. credentialing landscape.
• Align educational credentials with the needs of the labor market.
• Support transparency, transferability, and comparability of credentials.
• Enable learners to stack and navigate different types of credentials (in addition to college degrees) across education and work pathways.
• Provide a framework that could inform the development of digital credentials, Learning and Employment Records (LERs), and other innovations in verifying learning outcomes.

While not a federally mandated system, the CCF has been influential in several national and regional efforts:

• Credential Engine incorporated elements of the CCF in its Credential Transparency Description Language (CTDL) and Credential Registry, using it to help describe credential levels and competencies.
• Some workforce and education providers have used it to map credentials to competency levels and identify gaps or overlaps in credential offerings.
• Some state higher education systems and employers have aligned their internal frameworks to the CCF for clarity on credential value and progression.
• It influenced the design of other frameworks and tools related to skills-based hiring, pathways, and stackable credentials.

See: Qualifications Frameworks (QFs) | Learn & Work Ecosystem Library

As defined by the Velocity Network Foundation, protocol used to achieve agreement on a single data value among distributed processes or systems.

Higher education institutions require revenues to operate and these typically come from sources such as student enrollments (tuition/fees), grants, subsidies, and government support. In the face of inconsistent revenue sources, increasing financial pressures, and enrollment declines, some higher education institutions turn to mergers and acquisitions (M&A) to prevent school closure.  M & A arrangements can keep campus doors open, preventing calamitous academic and financial situations for students —who without the option of their campus remaining open, trap many with student debt, nowhere to transfer their college credits, and no pathway to a credential.

Mergers and acquisitions include consolidations and integrations. These are situations in which public regional higher education institutions within a state system of higher education (e.g., Georgia, Texas, Wisconsin, Pennsylvania) are consolidated to be a branch campus or outreach center of another public institution in the system, or to become a newly integrated university.

Depending on the size and goals of the institutions involved, these processes may be as simple as a larger university absorbing a smaller college. These arrangements may require many stages of negotiations. Typical stages (can occur over months to years) include:

• Planning by administration and governing boards around risk-benefit analysis, a consolidation timeline, creation of needed communication channels and guidelines for stakeholders.
• Selection by governing boards of personnel to lead the merger process, clarify the vision for the merger, and outline required resources.
• Crafting the new governance plan with multiple regulatory bodies; e.g., governing boards of the merging colleges, accreditors, and representatives from the government including the U.S. Department of Education and sometimes state government.
• Implementing the plan to include identifying the name and brand, the oversight and administrative services, and the new college or university’s leadership and governance structure.
• Once operational, the new institution addresses opportunities and issues related to the new identity and culture, and often develops needed new services and programs.

When the Pennsylvania State Systen of Higher Education (PASSHE) conducted its university integration effort through structural change (constructing co-equal partnerships), it moved from 14 universities in 2018 to 10 in 2022. There were four non-negotiable guides:

• Maintain the partner universities’ distinctive identities and brands (e.g.,  logos, colors, name parts) in order to retain the partner universities’ history, regional cultures and economies, and dedicated employees and alumni.
• Maintain sports teams and related organizations (athletes, band members, fans, students in associated educational programs such as sports management)to maintain the universities’ identities which has proven to be critical to preserving student enrollments,  improved student outcomes, and donor funding.
• Minimize reliance on fully online education since PASSHE universities attract undergraduates seeking residential higher education’s academic and co-curricular advantages
• Maintain viable instructional activity at each campus, recognizing that closure was not an option politically and there were potential impacts on local communities where campuses were lead employers (often the only four-year educational option for place-bound students not interested in fully online education).

Enabling state statute was required to authorize the PASSHE Board to change its corporate structure (though not to close universities). The Board established parameters for the planning process:

• Defined an integrated university as one bringing two or more independently accredited institutions together into a new accredited entity with a single leadership team, faculty, staff, academic program, enrollment management strategy, budget, and a reporting relationship through the Chancellor to the Board.
• Required the process to be rigorously analytical, data-driven, and time-limited in recognition of the system’s financial exigency and applicable industry averages. Anty resulting new integrated university would enroll its first cohort of students in Fall 2022.
• Established high-level design principles requiring plans to improve student outcomes and ensure sustainable university operations.
• Focus initial attention on three regionally proximate candidate combinations.

See: Chain Buying among Higher Education Institutions

The intentional design and management of the linguistic, informational, and interactional context in which an AI system operates in order to influence its outputs over time. Unlike prompt engineering, which focuses on individual prompts, context engineering shapes the broader conditions that guide model behavior—such as persistent instructions, accumulated examples, role continuity, reference materials, and conversational history—leveraging how language models extend patterns across context windows.

Context engineering reflects a shift from controlling AI through phrasing to guiding it through structured environments, making it especially relevant for learning systems, knowledge platforms, and workplace AI tools.

See: Prompt Engineering | Learn & Work Ecosystem Library

Context graphs are structured records of how decisions are made within an organization. They capture the steps taken, people involved, information used, and reasons behind a decision, allowing others to see not just the outcome, but how and why it happened. They function like a connected record of decision activity, linking actions, communications, and approvals into a traceable history that can be reviewed and learned from. Context graphs represent a shift from tracking what happened to understanding how and why decisions happen—an increasingly important capability in complex education and workforce systems.

In practice, context graphs are used to reconstruct, understand, and improve decisions, especially in situations where judgment and exceptions are common. Common uses include:

• Explaining past decisions (e.g., why an exception was approved, why a student received a waiver)
• Improving consistency (helping staff make similar decisions in similar situations)
• Training and onboarding (showing new staff how decisions are typically made)
• Supporting artificial intelligence tools (providing examples of how people apply rules and make judgment calls)
• Identifying patterns and improving processes (revealing bottlenecks, workarounds, or inconsistent practices)

Context graphs are usually not a single visible tool or document. Instead, they are built by connecting information that already exists across systems. They are most often found:

• Within internal systems, such as workflow or ticketing systems, advising or case management platforms, customer or student record systems, and collaboration tools (messages, comments, notes)
• As part of emerging enterprise tools, especially those using artificial intelligence to analyze workflows and decisions
• Behind the scenes, assembled by data, technology, or analytics teams rather than presented directly to end users

In many organizations, context graphs are still developing and may not yet be formally labeled as such.

Context graphs are typically internal and often confidential because they include:

• Decision rationale and internal discussions
• Personnel involvement
• Sensitive student, employee, or customer information
• Exceptions to standard policies or practices

Access is usually restricted based on role and governed by data privacy and compliance policies. They are rarely shared publicly.

While context graphs support multiple roles, they are most commonly used by:

• Managers and leaders to review decisions, ensure consistency, and guide policy or strategy
• Operational staff and practitioners to understand how similar decisions have been handled
• Data, technology, and analytics teams to build systems that analyze or support decision-making
• Organizations implementing artificial intelligence tools to improve how systems interpret and replicate human judgment

Over time, their use may expand as tools make this information easier to access and apply.

According to iCIMS (provider of talent acquisition software), a contingent workforce, or contracted employees, is a workforce that does not have an ongoing contract with a company. The types of contingent workers include:

• Contractors
• Seasonal workers
• Freelancers
• Consultants
• Interns

Refers to learning activities undertaken after formal schooling, typically by adults, to maintain, update, or expand knowledge and skills for personal or professional development. It can include courses, workshops, certifications, and other formal or informal learning opportunities.

Refers to the practice of bringing workers back to traditional offices from remote or hybrid work arrangements, so that managers can better signal their dominance in the corporate pecking order and can showcase their status and contributions better when employees are working onsite. The concept is founded in the world of peacocking, where male peacocks display their vibrant plumage to curry attention and attract mates.

There are some trends indicating that the days of corporate peacocking may end in the future because:

• Office leases, especially in downtown cities, will expire and convert to apartments.
• Industries will adapt to emerging technology and geopolitics.
• Self-organized teams will need fewer show-offs to demonstrate their value (value can be displayed digitally).

According to iCIMS (provider of talent acquisition software), cost per hire is a key recruiting metric that calculates how much a company spends to fill an open position. It helps talent acquisition leaders answer important questions, like “Are we spending our recruiting budget wisely?” and “How could our hiring processes be refined?”

Refers to the systematic, nationwide research process of identifying, categorizing, and quantifying all documented credentials offered in the United States. Led by Credential Engine, this process produces periodic reports—most recently Counting Credentials 2025—that provide an authoritative estimate of the total number of unique credentials across the education and workforce ecosystem. The count includes a broad range of credential types, such as degrees, certificates, badges, micro-credentials, certifications, licenses, and secondary school credentials.

Key features of the report include:

• Uses standardized definitions and methods to account for seven major credential categories.
• Combines data from thousands of providers, digital credential platforms, education institutions, and licensing/certification bodies.
• Highlights change over time, such as growth in badges, certificates, and micro-credentials.
• Emphasizes the growing role of digital and data-rich credentials in making skills visible and verifiable.

Counting Credentials helps policymakers, researchers, employers, learners, and educators understand the size, diversity, and growth of the U.S. credential landscape. By revealing how many credentials exist, who issues them, and how they are distributed across categories, the process supports transparency, improves market intelligence, and informs decisions related to funding, program development, skills-based hiring, and quality assurance.

Related Terms: Credential Transparency, Credential Registry, Digital Credential, Credential Transparency Description Language (CTDL)

Course articulation is the process of comparing the content of courses that are transferred between postsecondary institutions; i.e., . In course articulation, one institution matches its courses or requirements to coursework completed at another institution. Course articulation is distinct from the process of acceptance by one institution of earned credit from another institution, as applicable towards its degree requirements in transferring credit.

A term used in the learn-and-work ecosystem to describe a lifelong approach to education and workforce development that spans from early childhood through older adulthood. Unlike the traditional phrase “cradle to grave,” this framing emphasizes the increasing importance of ongoing learning, reskilling, and employment opportunities into later life as people live and work longer. In practice, “cradle to grey” calls for integrated policies, pathways, and programs that ensure equitable access to education, training, and career navigation across all stages of life.

Examples in practice include:

• Age-Friendly Universities (AFU): Institutions that commit to expanding access, curricula, and services for learners across the lifespan, including older adults.
• Lifelong learning initiatives: Governmental efforts that fund reskilling programs for mid-career and late-career workers.
• Employer programs for older workers: Workplace models that encourage continuous upskilling and create intergenerational teams, recognizing the value of older adults in the workforce.

A credential is a documented award by a responsible and authorized body that attests that an individual has achieved specific learning outcomes or attained a defined level of knowledge or skill relative to a given standard. Credential is often viewed as an umbrella term that includes degrees, diplomas, licenses, certificates, badges, and professional and industry certifications. Some do not include degrees within the term, credentials, creating confusion as to whether degrees are credentials.

As defined by the Velocity Network Foundation, a piece of technology, like a gateway, that serves as the interface to the distributed ledger. In the case of Velocity Network Foundation, the CA is a free/open piece of software supplied for access; while the CA is not required to interface with the Velocity Network distributed ledger, the free tool is recommended to minimize time and expense for any entity wishing to anchor verifiable credentials to the Network—the CA removes the need for organizations to build their own network-compliant Agents.

As defined by the Velocity Network Foundation, an organization operating and maintaining a Credential Agent. An organization may operate the Credential Agent on their own behalf to issue credentials from their own data stores; or, the organization/CAO may operate the Credential Agent to help their Clients issue credentials. In the latter scenario, the Credential Agent Operator processes the credential data, whereas the Client organization plays the Issuer role on the Network. A CAO may service multiple Clients with the same Credential Agent.

An initiative working toward a nationally recognized transferrable incremental credentialing system that increases the number of high-quality, post-high school credentials that lead to further education and employment. The system captures and verifies learning that is currently uncounted, enabling individuals to be recognized for what they know and can do as they acquire it; provides pathways for learners to continue their education, increasing their ability to gain higher credentials and better employment.

Credential databases are digital repositories that store, organize, and provide access to records of academic, professional, or skills-based credentials. These systems may contain degrees, certificates, microcredentials, licenses, badges, or verified learning records.

Credential databases support credential verification, portability, and interoperability across educational institutions, employers, and digital wallet systems. They may operate at institutional, state, national, or international levels and increasingly connect with Learning and Employment Record (LER) systems and digital credential standards.

As defined by the Velocity Network Foundation, an individual who owns and controls their verifiable credentials.

As defined by the Velocity Network Foundation, an entity that creates and issues verifiable credentials to individuals, such as employers, academic institutions, and certification agencies. They can sometimes be referred to as “attestors” and participate in a network through data processors or specialized software vendors.

Credential Management System (CMS) is a broad term that refers to the software used for issuing and managing credentials. Governments and other enterprises employ CMS software to issue and manage credentials using an array of devices, including laptop computers, smart cards, smartphones, and USB keys. (Wikipedia)

In the higher education and third-party credentialing arena, the term is commonly used to refer to integrated Credential Management Systems (Credential As You Go, Playbook on Technology-Integrated Credential Management). Integrated CMS use a comprehensive solution for managing a variety of credentials. The system streamlines the entire life cycle of credentialing—from a credential’s proposal and development to its issuance and verification. CMS operations typically include: conducting academic program reviews and documenting approvals; creating catalogs and marketing materials; processing learner applications; managing scheduling and enrollment; handling finances/billing; tracking individual learners’ progress; providing counseling/advising; conducting audits; issuing credentials; managing learner transcripts; facilitating graduation communications; and generating internal and external reports.

To support these operations, many credential providers rely on a variety of IT systems and applications. By leveraging systems effectively and ensuring their seamless integration, entities can streamline credential management processes, enhance data accuracy, and improve overall operational efficiency. The most commonly used systems and applications include:

• Student Information Systems (SIS): Platforms used to store and manage learner data, including enrollment, academic records, and personal information.
• Learning Management Systems (LMS): Platforms that facilitate online learning and course management, enabling institutions to deliver educational content, track learner progress, and assess performance.
• Customer Relationship Management (CRM) Systems: Systems that help credential providers manage interactions with current and prospective learners and other stakeholders, including tracking communication, managing inquiries, and supporting enrollment processes.
• Document Management Systems: Systems that provide a centralized repository for storing and organizing credential-related documents such as transcripts, certificates, and other supporting materials.
• Financial Management Systems: Systems that handle financial transactions related to credential offerings, including billing, payment processing, and financial reporting.
• Human Resources Management Systems (HRMS): Platforms that support employee management, including credential-related roles such as faculty and administrative staff.
• Reporting and Analytics Tools: Tools that enable credential providers to generate insights from credential data, track performance, and make informed decisions.
• Collaboration and Content Management System (CMS) tools: Tools that facilitate communication among stakeholders involved in the credentialing process, including websites, resources, email, messaging platforms, and project management software.

The Credential Registry (Registry), operated by Credential Engine, is a public, cloud-based system available to anyone seeking information about a variety of credentials and skills in an easily-accessible format. Users can explore competencies, learning outcomes, up-to-date market values, and career pathways and reference data on credential attainment and quality assurance at schools, professional associations, certification organizations, and the military, to name a few. The Registry updates when a credential is no longer offered or an institution offering that credential closes, but the historical data still remains in the Registry.

Credential Engine developed the Credential Transparency Description Language (CTDL) to ensure that data related to credentials and skills speak a common language. The CTDL is a schema (a type of mini-language that people and systems can use to understand each other even if their data comes from different sources and that anyone can use to share information about credentialing data. The CTDL provides a common and unified way of describing information in the Credential Registry, and also an open language that can be used on the Web.

As defined by the Velocity Network Foundation, an entity that validates the authenticity of a credential presented by a Credential Holder.

Refers to the policies and practices conducted by a higher education institution when a student transfers to it from another institution. The receiving institution typically evaluates the student’s transcript and decides which courses will transfer and apply to the student’s degree or certificate completion, which courses will transfer as elective credits, and which courses will not transfer. These processes are carried out manually by many institutions and can create financial disincentives for awarding degree-applicable credit given the complexity of some transfer situations. The field is moving toward sharing digital credentials to speed up the process and reduce costs for institutions, however, progress is moving slowly.

Prior learning assessment (aka recognition of prior learning) is a term used for various methods of valuing college-level learning that has taken place outside of formal educational institutions, that can be assessed to count toward degrees or other credentials. Common assessment methods: (1) Standardized examination such as students earning credit by successfully completing exams such as Advanced Placement (AP), College Level Examination Program (CLEP), International Baccalaureate (IB), Excelsior exams (UExcel), DANTES Subject Standardized Tests (DSST).(2) Faculty-developed challenge exam in which students take a comprehensive examination developed by campus faculty. (3) Portfolio-based and other individualized assessment in which students prepare a portfolio or demonstration of their learning from a variety of experiences and non-credit activities and faculty evaluate the portfolio and award credit as appropriate. (4) Evaluation of non-college programs in which students earn credit based on recommendations provided by the National College Credit Recommendation Service (NCCRS) and the American Council on Education (ACE) that conduct evaluations of training offered by employers or the military. Institutions also conduct their own review of programs, including coordinating with workforce development agencies and other training providers to develop crosswalks that map between external training/credentials and existing degree programs.

Refers to the ability of learners to receive and carry with them verified college-level credit from a variety of high-quality learning experiences. These often include credit from dual (concurrent) enrollment, work-based learning, military experience, corporate training, digital badging, and prior learning assessment (recognition of prior learning).

Research has identified three necessary supports to enable state systems of higher education and higher education institutions to serve mobile students’ transfer needs well (and achieve holistic credit mobility):

• State Policy:  State policy has been slow to attend to the financial disincentives higher education institutions face in the credit assessment and acceptance process.
• Responsive Institutional and Cross-Institutional Practices: Students face challenges from institutional or cross-institutional level practices that offer limited support to students who have withdrawn or stopped out, insufficient or ineffective student advising to encourage exploration of ways to gain credits outside of the classroom, and unclear pathways for moving between institutions.
• Supportive Technology: There is inconsistent or insufficient technological development to support reconciling learning from multiple sources, and the technologies that do exist are limited and fragmented.

Credit pathways are ways for learners to earn reputable or transferable credits for proven skills or work completed. Credit pathways include but are not limited to: credit/course articulation, credit for prior learning, and noncredit-to-credit bridges. Course articulation is the process of comparing the content of courses that are transferred between postsecondary institutions – one institution matches its courses or requirements to coursework completed at another institution. Noncredit education includes any course or program that did not go through the process to be approved for-credit at a community college or university. Many higher education institutions develop noncredit-to-credit bridge pathways to enable learners to earn credit for learning acquired through noncredit courses and programs.

A commercial, membership-based interactive digital tool (platform) designed to help higher education institutions manage the life cycle of Credit for Prior Learning (CPL). The tool captures student learning narratives and skill extraction to CPL matching, advisor workflows, and administrative dashboards at the institution. Developed by the Council for Adult and Experiential Learning (CAEL) to help streamline CPL processes, the tool is used by a range of stakeholders:

• Students/Prospective Students: Start the self-guided experience by completing experiential-learning profiles that include certifications, military experience, workplace skills, etc.
• Advisors: Review student submissions, create or edit CPL matches, and use dashboards to manage cases or reassign them to faculty reviewers. Workflow guidance and status updates are integrated into the system.
• Faculty Reviewers: Assess credentials routed to them, determine whether students qualify for credit, may request additional documentation, or recommend exams or portfolios.
• Administrators: Receive institutional dashboards for monitoring overall CPL program usage, advisor-student interactions, and key performance indicator (KPI) outcomes.  API access (protocols that allow different software applications to communicate and interact with one another) enables integration with other campus systems such as Student Information Systems (SIS) and Customer Relationship Management (CRM).

The tool is used at a range of institutions such as Wilmington University, Tiffin University, Thomas Edison State University, and Davenport University.

Various credit transfer options are available to help individuals receive credit for what they know. Credit by exam is a process that allows students to earn college credit typically by taking subject-specific tests.

• At the K-12 level, credit by exam (CBE) is a method for individuals to demonstrate proficiency in grade level or course content (often at the K-12 level). Some state policies (for example, the Texas Education Code -TEC- §28.023) allow students to accelerate a grade level or to earn credit for a course based on credit by examination. Credit-by-examination assessments (CBEs) are typically approved by a local school district board of trustees for their district. CBEs are administered by the district or test provider in accordance with local district policy. Scores are reported directly to the school district and/or student.
• At the higher education level, some higher education institutions offer their own challenge exams. These exams are known by terms such as institutional exams, credit by exam, departmental exams, or proficiency exams. The assessment process provides academic departments the flexibility to tailor exams to fit specific course curricula, give program faculty confidence the exams reflect an appropriate level of academic rigor, and provides faculty direct control of the assessment process.
• An intermediary, the College-Level Examination Program® (CLEP) helps individuals receive college credit for what they already know. CLEP offers 34 exams that cover introductory level college course material. Exams are available in History and Social Science; Composition and Literature; Science and Mathematics; Business; and World Languages. Exams cost around $100 (exam plus test center’s administration fee). This amount is commonly used to compare to the cost of a college course. For military service members, the exam cost may be free. More than 2,900 U.S. colleges and universities award credit for CLEP. A college’s CLEP credit policy explains which CLEP exams are accepted by the institution, what CLEP score is needed to receive college credit, and how many credits are awarded for a particular CLEP exam. The policy may also include other guidelines, such as the maximum number of credits a student can earn through CLEP. With a passing score on an CLEP exam, an individual could earn 3 or more college credits at many U.S. colleges and universities.
• Some third-party organizations offer courses of study that can result in individuals receiving credit transfer from a higher education institution when they successfully complete the course. Courses are evaluated by intermediaries such as the American Council on Education or National College Credit Recommendation Service (NCCRS). Over 1,500 accredited institutions in the U.S. accept credit earned by passing a course that is pre-approved (recommended) by ACE or NCCRS. The courses are evaluated for quality by a team of university faculty and since each course has been successfully evaluated, they can be recommended for credit transfer.
  • The American Council on Education (ACE) evaluates nontraditional forms of learning for transfer credit recommendation. Thousands of colleges and universities in the U.S. use ACE recommendations to determine transfer course equivalencies to their institutions.
  • The National College Credit Recommendation Service (NCCRS) evaluates non-collegiate learning experiences for possible college credit recommendations. Their mission is to provide access to a variety of educational opportunities to nontraditional students and adult learners by recognizing learning that occurs outside of the college classroom. NCCRS evaluated courses undergo extensive review to ensure academic rigor and high instructional quality. College professors (serving as subject matter experts) determine if the courses are equivalent to similar college level courses and if credit recommendations are warranted.

As defined by the Data Quality Campaign in Data 101, cross-agency data governance is a formal, leadership-level body that is responsible and accountable for making decisions about how data linked between state agencies is connected, secured, accessed, and used to meet state education and workforce goals. Typical features of these entities:

• They define clear purposes, roles, and responsibilities for participating agencies in the statewide longitudinal data system (SLDS) and ensure accountability for data quality, privacy, and security.
• The entity is codified into state law to ensure the right membership and sustainability over time,
• Best-practice includes senior leaders from each of the agencies that contribute data to the system plus stakeholders that represent state education and workforce priorities (e.g., business leaders, district leaders, community organizations).
• State leaders set a vision for data use and create accountability for making decisions about data.
• Creates sustainability for the SLDS, especially when codified by legislation, by ensuring that decision-making authority is clear and responsibilities for data collection, privacy and security, and access are defined.
• Builds trust by creating a space for cross-agency collaboration, facilitating a shared data culture, and ensuring processes and decisions are transparent.
• Creates forums for communication and decision-making that are open to and include input from the public as well as local government.

Refers to the direct evaluation of learning and credentials acquired in registered apprenticeship programs, industry-recognized credentials and assessments, and non-military training in the assessment of prior learning for college credit. The assessment involves subject matter experts examining the training directly (often in advance for commonly used training and credentials) and determining whether the outcomes of the training itself match the outcomes of a course (to create a “crosswalk” or “mapping”).  Learners who submit the proper documentation for the recognized training do not typically need to complete additional assessments in order to receive college credit; if insufficient documentation is provided, learners may be recommended to complete other forms of assessment in a credit for prior learning process.

Refers to the practice of securing financial support for scientific or scholarly research by soliciting small contributions from a large number of individual donors, typically through online fundraising platforms such as GoFundMe, Experiment.com, or Kickstarter. This alternative funding method may be used when traditional sources—such as federal grants or institutional support—are unavailable, delayed, or politically restricted. Fields such as environmental science, reproductive health, public health, and social sciences have recently experienced particular growth in this practice.

Crowdfunded research is viewed by many as allowing for greater public engagement and democratization of research agendas, although it also raises concerns about sustainability, equity, and peer review standards. Commonly referred to as crowdfunded science or citizen-funded research, this model can enable early-career scholars, educators, and under-resourced institutions to fund projects that may not yet be competitive in traditional grant systems or that address timely issues outside mainstream funding priorities.

Examples of fundraising platforms:

• GoFundMe – for general research needs and emergencies.
• Experiment.com – a science-focused platform tailored for research projects.
• Kickstarter – occasionally used for public-facing science products or media projects.
• Patreon – supports ongoing research or science communication through recurring donations.

Related terms include alternative funding, public science, scientific crowdfunding, research democratization.

As defined by the Velocity Network Foundation, these are used to secure and verify the authenticity of credentials. Cryptographic keys are used to sign and verify credentials. Public and private keys are part of the underlying infrastructure that ensures the security, trust, and privacy of data exchanged on a blockchain network; these play a role in ensuring that only the rightful owner can present or alter their credentials.

Refers to a globally unique identifier associated with a specific credential or credential-related resource.

Refers to the selection and hiring of employees who will bring diverse perspectives and specialized skills to an employer, often filling gaps in existing staff competencies and creating opportunities for growth into new markets. “Culture add” is seen as an improvement on the concept of “culture fit” which can stifle innovation and lock employers into a cycle of hiring only like-minded individuals.

Related term: Culture Fit

A term originating in human resources that refers to an employee’s compatibility with the culture, goals, mission, and values of an employer, and their ability to quickly assimilate into a cohesive team. The concept of “culture add” has emerged as a preferred term and an improvement on the idea of “culture fit,” encouraging companies to seek new and different perspectives.

Related term: Culture Add

A program in the United States that allows international students on F-1 visas to work off-campus while completing their studies. CPT is designed for students whose academic programs require an internship, practicum, or other experiential learning opportunities as part of their curriculum. CPT allows students to gain practical work experience that is directly related to their field of study.

Regulations for CPT:

• Required Component: CPT must be a necessary part of the student’s degree program— it is not additional training but an integral component of the curriculum.
• Internships and Experiential Learning: CPT typically involves internships, co-ops (co-operative education), practicums, or other experiential learning opportunities.
• Off-Campus Work: CPT allows students to work off-campus while maintaining their student status.
• Academic Credit: Students may earn academic credit for their CPT experience, depending on their university’s policies.
• Authorization: CPT requires authorization from the student’s Designated School Official (DSO) and the institution’s International Student Office.
• Job Offer Required: Students must have a job offer before applying for CPT authorization.
• Fulltime Enrollment: Students must maintain fulltime enrollment at the university while on CPT.
• Ineligibility for OPT: Students who have worked more than 12 months of fulltime CPT are generally ineligible for the program, Optional Practical Training (OPT).

 See: F-1 Visa & Optional Practical Training (OPT) | Learn & Work Ecosystem Library

A guide or framework that helps educators or credentialing entities to:

• Identify the skills (competencies, capabilities) embedded in a curriculum.
• Design badges (or microcredentials) that map to those skills.
• Define criteria / evidence for badge earning.
• Link those badges to curriculum units, learning outcomes, assessments.
• Communicate the badge’s meaning (metadata, descriptors) so that employers or other institutions understand what the badge signifies.

Such guides can be useful in assuring badges are not arbitrary “stickers” but are meaningfully aligned with curriculum, pedagogy, learning outcomes, and labor-market relevance.

According to iCIMS (provider of talent acquisition software), a custom career site is a proprietary website that lists your job openings in an intuitive and appealing way, encouraging visiting candidates to explore your brand and apply for available positions. Unlike third-party job-posting sites, career websites are owned by the company that is hiring, or by a recruiter hiring on behalf of another company. This gives them complete control over the design of the website, the messaging, the language, and the hiring process.

According to EDUCAUSE, the distributed computer, information, and communication technologies combined with the personnel and integrating components that provide a long-term platform to empower the modern scientific research endeavor. Components of CI include high-performance computing, storage resources, visualization facilities, sensors and other data collection apparatus, and advanced networks.

Refers to a policy implemented by the U.S. government in 2012 under the Obama administration. DACA provides temporary relief from deportation and enables work authorization to certain undocumented individuals who were brought to the United States as children (often referred to as “Dreamers”).  To be eligible for DACA, applicants must meet various criteria, including having arrived in the U.S. before turning 16 years of age, continuously residing in the U.S. since June 15, 2007, and meeting educational or military service requirements. DACA recipients are granted renewable 2-year periods of deferred action that allows them to remain in the U.S. legally and obtain work permits, although they are not provided a path to citizenship.

As defined by IBM, a data center is a physical room, building or facility that houses IT infrastructure for building, running and delivering applications and services. It also stores and manages the data associated with those applications and services.

Historically, data centers were privately owned, tightly controlled on-premises facilities housing traditional IT infrastructure for the exclusive use of one company. Recently, they have evolved into remote facilities or networks of facilities owned by cloud service providers (CSPs). These CSP data centers house virtualized IT infrastructure for the shared use of multiple companies and customers.

Managed data centers and colocation facilities are options for organizations that lack the space, staff or expertise to manage their IT infrastructure on-premises, and used by those who prefer not to host their infrastructure by using the shared resources of a public cloud data center. Companies often choose managed data centers and colocation facilities to house remote data backup and disaster recovery (DR) technology for small and mid-sized businesses (SMBs).

• In a managed data center, the client organization leases dedicated servers, storage and networking hardware from the provider. The provider handles the client’s administration, monitoring and management.
• In a colocation facility, the client owns all the infrastructure and leases a dedicated space to host it within the facility. In the traditional colocation model, the client organization has sole access to the hardware and full responsibility for managing it. This model is ideal for privacy and security but can be impractical, particularly during outages or emergencies. Today, most colocation providers offer management and monitoring services to clients who want them.

There are different types of data center facilities:

• Enterprise (on-premises) data centers— The user organization is responsible for all deployment, monitoring and management tasks. These centers offer users more control over information security and can more easily comply with regulations such as the European Union General Data Protection Regulation (GDPR)or the US Health Insurance Portability and Accountability Act (HIPAA).
• Public cloud data centers and hyperscale data centers—House IT infrastructure resources for shared use by multiple customers (from several to millions) through an internet connection. Many of the largest cloud data centers (known as hyperscale data centers) are run by major cloud service providers (CSPs), such as Amazon Web Services (AWS), Google Cloud Platform, IBM Cloud, and Microsoft Azure. These companies have major data centers in every region of the world. Hyperscale centers are larger than traditional data centers and can cover millions of square feet. They typically contain at least 5,000 servers and miles of connection equipment, and can be as large as 60,000 square feet.
• Edge data centers (EDCs) —Cloud service providers typically maintain smaller data centers that are located closer to cloud customers and their customers as well. These centers form the foundation for edge computing, a distributed computing framework that brings applications closer to end users. These centers are useful for real-time, data-intensive workloads like big data analytics, AI, machine learning, and content delivery.

A study from McKinsey & Company projects the industry to grow at 10% a year through 2030, with global spending on the construction of new facilities reaching USD49 billion.

According to the Data Quality Campaign’s Data 101, there are three key types of data common to education and workforce:

• Student Data (P-12)
  • Information such as attendance, grades, student growth, outcomes, enrollment.
  • Schools, school districts, and states collect student data and use it to make decisions about instruction, interventions, policy development, and resource allocation.
  • Most student data is stored at the school and district levels.
  • A limited amount of this data is reported to states.
  • States use and share this data in anonymous and aggregate forms.
• Postsecondary Data
  • Information such as admissions, enrollment, persistence, completion, courses or majors, use of support services or public benefits, financial aid, and student debt.
  • Information is collected to help better understand education options after high school, such as two- and four-year college programs, applied career training through community and technical colleges, professional certifications, and other noncredit training or education pursued after high school.
  • Many state, federal, and accrediting agencies require postsecondary institutions to report data for compliance purposes (e.g., federal financial aid eligibility or state authorization requirements).
  • Reporting requirements by governmental and other regulatory entities shape much of the data collected in the field.
  • Higher education institutions collect additional data on individual students and education programs. Data are typically used to evaluate programs and improve the tools and support offered to students. Such data is rarely collected at the state or federal levels.
• Workforce Data
  • Information relating to local or regional labor markets such as data about the existing workforce; wage information; use of social assistance programs; demand for occupations; growing fields and industry trends; and information on available training, skills, and credentialing resources.
  • Comes from a variety of sources, such as state unemployment records, tax data, public benefits programs, job posting sites, education institutions, job training programs, apprenticeship programs, the military, and adult education services.
  • The amount of data incorporated into statewide data systems is often limited. For example, in states where the data is incorporated, it may include only unemployment insurance data, which contains information from employers on their employees and their wages but has many gaps and limitations.
  • In many states, much of the data may be available only to private entities and employers.

Encryption is a security process to convert data into an unreadable code. The purpose is to ensure that sensitive data is protected from unauthorized access. Encryption happens when data is being transmitted or stored to keep it secure. The process uses complex mathematical algorithms, to convert readable data (plaintext) into unreadable data (ciphertext). Only those with the decryption key is able to reverse the encryption process to access the original data. Encryption is widely used in online transactions, communication, and data storage, to protect against unauthorized access and cyber threats.

Decryption is the reverse process. It converts encrypted data back to its readable form (plaintext) using the correct decryption key. Decryption takes place when the intended recipient receives the encrypted data and uses the proper key to restore the original message.

These two processes work together to safeguard data from unauthorized access and tampering.

According to Digital Promise, data governance refers to a data management process or framework whereby an organization or a collection of organizations ensure the collection, storage, and security of high-quality data.

Data literacy and data science skills are important for jobs in fields in business, engineering mathematics, statistics, computer science, life sciences, social sciences, digital humanities, and others. They are also important skills for navigating an increasingly data-driven world. These terms are useful especially to education and training programs established to develop learners’ skills, K-12 through postsecondary education.

Data literacy refers to foundational knowledge about and the ability to read, understand, and communicate data or claims derived from data. Literacy includes knowing how to communicate and question data and representations of data critically, including limitations and potential biases. Areas of knowledge include understanding probability and randomness, ways to visualize data, the meaning of descriptive statistics, the concept of statistical significance, the concept of mathematical techniques called hypothesis tests, how data is collected, and the concept of control groups.

Data science is an interdisciplinary field that refers to applying the processes of working with data. Applications can include calculating means and medians, formatting and graphing data including with multiple variables, performing a scientific study which includes establishing framing questions and crafting the methods of data collection, and measuring variation among the data collected.

A malicious type of cyberattack in which data used for analytics, machine learning, or artificial intelligence systems is intentionally manipulated, corrupted, or misleadingly labeled in order to distort system outputs, degrade performance, or introduce bias. Data poisoning can occur during data collection, curation, integration, or model retraining. It may be difficult to detect once embedded in complex data pipelines.

As governments and institutions increasingly rely on data-driven tools for planning and oversight, data poisoning has become a critical concern for data governance, transparency, and public accountability. In governmental, educational, and workforce policy contexts, data poisoning poses significant risks by undermining evidence-based decision-making, skewing labor market intelligence, misinforming funding or regulatory actions, and eroding trust in AI-supported public systems.

According to Digital Promise, data sharing systems are technological platforms where data collected by multiple entities can be shared across and within multiple stakeholders’ organizations.

A data silo is when data is locked away inside one department, one tool, or one platform, and cannot easily flow or connect with other systems. The effect is fragmentation (scattered, disconnected data), duplication of effort (retyping, copying), and limited ability to get holistic insight across systems. Examples in practice could look like:

• The math learning platform has its own student performance data, but you cannot export it in a useful format to your overall student information system (SIS) or learning analytics dashboard.
• The English assessment tool does not talk to the curriculum planner, so teachers have to re-enter or manually reconcile data.
• Each vendor uses its own data model or proprietary file format, so moving from one to another means rewriting or manually transforming everything.

Open standards are agreements (often technical specifications) about how data should be structured, labeled, exchanged, and understood — so that different systems can interoperate. Examples in practice could look like:

• The diagnostic assessment tool can export student results in a well-known format (e.g. CSV, JSON, or education-industry standard like IMS LTI, QTI, or Caliper) that other systems can read.
• When you change vendors, you can migrate your records with minimal friction because the data format is understood.
• Systems from different vendors can “plug in” to each other (e.g., assessments, gradebooks, dashboards, curriculum systems) because they share a “data language.”
• Analytics or reporting tools can aggregate data across different learning tools without custom connectors or massive rework.

A metaphor for these differences:

• In a data silo, each garden has its own high walls; you can’t see or reach over to the neighbor’s garden.
• In open standards, the gardens use a shared gate and paths so you can walk between them, bring produce together, and see the full patchwork.

Refers to having confidence that data meets quality standards and is ready to act on (e.g., analyze, understand, make decisions about). Data trust cannot be taken for granted; there should be evidence of the data’s quality standards.

The Data Management Association of the UK defines six dimensions of data quality required for trustworthy data:

1. Accuracy —degree to which data correctly describes the real-world object or event in question.
2. Completeness —proportion (percentage) of data stored versus 100% complete (e.g., are there blank values indicating certain data has not been populated).
3. Consistency —absence of difference when comparing two or more representations of an item against a definition (e.g., do dates and names match).
4. Timeliness — extent to which data is current enough to represent reality for realistic use.
5. Uniqueness — no item or entity instance is recorded more than once based upon how that item is identified (nonduplication).
6. Validity or conformity — extent to which data conforms to the syntax (format, type, range) of its definition.

There is important context behind data trust: (1) the multiplicity of data sources, and (2) potential errors in data management:

• Manual data quality management cannot handle immense volumes of data that come from multiple sources of data; e.g., from SaaS and web applications, direct data entry like web forms, unstructured sources like social media posts, machines such as smartphones and “Internet of Things” devices.
• Errors in data management are a factor in quality data. Errors are introduced by people who make mistakes, machines that are not foolproof, and errors can be introduced by data that often passes through complex information systems coded by multiple developers.

According to iCIMS (provider of talent acquisition software), embracing a data-driven recruitment process makes it possible to find and keep top talent at your company, especially when you incorporate the latest in recruitment metrics and analytics tools.

Moving beyond gut feelings and traditional relationship-building, this evidence-based approach delivers proven results for recruiting and HR teams striving to meet their business objectives—with less waiting, less hassle, and less missed opportunities.

As defined by the Velocity Network Foundation, refers to the distribution of control and decision-making from a central authority to a network of independent participants or nodes. In a decentralized system, no single entity has full control over the data or operations. Instead, power is shared across multiple actors, enhancing transparency, security, and resilience.

As defined by the Velocity Network Foundation, unique identifiers that enable verifiable, self-sovereign digital identities. They are created, owned, and controlled by the individual, and are used within a network to establish secure, tamper-proof credentials stored on the blockchain. DIDs are essential in ensuring privacy and security in the digital credentialing process.

Refers to a social web in which no single entity (or small group of entities) controls the Internet. The decentralized web is focused on principles of self-determination of users who have their own data and join the network seeking new practices around sovereignty, openness, protection of their data, and censorship-resistance. The decentralized Internet is also known as Web3 or the Dweb. Unlike the current Web (Web2) which relies on centralized servers, clouds, and platforms, Web3 embraces blockchain, peer-to-peer networks, and distributed storage. In Web3, ownership and control are distributed among users. This places ownership of personal data back into the hands of individuals rather than companies, which many contend take data from individuals and track both user’s data and data from user’s networks without permission.

The evolution of the internet and mobile technology make it possible for more workers to telecommute and contribute to a more decentralized workforce structure. In a decentralized workforce, employees may collaborate in a functional area or on a work team, but they do not necessarily work together in the same office.

Many believe that while the physical location of some work has changed, the origination of work is still centralized – but a future could be coming in which yet-untapped groups will achieve economic potential outside of traditional employment. These groups may be viewed by economists now as “inactive” in the workforce because their work is not recognized or monetized. If and when they are recognized or monetized, they could contribute to an evolving decentralized workforce. Examples of such groups include:

• An extended workforce joining the global economy from various nations. They may provide an untapped resource to boost the global economy. They would be enabled by freely available tools and technology such as mobile, video, social media, AI, and blockchain.
• Gig workers who work via a platform at some point to monetize their work. Examples: artists, on-call workers, contractors, seasonal workers, some consultants.
• Unpaid workers who support our collective well-being by working around the house, caring for relatives, shopping for necessary household goods, and childcare.
• YouTube influencers and individuals paid to play games in the digital games industry.
• Greater inclusion of the neurodiverse workforce (individuals on the autism spectrum). These individuals could use platforms and employment practices especially in the technology industry.
• Individuals who broadcast user-created videos on gaming platforms or who produce comics, photographs, and stories through various outlets.
• Individuals on unemployment.
• Former teachers, coaches, and stay-at-home parents who create courses and provide them for remuneration through online marketplaces.
• Individuals paid for their clicks and attention on browsers, receiving tokens which can be converted to cash.
• Artificial intelligence workers who complete tasks that AI cannot do yet for companies in the crowdsourcing marketplace.
• Expert consultants who can be hired for an hour or two or construct a new team.
• Digital shopkeepers who use various platforms (Shopify, Amazon, eBay, Etsy) to keep a digital shop open.

Deep Research is an advanced artificial intelligence (AI) tool developed by OpenAI that automates the process of searching, verifying, and synthesizing information from multiple web and scholarly sources. Deep Research is viewed as a new form of AI “research assistant” capable of multi-stage web and database searches, retrieval of primary sources, and generation of transparent, citation-rich reports. This tool is designed to assist scholars, journalists, and policy researchers who need traceable, source-based information. It differs from conversational AI systems such as ChatGPT, which rely primarily on pre-existing knowledge and single-query lookups.

According to IBM, some of the biggest markets for AI-driven research include pharmaceuticals, where companies are leveraging AI to identify new drug candidates; and venture capital firms and private equity investors using AI research tools to streamline due diligence processes (rather than spending weeks analyzing financial and market reports, financials and competitive landscapes, firms could use AI to generate analyses that highlight key risks and opportunities).

See Topic Brief: AI Tools for Searchers and Researchers: Conversational AI & Deep Research | Learn & Work Ecosystem Library

Refer to AI-generated (also known as synthetic or manipulated media) which is often video or audio, that convincingly mimic real people’s likenesses or voices, making them appear as if they are saying or doing things they never did. These digitally altered materials are often generated through advanced machine learning techniques, especially generative adversarial networks (GANs), which can create realistic yet fake and misleading content.

In higher education, deepfakes pose significant risks, including threats to privacy, reputation, and information security. The rise of deepfake technology has led to serious cybersecurity threats, as seen in an incident at the University of Utah. Here, phishing emails were used to acquire legitimate photos of students, which were later manipulated into threatening deepfake images for extortion purposes. Such attacks represent a new form of cybercrime, leveraging AI-driven digital manipulation to compromise personal identities and reputations and, in turn, exploit victims for financial gain.

To combat these sophisticated cyber threats, many higher education institutions are adopting a multi-layered approach to cybersecurity that combines education, technology, and strict security protocols, to help campuses manage and mitigate these threats. Examples of strategies:

• Awareness and Training: Educating students and faculty about recognizing phishing scams and verifying the authenticity of emails, particularly those requesting sensitive information. Many campuses are implementing mandatory cybersecurity training programs to foster vigilance.
• Digital Literacy Initiatives: Incorporating digital literacy into curricula to educate students on the risks of AI, data privacy, and safe online behavior, especially as AI tools become more accessible and students themselves may experiment with these technologies.
• Email and Network Security: Upgrading email filtering and monitoring systems to detect and block phishing attempts more effectively, especially those targeting students with requests for sensitive information.
• Enhanced Authentication and Detection Systems: Investing in and upgrading multifactor authentication (MFA) and using anomaly-detection algorithms that spot irregularities in digital communications; e.g., inconsistencies in face movements, unnatural speech patterns, or other technical markers. These tools use algorithms that can identify alterations in the pixelation or lighting of images, helping to screen out manipulated content. Machine learning models can also help identify signs of manipulation in images or videos. Some universities are using digital watermarking and blockchain-based authentication to verify legitimate content.
• Incident Response Plans and Forensic Analysis: Developing specific incident response strategies for deepfake and cyber-extortion cases, enabling quicker containment, investigation, and, where necessary, cooperation with law enforcement. When deepfake-based threats or attacks occur, universities employ forensic analysis to trace the source of the attack and collect evidence. These efforts aid in understanding the vulnerabilities exploited and strengthening defenses.
• Collaboration with Law Enforcement: Collaborating with local and federal law enforcement agencies, especially in cases involving extortion or blackmail. This collaboration aids in swift actions needed to protect students and staff and may serve as a deterrent to future attacks.

Refers to the postponement of necessary repairs and upkeep of college and university campus buildings, infrastructure, and systems due to budget constraints or competing institutional priorities. Over time, this backlog can lead to visibly decaying campuses, where aging facilities become outdated, unsafe, or unusable—posing risks to student learning, recruitment, and institutional reputation.

Many colleges and universities—especially those with declining enrollment or strained budgets—are able to only fund a small fraction of their facility repair needs, often covering less than 25% of estimated deferred maintenance each year. This leads to the accumulation of physical deterioration across campuses: leaking roofs, failing HVAC systems, outdated labs, inaccessible buildings, and even health or safety hazards.

A degree is a title given by an institution (usually a college or university) that has been granted the authority by a state, recognized Native American tribe, or the federal government to confer such degrees. Generally, degrees are provided for accomplishment in academic, vocationally related, or religious studies, and the degree requirements differ within each of these three realms but are presumed to be comparable in accomplishment. A degree is granted by an institution to individuals who are presumed or who have been attested to have satisfactorily completed a course of study from which the individual can demonstrate the knowledge, skills, and ability commensurate with the degree requirements within the specific field of study. Degrees vary in the level of knowledge and skills that holders of the degree are presumed to have.

A type of apprenticeship that combines working and studying toward a university degree. It allows individuals to gain practical work experience in a specific profession while also completing academic coursework that leads to a degree, typically in fields like engineering, healthcare, IT, and business. Degree apprenticeships are particularly prominent in the United Kingdom and some other European countries, where they integrate the workplace with university education, offering an alternative to traditional higher education.

Features include:

• Work-Based Learning: Apprentices work for an employer, applying what they learn in real-time, often in a structured program.
• Academic Study: Apprentices undertake academic study which could be at the level of a bachelor’s or master’s degree, often delivered by a university or higher education institution.
• Employer Sponsorship: Employers sponsor the apprenticeship (pay apprentice’s salary and often cover the costs of academic tuition).
• Paid Employment: Apprentices receive a salary while participating in the program.
• Qualification: Upon completion, apprentices earn both a degree and work experience.

Degree apprenticeships differ from other types of apprenticeships:

• Academic Focus: Traditional apprenticeships focus on learning practical skills on the job, while degree apprenticeships require the completion of higher education qualifications (degrees). In regular apprenticeships, participants may not pursue a formal degree.
• Level of Study: Traditional apprenticeships may lead to a certificate or a vocational qualification rather than university degree. Degree apprenticeships are typically tied to degree-level qualifications (such as Bachelor’s or Master’s degree).
• Duration: Degree apprenticeships generally take longer than nondegree apprenticeships because they require academic study in addition to the work experience. Degree apprenticeships tend to last 3-6 years compared to other apprenticeships (1-3 years).
• Costs: Employers and government often split the cost of degree apprenticeships. Traditional apprenticeships are usually less expensive and shorter-term for employers and participants.

Refers to the practice of employers using degree requirements to screen out otherwise qualified candidates in pursuit of hiring efficiencies.

Degree and diploma mills refer to institutions that offer educational credentials (also known as qualifications) which are not accredited and are not based on common standards of academic assessment. Though the terms are often used synonymously, there are differences between the terms.

Degree mills typically issue diplomas from unaccredited institutions which may be legal in some states but are generally illegitimate. Degree mills may claim to be accredited but they are accredited by fake or phony accreditation agencies.

Diploma mills issue counterfeit diplomas using the names of real colleges or universities. The term is also used to describe a legitimate educational institution that has low academic admission standards and a low job placement rate, such as some for-profit schools.

A third-party organization, GetEducated.com, tracks and maintains a free list of online colleges which do not have proper distance learning accreditation, or which have consumer fraud or scam warnings lodged against it. This service is available for consumer information and protection.

See: Fraudulent Credentials | Learn & Work Ecosystem Library (learnworkecosystemlibrary.com)

Document published in 2011 by Lumina Foundation that describes what degree recipients in the United States should know and be able to do at the associate, bachelor’s, and master’s degrees—regardless of a student’s field of specialization. The DQP does not attempt to “standardize” U.S. degrees—it recognizes the role and responsibility of faculty to determine both the content appropriate to different areas of study and the best ways to teach that content. The DQP describes generic forms of student performance appropriate for each degree level through reference points that indicate the incremental, integrative and cumulative nature of learning. The DQP focuses in five areas:

• Specialized/Industry Knowledge addresses what students in any specialization, major field of study, or career pathway should demonstrate with respect to that specialization.
• Broad and Integrative Knowledge asks students to bring together learning from industry knowledge, experience, and/or different fields of study to discover and explore the implications of concepts and questions that bridge essential areas of learning/practice as well as integrate their knowledge to advance solutions in support of a humane, just, and democratic society.
• Intellectual Skills include: analytic inquiry, use of information resources, engaging diverse perspectives, ethical reasoning, quantitative fluency and communicative fluency.
• Applied and Collaborative Learning emphasizes what students can do with what they know. Students are asked to demonstrate their learning by addressing unscripted problems in scholarly inquiry, at work and in other settings outside the classroom, individually and in teams.
• Civic/Democratic and Global Learning recognizes higher education’s responsibilities both to democracy and the global community. Students engage in integration of their knowledge and skills by addressing and responding to civic, social, environmental, economic, equity, inclusion, and social justice challenges at local, national, and global levels.

The DQP was updated in 2014, and again in 2021, with the assistance of the National Institute for Learning Outcomes Assessment (NILOA).

See Topic on Qualifications Frameworks

According to the Max Planck Institute for Demographic Research, “demographic change describes the changes in population size and structure caused by changes in birth rates, death rates, and by migration. Demographic change in the Western developed countries of today is marked by low birth rates below population replacement and by rising life expectancy. The result is that populations are aging and shrinking. And migration may overlap with these developments. Migration, for example, leads to further population reductions in the regions of origin and to attenuation in the regions of destination. And if it is the young rather than the old who migrate from a region, aging is exacerbated in the region of origin.”

Refers to an approach to learning, collaboration, and problem solving that uses a particular design process: a framework to identify challenges, gather information, generate potential solutions, refine ideas, and test solutions. The approach emphasizes empathy with users, collaboration, creativity, and iteration to generate innovative solutions. It originated from the field of design but is used now in many applications such as education, business, healthcare, and government. Design thinking is posited to better prepare individuals for the complexities of the 21st-century world since it promotes the mindset of experimentation, risk-taking, and continuous learning.

In the learn-and-work ecosystem, design thinking is used for:

• Curriculum Integration: Many educational institutions incorporate design thinking principles into their curriculum to cultivate students’ problem-solving skills, creativity, and empathy. Courses or workshops on design thinking are increasingly common across academic disciplines.
• Interdisciplinary Collaboration:  Fosters collaboration among students across many disciplines since design thinking values diverse perspectives and expertise.
• Project-Based Learning: Students working on real-world problems use this approach to apply knowledge in practical settings to foster critical thinking, innovation, and adaptability.
• Innovation Centers and Labs: Many universities operate innovation centers or incubators dedicated to fostering creativity, entrepreneurship, and innovation. Some are hubs for design thinking activities, providing resources, mentorship, and networking opportunities for students, faculty, and employer and government partners.
• Enhancing Employability Skills: Higher education institutions are integrating design thinking into their programs to better prepare students for the evolving demands of the job market.
• Addressing Complex Challenges: Design thinking equips students with the mindset and tools to address complex problems where conventional solutions do not work, such as in healthcare and social justice, where multifaceted challenges require innovative approaches.

A digital archive collects and preserves digital materials to make them accessible for long-term use. Techniques include the use of digital storage technologies, metadata for organization and searching, and preservation methods that ensure the integrity and usability of the materials over time.

There are two types of archives:

• Born-Digital Archives—Consist of materials that were created in digital format and never existed in a physical form; examples are emails, databases, websites, and digital publications.
• Digitized Archives— Consist of materials that were originally physical, such as books, photographs, films, or audio recordings but have been converted into digital formats.

The purpose of these archives is to ensure that valuable information, historical records, cultural heritage, and research data are preserved in a way that guarantees long-term access. This is achieved through the use of digital storage technologies, metadata for organization and searching, and preservation techniques that ensure the integrity and usability of the materials over time (Ashikuzzaman, 2024, Library & Information Science Education Network).

See Topic: Digital Decay, Internet Poisoning, and Digital Archiving | Learn & Work Ecosystem Library

A digital badge (aka e-badge) is a digital representations of individuals’ achievements, consisting of an image and metadata uniquely linked to the individual’s skills. Digital badges have an issuer (institution that testifies), an earner (learner), and a displayer (site that houses the badge) .Badges can be displayed, accessed, and verified online.

According to the AACRAO Higher Ed Glossary, an electronic representation of an earned skill or achievement. They can be embedded with metadata which make them verifiable, portable, and electronically sharable.

Digital credentials are similar to digital badges in the sense that they create opportunities for learners and workers to demonstrate qualifications, skill sets, claims, or achievements through digital certificates or documents. Digital credentials are verified and awarded through the digital credential ecosystem. An ecosystem or marketplace of schools, training programs, institutions, industries, employers, and career pathways allows for the issuing, awarding, and verification of these digital credentials and gives them validity.

Refers to the gradual loss, degradation, or inaccessibility of digital information over time. This can occur due to link rot (broken or outdated hyperlinks), obsolescence of digital formats or technologies, inadequate data preservation strategies, and the intentional or unintentional removal of content from the web. The phenomenon highlights the impermanence of online information and underscores the importance of digital archiving and preservation to ensure long-term accessibility and reliability of critical resources.

See: Internet Poisoning | Learn & Work Ecosystem Library

Refers to an online tool or system designed to measure learners’ strengths, weaknesses, and learning gaps—often before instruction or during learning. The “diagnostic” part means it aims to figure out where the student is now, not to grade for a final or summative evaluation. The “digital” part means it’s delivered via computer or an online system.

In practice, many digital diagnostic assessments end up constrained in various ways:

• They may force you into a platform where you cannot export or reuse your data, or move it to another system (known as vendor lock-in).
• They may support only a few kinds of question formats (e.g., multiple choice, true/false) rather than open-ended, project-based, or adaptive questions.
• They may hide or trap content (e.g., your questions, your item bank) inside that vendor’s system so you cannot take your own assessment materials with you.
• Over time, costs can escalate (e.g., subscription, licensing, per-student fees), especially if you are locked in and cannot shift to a cheaper or better alternative.

A digital diagnostic assessment in a “friendly” design would combine the benefits of computer delivery (scoring speed, adaptive branching, analytics) without imposing rigid constraints. It would allow portability, flexibility of question types, and control over your own content and data.

Refers to the gap between individuals, communities, or organizations that have access to reliable digital technologies—such as the internet, computers, and digital skills—and those who do not. This divide can be shaped by factors such as income, geography, education, infrastructure, and age. It influences people’s ability to fully participate in learning, work, and civic life, often reinforcing existing social and economic inequalities.

Refers to the overall quality and effectiveness of an employee’s interactions with the digital tools, platforms, and technologies they use in the workplace. This includes everything from onboarding software, productivity tools, communication platforms, and workflow systems to enterprise resource planning systems and AI assistants. DEX encompasses ease of use, accessibility, responsiveness, and how well digital systems support employees’ productivity, engagement, and well-being. The term is often understood as the sum of an employee’s perceptions of their digital environment.

See Topic Brief: Digital Employee Experience (DEX) | Learn & Work Ecosystem Library

The concept that every person should have equal access to digital technologies, including internet access. The concept aims to address the divide in access to digital infrastructure that gives some people advantages over others in education, work, and society.

As defined in the federal Digital Skills for Today’s Workforce Act, digital equity means the condition in which individuals and communities have the information technology capacity needed for full participation in the society and economy of the United States.

An example at an institution is Bowdoin College’s Digital Excellence Commitment (DExC) that provides every current student and their future students with a 13-inch MacBook Pro, iPad mini, and Apple Pencil plus course-specific software designed to advance learning, inspire innovative teaching, and create digital equity across the student body in the use of tools essential for success in the twenty-first century.

A secure mobile app that digitally stores and manages personal information and identity. These credentials can include IDs, passports, driver’s licenses, and other verifiable forms of identification. Unlike traditional identity systems that depend on centralized parties and external databases, digital ID wallets can store data directly on the user’s device, enabling individuals to determine what information to share, with whom, and under what conditions. This can ensure a higher level of privacy and security, as it minimizes data exposure and reduces reliance on third-party data storage.

Digital identity wallets eliminate the need for physical documents, streamlining identity verification and management. Governments worldwide are increasingly adopting digital ID solutions to enhance citizen interactions, improve service efficiency, and reduce fraud.

See Topic: Growth of Digital Tools: Digital Wallets, Skills Passports, and Digital ID Wallets | Learn & Work Ecosystem Library

Refers to a profile of individuals’ or entities’ online interactions and behaviors. The identity is a collection of data about an individual, organization, or electronic device that exists online. It includes unique identifiers and usage patterns used to recognize individuals or their devices across the digital ecosystem. They are often tied to identifiers such as usernames, passwords, or device IDs like Internet Protocol addresses. These identifiers are needed for authentication processes — to determine how individuals and devices are recognized and verified across the internet.

An array of data points may be used in constructing digital identifiers: username and password combinations, purchasing behaviors or transaction histories, birth date, social security number, online search activities and electronic transactions, and medical history. These data points form the detailed profile of individuals’ or entities’ online interactions and behaviors.

Digital identities are commonly used by website owners and advertisers to track users and tailor content delivery that aligns with users’ preferences and behaviors.

As defined by Wikipedia, refers to an online database of digital objects that can include text, still images, audio, video, digital documents, or other digital media formats or a library accessible through the internet. Digital libraries can vary widely in size and scope, and can be maintained by individuals or organizations. The digital content may be stored locally, or accessed remotely via computer networks. These information retrieval systems are often able to exchange information with each other through interoperability and sustainability.

Alternate names: Online library, internet library, digital repository, library without walls, digital collection

See Topic:  Digital Libraries in the Learn-and-Work Ecosystem | Learn & Work Ecosystem Library

The ability to use digital technologies, tools, and platforms to locate, evaluate, create, and communicate information. As described in the Digital Skills for Today’s Workforce Act, digital literacy includes using technology to complete tasks, developing digital citizenship, and responsibly navigating online environments. It encompasses both technical proficiency and the ethical use of digital systems.

See Glossary Term:  AI Literacy vs. Adversarial Literacy | Learn & Work Ecosystem Library

See Glossary Term: Opacity (in Artificial Intelligence) | Opaque AI | Learn & Work Ecosystem Library

See Glossary Term: Information Literacy | Learn & Work Ecosystem Library

See Topic Brief: Converging Terms:  Digital Literacy & Information Literacy | Learn & Work Ecosystem Library

A digital platform is a technology-enabled software solution, an interactive online service that allows exchanges of information, tools, and resources. Three main types of platforms serve components of the learn-and-work ecosystem: (1) learning platforms, (2) business and workforce development platforms, and (3) career navigation platforms.

Digital provenance (sometimes called data provenance or data lineage when referring specifically to datasets) is the documented history of a digital asset or data record—how it was created, modified, shared, and used over time. Provenance records help people verify whether digital content or data is authentic, reliable, and trustworthy by showing who created it, when changes occurred, and whether it has been altered.

Provenance can be captured through secure metadata, cryptographic signatures, audit logs, or—in some cases—blockchain systems that create tamper-resistant records. While blockchain can strengthen provenance, it is not required; the core purpose of digital provenance is to make the origins and lifecycle of digital information transparent for verification and trust.

In the learn-and-work ecosystem, provenance is particularly important for digital credentials, Learning and Employment Records (LERs), workforce data, and research datasets. It allows educational institutions, employers, and learners to understand and verify the origin, lifecycle, and integrity of information.

Provenance and verification are related terms: provenance is the historical evidence that shows an asset’s origin and transformations; verification is the process of checking that evidence to confirm authenticity.

See Topic Brief: Digital Provenance | Learn & Work Ecosystem Library

Digital skills are the abilities to use technology including computer software and applications, digital devices (cell phones, tablets, computers), and other computer hardware. Digital literacy skills enable individuals to participate in a range of tasks including:

• searching and using information on the internet
• being safe and responsible online
• communicating and collaborating online or remotely (e.g., through email, audio and video conferencing)
• shopping, banking, accessing services, applying for a job online, participating in digital platforms
• searching for, exploring, organizing and sharing data and information appropriately.

According to the AACRAO Higher Ed Glossary, a set of guidelines and protocols designed to ensure efficient data exchange, interoperability, and secure management of data. Encompassing data structures, metadata standards, security measures, and user control mechanisms to facilitate reliable and protected digital interactions.

In the learn-and-work context, typically refers to virtual spaces where individuals can store and share their academic and professional achievements. These are usually set up using a secure, blockchain-based system in which learners store their verified credentials, badges, industry certifications, and other educational records. It allows individuals to share their credentials with educational institutions, employers, or other stakeholders directly, without relying on traditional paper transcripts.

Related Terms:

• Competency Passport: A record focused on documenting specific competencies and skills acquired by an individual.
• Credential Wallet: Another term for a digital wallet that stores and shares credentials.
• E-Portfolio/Digital Portfolio: Where learners showcase their work, learning experiences, and credentials in a way that is accessible to educators and employers.
• Digital ID Wallet: A secure mobile app that digitally stores and manages personal information and identity. These credentials typically include IDs, passports, driver’s licenses, and other verifiable forms of identification. Unlike traditional identity systems that depend on centralized parties and external databases, digital ID wallets can store data directly on the user’s device, enabling individuals to determine what information to share, with whom, and under what conditions. This can ensure a higher level of privacy and security, as it minimizes data exposure and reduces reliance on third-party data storage.
• Learning and Employment Records (LERs): Comprehensive digital records of an individual’s skills, competencies, credentials, and employment history that may be able to show a complete picture of an individual’s education and work experiences. They have the potential to highlight verified skills, reduce hiring biases, and match people to employment opportunities. A LER can document learning wherever it occurs.
• Learning Passport: A record that documents specific competencies and skills acquired by an individual.
• Learning Record Store (LRS): A system that stores and tracks learning experiences, typically used in conjunction with xAPI standards to collect and store learning data.
• Microcredential Portfolio: A collection of small, verified credentials that certify the completion of specific learning or skill-based activities.
• Skills Passport: A record that documents professional and vocational skills to make learning portable and verifiable, allowing easier transitions between education and the workforce.
• Skills Wallets: Hold records of various skills earned over a lifetime of learning and work.

As defined in the Digital Skills for Today’s Workforce Act (March 2024), refers to the broad array of foundational and more specialized skills (including basic, advanced, and applied skills) that enable an individual to be an effective user or creator of technology while on the job; and includes industry-specific skills and skills that are transferable across industries.

See Topic Brief:  Converging Terms:  Digital Literacy & Information Literacy | Learn & Work Ecosystem Library

As defined in the Digital Skills for Today’s Workforce Act, digitally resilient means:

• With respect to an individual: an individual with the awareness, skills, agility, and confidence to be an empowered user of new technologies and able to adapt to changing digital skill demands by improving the capacity to problem-solve, improve and update skills, navigate digital transformations, and be active participants in society and the economy,
• With respect to a system: education or workforce development system that (a) has established processes and policies to adapt to evolving technological demands, buffer workers and businesses from the disruptive effects of rapid technological shifts, and foster the ability of an individual to create, critique, and use new technological tools; and (b) offers multiple pathways to economic success and does not endorse any single type of technology or digital skill as the mechanism for economic mobility.

A tool of the European Higher Education Area (EHEA) to support the recognition of academic qualifications and ensure that graduates’ degrees are recognized by higher education institutions, public authorities, and employers in their home countries and abroad. Produced by higher education institutions according to standards agreed upon by the Commission, the Council of Europe, and the United Nations Educational, Scientific, and Cultural Organization (UNESCO), it is also part of Europass framework transparency tools. The Diploma Supplement contains 8 sections providing information regarding the holder of the qualification, qualification type and its originating institution, qualification level, content of the course and results gained, and the function of the qualification. It does not represent a curriculum vitae or substitute for the original qualification.

Refers to a streamlined college application process which results in immediate acceptance based on quantitative factors such as test scores.  Direct admission programs often eliminate many features of a traditional college application process, such as essay writing, letters of recommendation, and application fees. Direct admission applications can be evaluated more quickly, often using an automated process requiring less human oversight. Guaranteed admissions offered by some state colleges to residents who meet specific criteria are an example of direct admission. 

Refers to efforts within the philanthropic sector to promote inclusion, rights, and justice for people with disabilities.

Examples of direct service organizations:

• The Arc is a community-based organization that serves people in the U.S. with disabilities.  They work with over 100 diagnosed disabilities, provide residential housing and support, family support, education and individual advocacy, and employment and referral services.
• Parents Helping Parents is a nonprofit agency that provides information, support, and training for families with special needs children. They specialize in children with special needs and different disabilities, including children with disabilities and children diagnosed with cancer and other major illnesses.
• Friendship Circle serves children with disabilities and specializes in cultivating volunteerism among teenagers with disabilities.
• Special Olympics helps people with disabilities discover new skills, abilities, strengths and success through sports.
• United Cerebral Palsy works with people who have different disabilities. They use an affiliate network throughout the U.S.  to connect families and individuals for empowerment. Services include employment support, family support, advocacies, health and wellness education, housing support, and financial assistance.
• Birth Injury Justice Center is an online resource for those affected by birth injuries, cerebral palsy, brain injuries, Erb’s palsy or other disabilities. Services include guidance to help families and children get the assistance needed to help improve their overall quality of life. 

Examples of foundations that support disability-related initiatives through grants:

• Abilis Foundation
• Disability Rights Fund and Disability Rights Advocacy Fund
• Ford Foundation
• National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR)

While many employers recognize the importance of incorporating disability into their talent management strategies and value a disability-inclusive workplace culture, many need assistance in knowing how to effectively recruit, hire, and retain, and advance people with disabilities. Disability workforce intermediaries work with employers to 1) educate businesses on the benefits of a disability-inclusive workforce; 2) identify and directly connect employers with qualified job seekers with disabilities; and 3) provide ongoing supports to ensure the success of people with disabilities after employment. Intermediaries that work in this arena have two customers — individuals with disabilities and employers.

As defined by the Velocity Network Foundation, a request to share specific credentials or claims sent by a Relying Party requestor to the Individual that holds a credential in their Velocity-enabled digital wallet or holder application. The Individual/Holder must consent to sharing specific credentials with the requesting Relying Party. The Disclosure Request, when made to the Individual/Holder, must specify (a) the purpose for which the personal data is required, (b) the duration that the verifiable credentials will be retained, and (c) all related terms and conditions pertaining to the Disclosure.

Disruptive innovation refers to innovations and technologies that make expensive or sophisticated products and services accessible and more affordable to a broader market. The term was coined in the early 1990s by Harvard University Business School professor Clayton Christensen.  The term is often misunderstood to describe breakthrough technologies that make good products better—rather it refers to innovations that make products and services more accessible and affordable, and therefore, more available to a larger population.

According to EDUCAUSE, refers to teaching methods and associated technology that enable learners t access instruction and instructional resources without being in the same educational setting as the instructor. Models for distance education include distributed students with real-time or asynchronous access to an instructor, other students, or online materials; students assembled in a classroom with a remote instructor; students and instructor(s) in multiple classrooms connected simultaneously; and other variations.

In the learn-and-work ecosystem, a three-part framework used to promote the fair treatment and full participation of all people, especially groups historically underrepresented or subject to discrimination on the basis of race/ethnicity, gender, sexual orientation, disability, age, religion, etc.

• Diversity: the representation of people from a variety of at all levels in an organization, including the leadership level.
• Equity: fairness and justice, especially related to whether people are being paid or treated fairly in the workforce.
• Inclusion: the feeling of belonging, feeling heard or valued in an organization.

See MEI – MEI / DEI | Learn & Work Ecosystem Library

A new approach to hiring and workplace diversity is gaining traction across corporate America, challenging DEI (diversity, equity, and inclusion) initiatives. The emerging movement advocates for MEI (merit, excellence, and intelligence), which emphasizes selecting candidates for jobs based solely on their qualifications, abilities, and intelligence. Proponents argue that MEI offers a more equitable and effective method for building high-performing teams, moving away from demographic considerations to focus exclusively on individual merit.

Refers to postsecondary colleges or universities that intentionally integrate traditional academic programs (such as liberal arts and baccalaureate degree pathways) with career-focused, workforce-aligned education (including short-term credentials, certificates, and associate degrees) within a single institutional structure. This model enables learners to move flexibly among academic and workforce pathways and supports both broad educational development and direct labor-market preparation.

Key characteristics:

• Two complementary missions: (1) A traditional academic mission offering bachelor’s degrees (and often graduate degrees) grounded in liberal arts and foundational disciplines. (2) A workforce mission delivering applied, technical, and nondegree credentials aligned with local or regional labor market needs.
• Flexible credentialing: Programs and credentials are designed to support stackability and seamless transitions across types of awards (e.g., certificate → associate → bachelor’s).
• Regional focus and partnerships: These institutions often have strong connections with regional employers, K–12 systems, and community partners to co-design programs that respond to economic needs.
• Access and affordability: Open-access admissions policies, affordable tuition structures, and a commitment to serving diverse learner populations are common features.

Though not formally recognized in traditional classification systems such as the Carnegie Classification, nearly 400 U.S. institutions are estimated to operate under a dual mission model. These include public universities, comprehensive colleges, and other regionally engaged institutions working to bridge the gap between academic and workforce education and to expand access and completion for a wide range of learners.

The concept of dual mission institutions has been formally convened, named, and documented by the American Council on Education (ACE). ACE has shaped the term through a national community of practice, leadership convenings, research reports, and shared membership criteria designed to identify and advance institutions operating with integrated academic and workforce missions.

National visibility of the dual mission model accelerated in 2022, when ACE and partner institutions convened the National Dual Mission Summit, bringing together institutional leaders and policy stakeholders to define the model’s characteristics, examine classification challenges, and explore its role in aligning education with workforce needs.

See: Dual Mission Institutions Initiative — American Council on Education (ACE) | Learn & Work Ecosystem Library

Refer to an essential combination of 21st century skills also commonly known as soft skills, human skills, or power skills. The term emphasizes the lasting value and universal applicability of these skills. They include problem-solving, leadership, critical thinking and personal skills like teamwork, cognitive flexibility, adaptability, collaboration, creativity, negotiation, initiative, risk-taking, cognitive flexibility.  Several research studies have demonstrated that durable skills are increasingly in demand by employers. 

Durable skills differ from hard skills in that hard skills are more often traditionally taught by colleges and universities, and easily measured and credentialed. By contrast, durable skills are seldom directly taught by higher education institutions and are more challenging to measure. 

Durable skills are recognized for their lifelong durability, while hard skills often become outdated or irrelevant over a lifetime, depending on the industry group in which employees are applying their technical skills. Durable skills are transferable and relevant in any job, cannot be easily displaced by technology, and are critical to creating positive work environments. 

According to EDUCAUSE, learning that involves a web-based component, enabling collaboration and access to content that extends beyond the classroom.

According to EDUCAUSE, refers to an institutional platform that provides electronic distribution of text, enabling students to access e-books/e-texts on mobile devices and offering additional features such as annotation, search across texts, and note sharing. Basic systems deliver existing packaged e-content; advanced systems enable collection, copyright clearance, and bundling of content similar to e-texts and e-coursepacks.

An individual, organization, or institution that embraces a new idea, technology, product, or practice before it reaches mainstream acceptance. Early adopters are typically willing to take calculated risks, invest resources, and experiment with emerging innovations to gain advantages such as increased visibility, improved outcomes, or competitive positioning. Their actions often influence whether and how innovations spread more broadly.

Three factors are especially relevant for early adopters:

• Early adopters drive innovation diffusion, serving as critical test beds for systemic change.
• They can gain reputational, strategic, and operational advantages but must weigh these against costs, uncertainty, and scale risks.
• Success often hinges on collaboration, interoperability, and adoption of shared standards to scale innovations.

There can be both upsides and downsides to early adoption:

• Upsides
  • Access to new tools, resources, or policies before others
  • Reputational leadership
  • Access to funding, partnerships, and pilot opportunities
  • Ability to shape standards, policies, and practices in development
  • Ability to influence future adoption by serving as a model or case study
  • Stronger alignment with innovation-driven employees and learners
• Downsides
  • Risk of adopting untested or unstable systems
  • Higher initial costs before economies of scale emerge
  • Uncertainty in outcomes due to limited data or research
  • Organizational disruption during transition or experimentation
  • Need for greater time and capacity investment in troubleshooting and refinement
  • Possibility of failure or abandonment if the innovation does not gain traction

See Topic Brief: Early Adopters & Early Adoption Practices in Learn-and-Work Ecosystem | Learn & Work Ecosystem Library

As defined by Austin, Sorcinelli, and McDaniels, early career faculty are those within the first seven years of appointment to a faculty position or those who have not yet been awarded tenure (in institutional contexts where tenure is a possibility), acknowledging that, in some cases, faculty members are awarded tenure prior to the seventh year. New faculty may be individuals in their 20s or individuals older in age, having had other professional posts prior to moving into the professoriate. New faculty may work either full- or part-time.

Several issues are germane to new faculty: 1) the demographics of today’s early career faculty (e.g., gender, race/ethnicity); 2) the preparation they receive and the gaps in their graduate and post-doctoral backgrounds; 3) the abilities and skills they need to succeed in higher education; 4) expectations they have for their careers and the challenges experienced in their new roles; 5) strategies individual early career faculty and employing institutions can employ to enhance their professional growth; and 6) directions for future research.

Alternate terms:  early career faculty member, new faculty member.

Refer to high schools where students can simultaneously work toward both a high school diploma and associate degree or other college credential, at no cost, through an organized course of study. Federal law specifies that students at these schools, which are partnerships between a local educational agency and at least one higher education institution, earn “no less than 12 credits” that are transferrable to the college or university partner. Similar to dual (concurrent) enrollment programs, early college high schools typically provide additional support services to students and intentionally recruit students who are underrepresented in higher education.

The model especially grew after the Gates Foundation launched its Early College High School Initiative in 2002, helping to support the development of public early college high schools. Programs developed through the initiative often offered more college credits than the federal definition requires, resulting in many students earning an associate degree.

There are about 400 early colleges in the U.S., although this is a modest number compared to the some 23,500 public secondary and high schools in the U.S.

Some states regulate which programs qualify as early colleges while other states allow the term to be used more loosely.

In some states, Early College programs operate under statewide designation frameworks that establish quality standards, integrated pathways, and equity-focused access goals.

Refers to changes in an individual’s or family’s economic position over time, most commonly measured through income, earnings, or wealth. The term is used in several related ways across research, policy, and practice.

• Earnings growth and financial stability (common applied use): Often refers to an individual’s ability to increase earnings, improve financial stability, and move into a more secure and sustainable economic position. This is the most common usage in higher education, workforce development, and credentialing discussions.
• Return on investment (ROI) and time to sustainable wages (emerging use): Increasingly defined in terms of how quickly education or training leads to improved economic outcomes. Metrics commonly used: the speed at which a learner can earn back the cost of a program; and time to employment, wage gains, and attainment of sustainable or family-supporting wages. This framing is gaining traction in discussions of short-term credentials, workforce programs, and skills-based pathways.
• Changes over a lifetime: Describes how a person’s economic situation improves (or declines) over the course of their working life; e.g., moving from lower-wage to higher-wage work over time.
• Changes across generations: May refer to how a person’s economic position compares to that of their parents or family of origin; e.g., individual earning more than the household born into.
• What “moving up” means (how improvement is measured): Economic mobility can also be understood in terms of whether people are better off than before (e.g., compared to their parents or earlier in their lives) — sometimes called absolute mobility; or whether people are moving up compared to others in the economy (e.g., from lower-income groups into higher-income groups — sometimes called relative mobility.

The meaning of economic mobility is shifting in practice. In traditional research, mobility has often been measured over long time horizons, such as across a career or across generations. Increasingly, mobility is being evaluated based on shorter-term outcomes, including speed to employment, time to wage gains, and return on investment (ROI) for education and training.

In a lifelong learning environment (sometimes described as a 100-year lifespan), economic mobility is no longer a one-time outcome but a repeated, cumulative process. Individuals may move in and out of learning and work multiple times, making mobility something that must be supported continuously rather than achieved once.

See: Social Mobility | Learn & Work Ecosystem Library

See: Internal Mobility | Learn & Work Ecosystem Library

See: Learning Mobility / Learner Mobility | Learn & Work Ecosystem Library

Refers to a projected point in the future when accelerating advances in artificial intelligence and automation fundamentally transform the economy to such a degree that traditional labor markets can no longer function as they do today. While speculative, the idea is used by economists, futurists, and workforce-development leaders to explore scenarios in which technological progress outpaces society’s ability to adapt through retraining, new job creation, or policy interventions.

At this stage, machines could perform most economically valuable tasks more efficiently than humans. These tasks include data analysis, report writing, customer service interactions, scheduling and logistics planning, financial modeling, diagnostic support, legal document drafting, and quality-control inspection—areas economists monitor as indicators of accelerating automation.

This scenario could potentially lead to widespread job displacement, major shifts in income distribution, and the need for new economic models to support human well-being. The concept draws from the technological “singularity” idea but focuses specifically on economic and labor-market implications.

See Topic Brief: Economic Singularity: Humans & AI | Learn & Work Ecosystem Library

Allows data to be processed and analyzed closer to the source of the data, rather than in a centralized data center. This can improve response times, reduce latency (amount of time it takes for a data packet to travel from one designated point to another), and reduce the amount of data that needs to be transferred over connected networks.

According to the Data Quality Campaign, education data is information about individuals, groups, and entire populations. This includes:

• course access and attendance by students
• performance data and postsecondary enrollment rates
• any information that can be used to support individuals throughout their education and workforce journeys.

According to EDUCAUSE, refers to the functions and resources associated with and specific to supporting teaching and learning at higher education institutions. They often include:

• Instructional technology support including instructional support staff (including technologists and designers); instructional technology used by faculty (including learning management system and support, e-portfolios, assessment systems; and collaboration tools, etc., and teaching and technology center staff
• Learning management systems (homegrown or purchased)
• Library systems
• Technology systems to support student success (e.g., degree audit, advising center management, academic early alert system, etc.)
• Classroom technology equipment and installation
• Classroom and learning space support (including audio visual support)
• Student technology centers (labs, makerspaces, collaborative spaces, training, support, etc.)
• Specialized training for faculty and students
• Distance education, e-learning, online learning, and/or hybrid learning support and related technology
• Business process/systems analysis specific to this domain area
• Technology research and development specific to this domain area
• Management responsibilities (HR management, financial planning, project management, vendor contract management, etc.) for staff dedicated to this domain area
• Staff affiliated with these functions (including administrative, clerical)
• Hardware, software, and supplies affiliated with these functions

They typically do not include:

• Multimedia services (Support for design, production, and deployment of content in audio, still image, animation, video, and interactive formats, often in combination with text.) (often found in IT Support Services)
• TV production and broadcasting (IT Support Services)
• Desktop computing/endpoint computing (IT Support Services)
• Specialized support centers (e.g., special center for multimedia production or centers that support use of specialized hardware or software) (IT Support Services)
• IT Training and education including policy training and education; general user training and education and related staff (IT Support Services)

The eldercare workforce refers to the paid and unpaid individuals who provide health, personal, social, and supportive services specifically to older adults, typically age 65 and above. This workforce includes direct care workers in home- and community-based settings, certified nursing assistants and nursing home staff, licensed healthcare professionals (such as nurses and therapists), social workers, care coordinators, long-term care administrators, and family caregivers whose care recipients are older adults.

In the mid-20th century, eldercare was primarily a family responsibility, with most support provided informally by relatives. As women entered the paid workforce in larger numbers and life expectancy increased, structural gaps in eldercare became more visible, prompting public policy debates about long-term care financing, workforce training, and support systems. In 2009, coalition efforts such as the formation of the Eldercare Workforce Alliance signaled a more organized policy focus on training and retaining workers capable of meeting the needs of an aging population, building on earlier calls from national health bodies to expand geriatric education and workforce capacity.

In terms of size and scale in the United States:

• Unpaid eldercare: According to U.S. Bureau of Labor Statistics data for 2023–24, an estimated 38.2 million U.S. adults age 15 and over provided unpaid care to someone age 65 or older during that period, representing about 14% of the civilian noninstitutional population.
• Paid direct care workers: The broader group of direct care workers — many of whom provide support to older adults — includes roughly 5.4 million workers in the U.S., comprising home care workers, residential care aides, and nursing assistants across settings such as home care agencies and longterm care facilities.
• Long-term care employment footprint: Industries providing home and community-based care accounted for about 4.3 million jobs in mid-2024, including services primarily for older adults and people with disabilities.
• Projected growth: Employment of home health and personal care aides — key components of the eldercare workforce — is expected to grow much faster than the average for all occupations over the next decade, driven by rising demand for aging-related support services.

The eldercare workforce operates across private homes, assisted living communities, nursing homes, healthcare facilities, hospice programs, and community-based organizations. Demand for these services is increasing rapidly as the U.S. population ages and more adults live longer with chronic conditions.

Because the eldercare workforce is defined by the age cohort it serves (older adults), it is a subset of the broader caregiving workforce, which includes workers serving individuals across the lifespan. This age-specific focus shapes unique workforce challenges — including workforce shortages, training needs in geriatric care, compensation structures, and policy frameworks for long-term services and supports.

See: Caregiving Workforce | Learn & Work Ecosystem Library

Eligible training provider lists (ETPLs) are lists of pre-approved programs established by each state and territory under United States workforce development law, the Workforce Innovation and Opportunity Act (WIOA). WIOA funds vouchers for unemployed or underemployed workers to enroll in job training services included on the lists. These are typically short-term, non-four-year-degree programs.

Eligible Training Provider programs are job training programs eligible for funding under United States workforce development law, the Workforce Innovation and Opportunity Act (WIOA). Under the law, each state and territory must maintain a list of pre-approved programs from which eligible individuals may select. Programs are pre=-approved on lists known as Eligible Training Provider lists.

Refers to a postsecondary education credential that is intentionally built into a longer academic or workforce pathway—such as a bachelor’s degree program, apprenticeship, or employer-aligned training model—so that learners earn a recognized associate degree along the way to a higher-level credential or career outcome. By awarding the associate degree as part of the pathway, embedded models ensure that learners receive a meaningful, labor-market-relevant credential even if they do not complete the full bachelor’s program.

Advancements in the internet of things (IOT) and other technologies such as artificial intelligence and virtual reality are spawning new occupations. These new occupations are emerging to create, sell, maintain, service, and grow technologies and their integration into teaching and service occupations. Tracking emerging occupations and their impact on the workforce is critical to preparing secondary, postsecondary, and other educational markets for changing workforce demands from employers.

Developed in 2012 with funding from the U.S. Department of Education to advance a unifying set of employability skills, the Employability Skills Framework details a set of nine key skills that are organized in three broad categories: (1) Applied Knowledge, (2) Effective Relationships, and (3) Workplace Skills. The Framework is designed to support individuals seeking career advancement and to unify the workforce development and education sectors. It is based on an inventory of existing employability skills standards and assessments.  The associated Skills Checklist demonstrates ideas for integrating skills into instruction.

Refers to tools or methods some companies use to assess the skills and abilities of prospective and current employees for hiring, promotion, training, and/or talent development decisions. A 2018 report (Saville and Holdsworth- SHL) found that about 60% of workforce managers use a tool for talent development, and about 93% of recruiters use a tool for hiring purposes. Examples of the various types of employee assessments include:

• 360-Degree Feedback: Gathers feedback from multiple sources including peers, subordinates, supervisors, and sometimes external stakeholders, to provide a comprehensive view of an employee’s performance. It focuses on assessing interpersonal skills, leadership abilities, teamwork, and communication effectiveness.
• Psychometric Assessments: Assesses psychological traits and characteristics of individuals, such as cognitive abilities, personality traits, behavioral tendencies, and emotional intelligence; e.g., Myers-Briggs Type Indicator, DISC assessment, and Hogan Personality Inventory.
• Performance Appraisals: Evaluating an employee’s job performance against predetermined criteria and goals, typically occurring at regular intervals such as annually or semi-annually, and focusing on aspects like task completion, goal achievement, job knowledge, and contributions to the organization.
• Skill Tests and Simulations: Assess proficiency in specific job-related skills or tasks. These can include practical exercises, case studies, role-playing scenarios, or technical exams designed to measure competencies such as problem-solving abilities, technical expertise, decision-making skills, and attention to detail.
• Behavioral Interviews: Asking candidates to provide specific examples of past behaviors or experiences in various situations. Interviews are designed to predict future behavior based on past actions and assess competencies such as adaptability, conflict resolution skills, leadership potential, and problem-solving abilities.

According to iCIMS (provider of talent acquisition software), E-Verify is a web-based system operated by the Department of Homeland Security (DHS) in partnership with the Social Security Administration. It allows employers to electronically verify the employment eligibility of their new employees.

Also known as employee experience mapping or employee experience journey mapping, refers to a structured human resources (HR) and organizational development practice that visually represents the stages of an employee’s experience throughout their lifecycle with an employer. The process typically covers the full journey—from attraction and recruitment to onboarding, professional development, performance management, retention, and eventual exit (through resignation, retirement, or other separation).

The map highlights key touchpoints (e.g., job application, onboarding, first performance review, career advancement opportunities), along with the associated emotions and experiences that shape employee engagement and satisfaction. By identifying pain points and opportunities, organizations use journey mapping to improve workplace culture, strengthen retention, and enhance overall employee well-being.

Employee journey maps are most often designed and implemented by employers—usually HR teams, organizational development specialists, or consultants—in collaboration with employee feedback (e.g., surveys, interviews, or focus groups). While increasingly adopted across organizations of different sizes, the practice is most prevalent in larger companies with formal HR functions, since it requires data collection, visualization, and analysis capacity.

The concept of journey mapping originated in customer experience (CX) design more than a decade ago, and HR professionals adapted it into “employee journey mapping” in the mid-2010s. Its use has grown with the rise of “employee experience” as a strategic HR focus, especially in large organizations prioritizing retention, engagement, and employer branding.

Related concepts include: employee lifecycle and employee experience strategy.

Refers to the practice of employers who implement surveillance software tools for remote or hybrid workers, often unbeknownst to them. Where workers do not know these software tools have been implemented, this aspect makes this an issue of transparency, ethics, and often privacy law compliance, depending on the jurisdiction.

There are several alternative or related terms for this practice:

• Bossware – referring to software that allows employers to monitor workers’ activities, often secretly or without explicit consent (intrusive employee surveillance tools).
• Surveillance Capitalism – broader term that is relevant when the data collected from workers is monetized or analyzed to shape behavior.
• Digital Surveillance or Workplace Surveillance – general terms for the practice of monitoring employees through digital means.
• Productivity Monitoring Tools – often used by companies to frame the software in the context of productivity.
• Time Tracking Software – often overlaps with surveillance when it includes screen capturing, keystroke logging, or webcam monitoring.
• Workplace Panopticon – metaphor from philosophy (Bentham/Foucault), used in HR/ethics discussions about constant surveillance and its psychological effects.
• Algorithmic Management – refers to using automated systems (including monitoring tools) to manage and evaluate employees.
• Helicopter Management – According to Owl Labs’ 2024 State of Hybrid Work report, refers to a growing trend for company managers to evaluate workers’ performance, particularly in remote and hybrid settings using activity monitoring software tools.  The majority of workers (86%) in the 2024 survey believe it should be a legal requirement for employers to disclose if they use monitoring tools.

These are voluntary, employee-led groups that bring together individuals with shared identities, interests, or experiences within an organization such as a company. While often rooted in cultural or demographic commonalities (such as race, gender, LGBTQ+ identity, veterans, or working parents), ERGs/BRGs are typically cross-functional and open to all employees, fostering inclusion and community across departments.

In addition to supporting diversity and belonging, ERGs/BRGs can serve a talent management function in organizations—helping attract, develop, and retain talent; inform inclusive product or service design; and identify high-potential employees for leadership pipelines.

While ERGs/BRGs are a widely adopted practice, they are not always defined identically across organizations. Here are examples of several sources that discuss ERGs / BRGs:

• Catalyst (2021) – Employee Resource Groups (ERGs)
  • https://www.catalyst.org/topics/employee-resource-groups-ergs/
  • Describes ERGs as voluntary, employee-led groups that foster inclusive workplace cultures and support talent development
• Harvard Business Review (2019) – Thomas, D.A., & Creary, S.J. Having a Strong ERG Isn’t Enough
  • https://hbr.org/2019/10/having-a-strong-erg-isnt-enough
  • Discusses need to integrate ERGs into corporate strategy and leadership development to drive impact
• McKinsey & Company (2020) – How employee resource groups can drive inclusion and performance
  • https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/how-employee-resource-groups-can-drive-inclusion-and-performance
  • Discusses how ERGs contribute to performance, belonging, and leadership pipelines
• Society for Human Resource Management (SHRM) – Employee Resource Groups: An Introduction
  • https://www.shrm.org/resourcesandtools/tools-and-samples/toolkits/pages/employeeresourcegroups.aspx
  • Outlines role of ERGs in fostering workplace belonging, supporting HR goals, and aligning with business strategies

Employer compensation systems refer to the structures, policies, and practices organizations use to determine how employees are paid and rewarded for their work. These systems typically include base salary structures, wage progression models, bonuses, incentives, benefits, and other forms of financial or non-financial compensation. Compensation systems are designed to support organizational goals such as attracting talent, motivating performance, retaining employees, and maintaining internal and external pay equity.

Human resources professionals and organizational leaders often use a range of specific terms to describe different compensation approaches or issues that arise within compensation systems. Examples include merit pay, cost-of-living adjustments (COLA), market adjustments, pay compression, and informal expressions such as “peanut butter raises,” which describe evenly distributed salary increases across employees regardless of role or performance.

Understanding employer compensation systems is increasingly important within the evolving learn-and-work ecosystem. As organizations adopt skills-based hiring, recognize microcredentials and alternative learning pathways, participate in talent marketplaces, and integrate AI and machine-human workforce models, employers are reexamining how compensation reflects skills, competencies, productivity, and value creation. These changes are prompting new conversations about how pay systems reward verified capabilities, continuous learning, and contributions made by both human workers and technology-enabled teams.

Refers to organizations that serve as a bridge between employers and the education and workforce systems by providing the capacity, expertise, and coordination needed to design, implement, and scale work-based learning opportunities, including internships and apprenticeships, while strengthening talent pipelines for industry. Employer intermediaries help employers overcome common barriers such as limited staffing, program design challenges, regulatory compliance, and candidate recruitment. Intermediaries engage employers around talent needs, co-designing solutions with education and training providers, brokering partnerships, supporting program implementation, and managing administrative and funding requirements.

The ways that new employees are welcomed to the organization, receive information about how the organization functions on a day-to-day basis, and are introduced to others who work for the organization.

An Employer Services Compliance library helps employers electronically navigate the complicated and dynamic landscape of employment forms required for workforce compliance across the U.S., addressing the essential tasks conducted by Human Resources (HR) teams. The plethora of forms stem from federal, state, and local jurisdictions, and cover issues such as digital labor law, family rights, gender equity, medical and family leave, paid sick leave, pregnancy accommodation, wage and hour, and state separation—in onboarding, employment, and offboarding.

Example: Experian Employer Services Compliance Library

Refers to a contractual arrangement between an employer and employee in which the employer covers the costs of some or all of an employee’s tuition for a program of study such as a college or university degree, or other forms of education. Employer tuition reimbursement programs are viewed as a win-win strategy—employers use the tuition assistance program as an employee retention and recruitment tool, and employees use the program as a form of financial aid to pursue their educational interests. Tuition assistance programs are supported by federal tax policies. Federal tax laws allow employees to receive up to $5,250 in tuition reimbursement tax-free annually from their employer.

See Topic: Employer Tuition Assistance Program / Employe Tuition Reimbursement – Finance

A training or upskilling initiative in which one or more employers set the skill requirements, guide program design, and influence delivery to meet their workforce needs. Instruction may be delivered by the employer, a higher education institution(s), a workforce intermediary, or a private training vendor (third-party provider), but employers drive the goals, content, and outcomes.

Note: Employer-led training partnerships refer to who governs and shapes the training, in contrast to a related term, third party training / third-party credential providers which refer to who delivers instruction or awards the credential. While these categories often overlap, they are not interchangeable. An employer may lead a program that is delivered by a third party, or a third party may offer training independently without employer involvement. Keeping the concepts distinct helps clarify roles, responsibilities, and the source of quality assurance.

See Topic Brief: Employer-Led Skills Pathways & Workforce Development Initiatives | Learn & Work Ecosystem Library

Employer-Operated Academies (often known as corporate academies or corporate academy systems) are structured, organization-led learning and workforce development programs designed to upskill, reskill, and advance employees in alignment with business strategy and evolving skill needs. These academies are created, governed, and managed by employers. They typically combine curated learning content, cohort-based experiences, applied projects, mentoring, and assessment within an integrated program framework. Digital learning platforms such as Learning Experience Platforms (LXPs), Learning Management Systems (LMSs), or specialized academy-building tools commonly support delivery, engagement, and outcomes measurement.

There are multiple aims of these academies:

• Build priority skills at scale, reaching large numbers of employees in a company
• Prepare employees for emerging roles
• Support internal talent mobility and career progression
• Reduce reliance on external hiring
• Align workforce capabilities with business transformation strategies

Key features:

• Employer ownership and governance: The organization designs curriculum, selects participants, defines success metrics, and manages delivery.
• Skills and role alignment: Programs are built around defined skill frameworks, role profiles, or career pathways.
• Blended learning design: Academies combine self-paced digital content, live instruction, peer collaboration, coaching, and work-based projects.
• Cohort and community structure: Participants progress together to build shared practice, engagement, and peer networks.
• Outcome orientation: Programs emphasize measurable results such as skill acquisition, certification completion, role readiness, promotion eligibility, or performance improvement.
• Technology collaborators: Platforms such as Degreed, 360Learning, Intrepid, Cornerstone, or proprietary enterprise systems provide infrastructure for experience design, content integration, learner tracking, and analytics.

Traditional corporate training often consists of stand-alone courses, compliance modules, or ad hoc professional development. Employer-Operated Academies instead deliver cohesive, multi-week or multi-month learning journeys that are connected to strategic workforce priorities, career pathways, and measurable talent outcomes.

Examples:

• Walmart Academy – Frontline, supervisory, and leadership development academies for store and supply-chain employees.
• Deloitte Academies – Capability-based academies in areas such as digital transformation, AI, sustainability, and leadership.
• Telstra Enterprise Academy – Internal academy system aligning workforce learning with organizational transformation.
• Technology-enabled academies – Built on platforms such as Degreed or 360Learning to deliver leadership, digital skills, and reskilling pathways.

Employer-Operated Academies reflect the shift toward skills-based workforce development, continuous learning, and internal talent mobility. They illustrate how employers are increasingly acting as education providers — designing learning pathways, credentialing skills, and integrating training directly into workforce strategy — often in partnership with learning technology platforms and external content providers.

A structured “earn‑and‑learn” training model in which an employer or group of employers takes primary responsibility for recruiting, hiring, training, and progressing apprentices. These programs combine on‑the‑job work with mentoring or supervision and often include formal classroom or related instruction. Many U.S. programs are registered apprenticeships authorized by the Department of Labor (DOL) or state apprenticeship agencies. Many employer-operated apprenticeship programs also work with an intermediary to secure needed services.

The purpose of these apprenticeship programs is generally to:

• Address talent shortages and skills gaps by building a workforce tailored to specific employer and industry needs.
• Improve retention, as apprentices trained by the employer are more likely to stay.
• Transfer institutional knowledge and technical skills to the next generation.
• Enhance productivity, quality, and workplace engagement.
• Provide apprentices with earn‑and‑learn opportunities and career pathways, often with recognized credentials (such as industry certifications or associate degrees when higher education institutions are included in the partnership).

Key attributes of employer-operated apprenticeship programs:

• Apprentices are paid employees, with wage progression as skills improve.
• Learning occurs primarily on the job, guided by experienced mentors, with supplementary technical instruction.
• Programs are aligned with employer skills needs and industry standards, producing recognized credentials.
• Employers take direct ownership of talent pipelines, from recruitment to career progression

Common design elements include:

• Employer-led curriculum: Aligned with business objectives and skills requirements.
• Structured on-the-job training with defined competencies and learning milestones.
• Mentorship and supervision by experienced staff.
• Wage progression tied to skill mastery.
• Credentialing: Completion results in recognized certificates or degrees.
• Retention strategies: Supporting apprentices into journeyman, advanced, or leadership roles.

See Topic Brief: Employer‑Operated Apprenticeship Programs | Learn & Work Ecosystem Library

See Topic Brief: https://learnworkecosystemlibrary.com/topics/apprenticeship/

Refers to often proprietary data that are collected and managed by employers to track an employee’s status, wages, participation in training, and, sometimes, quality and performance of work.

Refers to a new phase of work undertaken later in life, often after traditional retirement, where individuals pursue meaningful and fulfilling activities, whether paid or unpaid. Also known as “second act” or “encore work.”

Refers to the final, independent evaluation of an apprentice’s ability to perform a specific job role. It takes place at the end of an apprenticeship program and serves as a checkpoint to ensure the apprentice has gained the skills, knowledge, and behaviors needed to be job ready.

These assessments are typically conducted by a third-party organization, separate from the apprentice’s employer or training provider, to ensure objectivity and quality. The assessment methods vary by occupation and may include:

• Practical demonstrations or simulations
• Oral interviews or professional discussions
• Written tests or digital exams
• Review of a portfolio of evidence

A number of digital tools are used, especially in the UK, to manage and deliver EPAs:

• EPAPro – A UK-based platform for managing assessment logistics and tracking progress (epapro.co.uk)
• Smart EPA – A management tool used by EPAOs and training providers (smartepa.co.uk)
• OneFile – An e-portfolio platform used to compile apprentice evidence and facilitate EPA (onefile.co.uk)
• Zoom / MS Teams – Used for remote interviews, professional discussions, and virtual test environments
• Moodle or similar LMS (Learning Management System) – Sometimes used for digital testing and portfolio management

Use of end-point assessments is more common outside the U.S.  For example, in the United Kingdom, EPA is a standardized, required element of all apprenticeship programs in England. Introduced as part of major apprenticeship reforms, the assessment ensures consistency and credibility across industries. Apprentices cannot complete their program or receive full certification without passing an EPA administered by an approved independent End-Point Assessment Organization (EPAO).

While the term “End-Point Assessment” is unique to the UK, other countries use similar approaches under different names:

• United States: Registered apprenticeships may end with an employer-administered competency check or an industry-recognized certification (e.g., from NIMS, AWS, CompTIA). The U.S. does not have a nationally standardized EPA system but increasingly supports competency-based apprenticeship models through the U.S. Department of Labor.
• Germany, Switzerland, Austria: Apprenticeship systems include final exams administered by chambers of commerce or government agencies, serving a similar function.
• Australia and New Zealand: Include workplace-based competency assessments, sometimes with independent oversight.
• Canada: Uses Red Seal exams as final qualification checks in skilled trades.

EPA plays a vital role in the global shift from time-based learning (hours spent in training) to competency-based learning (proof of skill). It ensures apprentices demonstrate real-world capabilities before earning a credential, making learning more relevant and transferable across employers and industries.

Refers to job positions that generally do not require prior work experience. These roles are designed for individuals who are starting their careers, such as recent school graduates. Entry-level positions typically require minimal formal education though some may still require specific credentials such as degrees or certifications depending on the industry. These positions often provide training to help new employees develop skills necessary for advancement.

The Equal Employment Opportunity Commission (EEOC) is a U.S. government agency that enforces laws against workplace discrimination. It makes sure that people are treated fairly at work, regardless of their race, color, religion, sex, national origin, age, disability, or genetic information. The EEOC investigates complaints, helps resolve disputes, and provides guidance to employers and employees about their rights and responsibilities under federal anti-discrimination laws.

Equity means that no matter what a student’s background, language, race, economic profile, gender, learning capability, disability or family history, each student has the opportunity to get the support and resources they need to achieve their educational goals. While the terms equity and equality are often used interchangeably, there are differences between the two. Equality focuses on ensuring students are presented with the same educational opportunities throughout their scholastic career; however, this approach doesn’t take into consideration that even with those opportunities, different students will have different needs in order to succeed. Equity focuses on taking those opportunities presented to students and infusing them with support and resources to turn the education system into a level playing field. This means that disadvantaged students will get the support they need to become equal to students who are not disadvantaged.

Refers to a core set of industries that provide the foundational goods, services, and infrastructure necessary for a nation’s functioning and growth. In the United States, this includes construction, manufacturing, energy, public services, transportation and logistics, and skilled trades. Collectively, these industries employ approximately 95 million workers across 3 million businesses and generate over one-third of the nation’s gross domestic product (GDP). Despite its outsized role in supporting daily life and long-term economic resilience, the Essential Economy faces persistent challenges—including labor shortages, skill gaps, and declining productivity. These challenges pose risks to national competitiveness and future prosperity.

Essential Learning Outcomes (ELOs) define the knowledge and skills gained from a liberal education. ELOs provide a framework to guide learner’s cumulative progress in higher education institutions.

Ethics and integrity are crucial to the learn-and-work ecosystem, particular in four areas:

• Acquiring Credentials. Credentials such as diplomas, degrees, certifications, certificates of accomplishment, licenses, and badges represent an individual’s qualifications and expertise in various fields. Credential earners are expected to adhere to ethical behavior; i.e., adhering to the rules and regulations of educational institutions, certification bodies, and licensing authorities. This typically means engaging in honest practices, including completing required coursework, exams, and practical experiences without resorting to plagiarism, cheating, or misrepresentation. Ethical conduct ensures that credentials are earned through genuine effort and knowledge, upholding the integrity of the educational and professional systems.
• Representing Credentials. Maintaining integrity in the use of credentials is critical. This means individuals accurately represent their qualifications and expertise, without falsification and exaggeration.
• Awarding Credentials. Credential providers (educational institutions, third-party organizations) are expected to issue credentials that adhere to ethical practices in credential development, assessment and verification, and evaluation. Upholding professional ethics and ethical standards, maintaining confidentiality, and avoiding conflicts of interest are crucial to establishing and preserving trust and reliability in the system of credentialing.
• Employer / Others’ Use of Credentials. Employers, professional organizations, and the general public rely on the accuracy and authenticity of credentials to make informed decisions. Upholding integrity means using credentials truthfully and responsibly, respecting the trust placed in the system and ensuring that individuals are qualified for the roles they undertake.

The European Union (EU) is creating EU Digital Identity Wallets to meet the challenges of digital identification for citizens, residents, and businesses across Europe.  The Identity Wallets are proposed in policy – in the Digital Identity Regulation, effective May 2024.  The aim of the Identity Wallet is to enable more services to be accessed digitally by the public, including across borders. Businesses will also find it easier to offer online services across Europe since the wallet means secure authentication will be available to every potential customer in the EU. Use cases expected in areas such as: (1) education Certification – storing and sharing education credentials; (2) accessing government services – accessing digital public services (nationally and across borders) by using the wallet to securely identify and authenticate yourself; (3) travelling – storing and sharing key travel documents like boarding passes and identify yourself when reserving a hotel; (4) opening a new bank account; and (5) enrolling in a university abroad.

The wallet is a collaboration between the EU institutions and Member States. Key features of the wallet initiative:

• All EU Member States (country) are obligated to offer at least one version of the wallet to their citizens, residents, and businesses.
• The wallets must be built to common technical standards and offer the same user experience and functionalities, regardless of where used and in which Member State issued.
• Each wallet will be interoperable, working with every other national wallet; accepting the same digital documents (mobile driving licenses, university diplomas etc.) and digital ID (date of birth, nationality, etc.).
• Every certified version of the wallet will be recognized throughout Europe.

See: https://learnworkecosystemlibrary.com/initiatives/eu-digital-identity-wallets-european-union/

A pair of interconnected initiatives developed by the European Commission to advance digital literacy and competence across Europe.

The European Digital Competence Framework (DigComp)—first published in 2013 after development began in 2010—is a reference model that defines digital competence in five key areas: (1) information and data literacy, (2) communication and collaboration, (3) digital content creation, (4) safety, and (5) problem solving. It includes proficiency levels and descriptors. Updates are made on a reglar basis.

Building on this framework, the EU Digital Skills Assessment Tool was launched in 2021 as an online self-assessment resource that individuals use to measure their skills against DigComp standards, receive tailored feedback, and identify areas for improvement.

Together these resources provide a common language and method for assessing, comparing, and developing digital skills in support of education, workforce readiness, and EU digital policy objectives.

A key action in the European Skills Agenda designed to help people plan their learning and careers.  Europass offers a range of free, multilingual tools to help support individuals who are learning and working across Europe:

• Europass Profile allows users to describe their skills, discover interesting job and learning opportunities, manage their applications, and create curriculum vitae and cover letters.
• Individuals can store their Europass Mobility from experiences abroad in their Europass Library.
• Information resources on working and learning in different European Union (EU) countries.
• Digitally signed credentials: free tools and software for institutions to issue digital, tamper-proof qualifications and other learning credentials.
• Interoperability: this feature allows users to correspond with employment and learning services, to connect with them and make applications.
• Diploma Supplement is issued by higher education institutions and offers helpful information on higher education qualifications (such as grades, achievements and institutions) in a standard format.
• Europass Certificate Supplement is issued by vocational education and training (VET) institutions and offers helpful information on your vocational qualifications (such as grades, achievements and institutions) in a standard format.

Refers to the multilingual classification of European Skills, Competences, Qualifications and Occupations. The classification identifies and categorizes skills, competences, qualifications, and occupations relevant for the European Union (EU) labor market and education and training. It systematically shows the relationships between the different concepts. ESCO was created in 2015 and last updated in 2018; is part of the Europe 2020 strategy; has been developed in an open IT format; and is available for use free of charge and accessible via the ESCO portal.

Refers to news, analysis, or informational content that remains relevant and useful over a long period, rather than being tied to a specific, time-sensitive event. This content continues to provide value to readers or users months or even years after publication. Evergreen journalism is especially valuable in education, workforce development, and lifelong learning, where users seek guidance, explanations, or historical perspectives that retain relevance over time.

In an AI-enabled information environment, evergreen content requires structured storage, metadata tagging, and retrieval systems to ensure discoverability and accessibility. AI tools can also help maintain its relevance by suggesting updates or linking it to new developments.

Examples

• Explainers on emerging workplace technologies
• Historical analyses of industries or labor trends
• Educational “how-to” or skill-building articles

Refers to companies that encompass event planning, show production, logistics, venue management, and technology. Examples of industry organizations in this sector include:

• Exhibitions & Conferences Alliance (ECA)
• Exhibition Services & Contractors Association (ESCA)
• Experiential Designers & Producers Association (EDPA)
• International Association of Exhibitions & Events (IAEE)
• International Association of Venue Managers (IAVM)
• Society of Independent Show Organizers (SISO)

In 2024, the Exhibitions Industry Collective was created to nurture talent, promote workforce development, and ensure the long-term sustainability of the exhibitions and events industry.

See:  Exhibitions Industry Collective + Workforce Development Initiative | Learn & Work Ecosystem Library

A set of processes and methods that allows human users to comprehend and trust the results and outputs created by the machine learning algorithms of AI systems. Instead of simply producing a score or recommendation, explainable AI shows which factors the algorithm considered and how much weight each factor received.

Many AI hiring tools operate as “black boxes,” making it difficult to determine why a candidate was selected or rejected. Explainable AI mitigates this problem by offering visibility into how the model functions, allowing employers to identify potential sources of discrimination and to demonstrate accountability in hiring practices. This interpretability enables users—such as employers, regulators, and job applicants—to comprehend, trust, and audit algorithmic outcomes. This is critical  for ensuring fairness, detecting bias, maintaining compliance with anti-discrimination laws, and ensuring transparency and accountability. A growing concern is that many employers purchase AI systems from vendors without fully understanding how those systems function. This lack of insight can lead to unintended legal and ethical consequences when algorithmic decisions replicate or amplify bias.

See: AI Hiring Discrimination, Lawsuits & Accountability | Learn & Work Ecosystem Library

Refers to structured routes that connect entry into a job by adult learners and workers, often through skills-based hiring that can open pathways to better paying entry-level roles for workers without a college degree. These routes also connect adults and workers to continued education, training, and credential opportunities that support long-term career advancement.

Extended pathways often build on prior learning and work experience; address common barriers faced by adults; and link employers, educators, and workforce partners to promote sustained mobility and equitable career growth.

This approach is founded in the recognition that:

• Many adult learners and workers are not starting their career search with a blank slate: many possess valuable skills and knowledge from community college, military service, on-the-job training, and life experiences.
• Career “dead-ends” can be addressed by providing structured opportunities for reskilling, upskilling, and credential attainment beyond the initial point of hire.
• Employers, educators, and workforce organizations can strengthen internal worker mobility, improve retention, and promote a more inclusive, equitable workforce through extended pathways.

The public service arm of U.S. land-grant colleges and universities that puts research into practical use in local communities. The Extension Service was established in 1914 through the Smith-Lever Act (7 U.S.C. 341 et seq.).  It functions as a partnership among federal, state, and local governments, with campus-based faculty members and locally based educators helping people, businesses, and communities solve problems, develop skills, and collect input to prioritize future research at the land-grant colleges and universities to meet local needs. Extension operates in all 50 states, six U.S. territories, and the District of Columbia — and operated by each jurisdiction’s land-grant university or universities. Extension has offices in or near most of the nation’s 3,000 counties.

With its expansive mission, historical impact, and strong ties to local communities, Extension plays a major role in community, economic, and workforce development (CEWD). Cooperative Extension is the nation’s largest provider of noncredit education.

An F-1 Visa is a type of visa in the United States that allows international students to come to the U.S. to study fulltime at an accredited college, university, high school, language school, or other approved academic institution. To qualify for an F-1 visa, a student must:

• Be accepted by a U.S. school that is authorized to enroll international students.
• Prove they have enough money to pay for their education and living expenses while in the U.S.
• Show that they plan to return to their home country after finishing their studies.

The F-1 visa is the most common visa for international students. It opens doors for students around the world to access U.S. education and, in many cases, gain practical work experience through programs like Optional Practical Training (OPT).

Optional Practical Training (OPT) is a program in the U.S. that allows international students who are studying at U.S. colleges or universities on an F-1 visa to work in jobs related to their field of study for up to 12 months during or after finishing their degree. Students in science, technology, engineering, or math (STEM) fields may be eligible for a 24-month extension, for a total of up to 36 months of work authorization.

Benefits of OPT are fourfold:

1. Gives international students the chance to apply what they’ve learned in the classroom to jobs in the U.S., helping them build valuable skills and increase their chances of future employment—whether in the U.S. or their home country.
2. Offers U.S. employers access to a highly skilled, diverse, and motivated talent pool.
3. From a workforce development perspective, OPT can contributes to innovation and economic growth by filling skill gaps in key industries, especially in STEM fields.
4. OPT offers the opportunity to strengthen ties between higher education and employers, showing how international education can support labor market needs.

OPT has existed since the early 1990s in its current form but has roots in earlier immigration policy that allowed student employment under certain conditions. The original purpose was to provide a bridge from study to work without requiring students to immediately change their visa status. The 24-month STEM extension was introduced in 2008 and revised in 2016 to expand access and strengthen oversight.

In recent years, OPT has faced political and legal scrutiny. Some critics argue it takes jobs from U.S. workers, while supporters emphasize its role in attracting global talent and maintaining U.S. competitiveness. Proposals to change or limit the program have surfaced periodically, but major changes have not yet occurred. However, the future of OPT is uncertain, and it continues to be a subject of debate in immigration and workforce policy.

See:  Curricular Practical Training (CPT) | Learn & Work Ecosystem Library

Fair chance hiring is designed to give people with some type of criminal record greater employment opportunities.

A fair chance employer bases employment decisions on the applicants’ qualifications rather than their criminal records, and (1) does not include questions about arrests or convictions on its application, (2) does not inquire about criminal convictions before extending a conditional offer of employment, and (3) while can still consider criminal convictions, must first individually assess the conviction as it directly relates to the job position plus how much time has passed since the conviction. If the employer decides against hiring the applicant, it must complete an adverse action process required under the Fair Credit Reporting Act (FCRA). The process includes sending a pre-adverse action notice to the applicant and identifying the conviction that makes the employer want to deny employment. This process includes a copy of the background check report and gives the applicant five days to challenge the information or present mitigating evidence. The employer sends a final adverse action notice to the applicant if making a final decision not to hire the applicant and provides a copy of their rights under the FCRA and state laws.

As of January 2025, 37 states and 150 municipalities and counties in the U.S. had enacted fair chance or ban-the-box laws. (Ban the Box laws are aimed at removing the check box that asks applicants about their potential criminal record from employer hiring applications.)

Related Federal laws:

• Congress passed the Fair Chance to Compete for Jobs Act of 2019 as a part of the National Defense Authorization Act for Fiscal Year 2020, which was signed into law in Dec. 2020. The law prohibits federal agencies from awarding government contracts to federal contractors that require criminal background information before extending conditional offers of employment.
• Title VII of the 1964 Civil Rights Act prohibits discrimination in all aspects of employment that is based on the protected characteristics of employees or applicants. This includes giving equal employment opportunities to people with convictions.

Fair use is a legal doctrine in U.S. copyright law that permits limited use of copyrighted material without permission from the rights holder for purposes such as education, research, scholarship, commentary, criticism, and news reporting. Fair use determinations are context-specific and rely on an analysis of four factors: (1) the purpose and character of the use, (2) the nature of the copyrighted work, (3) the amount used, and (4) the effect on the market value of the original work.

The growth of artificial intelligence (AI) has complicated traditional understandings of fair use, raising new legal and institutional questions about the use of copyrighted materials.  In digital learning, research, and AI-enabled environments, fair use plays a critical role in enabling access, innovation, and knowledge sharing while balancing the rights of content creators. AI complicates fair use practices because it raises unresolved questions such as:

• Whether training on copyrighted works constitutes fair use?
• Whether AI outputs are “transformative”?
• Who bears responsibility when outputs resemble copyrighted material?
• How should attribution, licensing, and compensation work at scale?

Courts have historically emphasized human purpose and transformation. AI introduces non-human intermediaries, probabilistic reuse, and scale far beyond traditional educational copying. This matters for educators, researchers, libraries, and publishers because fair use is no longer just about what humans copy: it is about what systems ingest, how outputs are generated, and how institutions manage risk, disclosure, and compliance.

A growing threat facing companies globally are jobseekers who are not who they say they are, using AI tools to fabricate photo IDs, generate employment histories, and provide answers during interviews.  An imposter candidate who is hired can install malware to demand a ransom from a company, or steal its customer data, trade secrets, or funds. Estimates are that by 2028 globally, the rise of AI-generated profiles will result in 1 in 4 fake job candidates. Some companies report seeing individuals using fake identities, fake faces, and fake voices to secure employment, even going so far as doing a face swap with another individual who shows up for the job.

To address this problem, there is an emerging industry of identity-verification companies to weed out fake candidates.

Fake jobs refer to online listings for roles an employer does not intend to hire for. A Survey by Resume Builder found that as many as 4 in 10 companies (among 649 managers surveyed) indicate they have posted a “fake job listing” in 2024, and 3 in 10 companies are currently advertising for a role that is not real.  According to the survey, hiring managers are most likely to post fake openings for entry-level and mid-level roles.

Common reasons for posting fake jobs:

• Advertising nonexistent openings may have a positive impact on a company’s revenue by making it appear as if the company is growing faster than it is.
• The practice may increase employee morale by making over-extended staffers feel their workload will soon be alleviated.
• The practice may boost productivity by making employees feel las if they are replaceable and have to prove themselves against a potential newcomer.
• Hiring managers may keep fake listings up in order to collect resumes to keep on file for later.

Refers to the intentional, ongoing collaboration between families, educators, schools, and communities to support student learning, development, well-being, and educational decision-making. Unlike traditional models of family involvement—often limited to participation in K-12 school events or receiving information—family engagement emphasizes reciprocal partnerships in which families are recognized as essential partners, co-creators, and assets in educational systems.

Family engagement encompasses a wide range of practices, including shared goal-setting for student learning, two-way communication between families and educators, participation in school governance and planning, culturally responsive collaboration, and alignment between home, school, and community learning environments. Research across K–12 and early childhood education demonstrates that meaningful family engagement is associated with improved academic outcomes, stronger student well-being, increased attendance, and enhanced trust between schools and communities.

The concept gained prominence in education policy and research in the late 20th and early 21st centuries as scholars and practitioners shifted away from deficit-oriented models of “parent involvement” toward partnership-based approaches recognizing families’ lived experiences, cultural knowledge, and leadership roles.

Recent global initiatives emphasize family-centered education systems, which position students, families, educators, and communities as collaborative partners in shaping educational visions and practices. Global initiatives are increasingly advancing family-centered education systems. For example, cross-national collaborations supported by the Brookings-led Center for Universal Education have tested partnership strategies across countries such as Brazil, Bangladesh, Colombia, South Africa, Sierra Leone, and Zanzibar, highlighting the importance of relational trust, educator preparation, and inclusive governance structures in meaningful family engagement.

Key elements commonly associated with effective family engagement include:

• Reciprocal communication and relationship-building
• Inclusive participation across diverse family backgrounds and languages
• Integration of family perspectives into policy and governance structures
• Educator training and institutional support for partnership-building
• Recognition of families as contributors to learning both inside and outside school settings

See: Morris, E., and Hoysala, R. (2026, February 10). Moving from family involvement to engagement in education.  About Us | Brookings

Refers to the income a family needs to cover minimum necessary expenses such as food, childcare, medical care, housing, and transportation, in a given geographical area.  The related term, living wage, generally refers to the income for a single individual to live on, but not necessarily sufficient to support a family.

A standardized occupational classification used by the United States federal government to organize federal civilian positions into occupational categories based on the nature of the work performed, the qualifications required, and the responsibilities associated with the role. Each job series is identified by a four-digit numeric code (e.g., 0343 for Management and Program Analysis, 2210 for Information Technology Management) and is maintained by the U.S. Office of Personnel Management (OPM).

Federal job series support workforce management and personnel administration across federal agencies. They help classify positions consistently government-wide, establish qualification requirements, align pay structures within the General Schedule (GS) and other federal pay systems, guide hiring and promotion processes, and support workforce planning, reporting, and data analysis.

Job series descriptions typically include occupational coverage, distinguishing characteristics, and minimum qualification standards such as required education, specialized experience, or certifications.

OPM currently maintains more than 400 job series, organized into approximately two dozen broader occupational groups or job families such as:

• administrative and management occupations
• business and financial operations
• information technology and cybersecurity
• engineering and science
• medical and health services
• legal occupations
• program and policy analysis.

Job series and related qualification standards are updated periodically to reflect evolving workforce needs, occupational practices, and federal policy priorities.

Federal job series are part of a broader occupational classification ecosystem and are often used alongside related frameworks such as OPM job grading and qualification standards. Comparable classification systems exist outside federal employment. For example, the Occupational Information Network (O*NET) provides a national database describing work activities, skills, knowledge, and competencies across occupations in the U.S. economy, while the Standard Occupational Classification (SOC) system is used for federal labor market data reporting. These systems can be cross-referenced with federal job series to support workforce analysis, career navigation, and translation of skills between federal and non-federal employment sectors.

As described by U.S. Department of Labor Office of Federal Contract Compliance Programs (OFCCP), U.S. Department of Labor Veterans’ Employment and Training Service (VETS), U.S. Department of Justice Civil Rights Division (CRT), and U.S. Equal Employment Opportunity Commission (EEOC), multiple laws prohibit employment discrimination against applicants and employees:

• The Americans with Disabilities Act of 1990 makes it illegal for employers to discriminate against qualified job applicants and employees based on their physical or mental disabilities, including failing to provide a reasonable accommodation to a qualified employee or applicant.  Section 501 of the Rehabilitation Act of 1973 applies the same standards to federal agency employers.
• Section 503 of the Rehabilitation Act of 1973 prohibits federal contractors and subcontractors from discriminating in employment against individuals with disabilities, including failing to provide a reasonable accommodation to a qualified employee or applicant.  It also requires employers to take affirmative action to recruit, hire, promote, and retain these individuals.
• Title VII of the Civil Rights Act of 1964 prohibits discrimination based on race, color, national origin, sex (including pregnancy, sexual orientation, and gender identity), and religion.
• The Equal Pay Act of 1963 requires that men and women in the same workplace be given equal pay for equal work.
• The Age Discrimination in Employment Act of 1967 protects people who are 40 or older from discrimination because of age.
• The Genetic Information Nondiscrimination Act of 2008 prohibits discrimination based on genetic information (which includes family medical history).
• The Immigration and Nationality Act’s Anti-Discrimination Provision prohibits discrimination based on citizenship, immigration status, and national origin for certain employers not covered under Title VII).
• Executive Order 11246 prohibits federal contractors and subcontractors from discriminating on the basis of race, color, religion, sex, sexual orientation, gender identity, or national origin and requires affirmative action to promote equal opportunity.  Contractors also are prohibited from discriminating against applicants or employees because they inquire about, discuss, or disclose their compensation or that of others, subject to certain limitations.

Employers are prohibited from retaliating against for asserting their rights under these laws or otherwise participating in protected activity (e.g., filing a complaint or participating in an investigation).

For military protections see: Federal Laws Prohibiting Discrimination in Employment based on Service Member or Veteran Status | Learn & Work Ecosystem Library (learnworkecosystemlibrary.com)

As described by U.S. Department of Labor Office of Federal Contract Compliance Programs (OFCCP), U.S. Department of Labor Veterans’ Employment and Training Service (VETS), U.S. Department of Justice Civil Rights Division (CRT), and U.S. Equal Employment Opportunity Commission (EEOC), two federal laws prohibit discrimination in employment based on status as a service member or veteran:

• The Uniformed Services Employment and Reemployment Rights Act (USERRA) prohibits civilian employers from discriminating based on present, past, and future military service.  It also entitles service members, such as National Guard members and reservists, who leave their civilian employment to perform covered military service to prompt reemployment with their pre-service employer following the completion of their duty.  Service members who meet the eligibility criteria for reinstatement must be promptly reemployed with their pre-service employers with the seniority, status, and rate of pay they would have obtained with reasonable certainty had they remained continuously employed.
• The Vietnam Era Veterans’ Readjustment Assistance Act of 1974 (VEVRAA) prohibits federal contractors and subcontractors from discriminating in employment against protected veterans and requires employers take affirmative action to recruit, hire, promote, and retain these individuals.  This protection against discrimination extends to spouses and other individuals the contractor knows have a relationship or association with a protected veteran.

Additional laws prohibit employment discrimination against applicants and employees for other reasons veterans and service members may face:

• The Americans with Disabilities Act of 1990 makes it illegal for employers to discriminate against qualified job applicants and employees based on their physical or mental disabilities, including failing to provide a reasonable accommodation to a qualified employee or applicant.  Section 501 of the Rehabilitation Act of 1973 applies the same standards to federal agency employers.
• Section 503 of the Rehabilitation Act of 1973 prohibits federal contractors and subcontractors from discriminating in employment against individuals with disabilities, including failing to provide a reasonable accommodation to a qualified employee or applicant.  It also requires employers to take affirmative action to recruit, hire, promote, and retain these individuals.
• Title VII of the Civil Rights Act of 1964 prohibits discrimination based on race, color, national origin, sex (including pregnancy, sexual orientation, and gender identity), and religion.
• The Equal Pay Act of 1963 requires that men and women in the same workplace be given equal pay for equal work.
• The Age Discrimination in Employment Act of 1967 protects people who are 40 or older from discrimination because of age.
• The Genetic Information Nondiscrimination Act of 2008 prohibits discrimination based on genetic information (which includes family medical history).
• The Immigration and Nationality Act’s Anti-Discrimination Provision prohibits discrimination based on citizenship, immigration status, and national origin for certain employers not covered under Title VII).
• Executive Order 11246 prohibits federal contractors and subcontractors from discriminating on the basis of race, color, religion, sex, sexual orientation, gender identity, or national origin and requires affirmative action to promote equal opportunity.  Contractors also are prohibited from discriminating against applicants or employees because they inquire about, discuss, or disclose their compensation or that of others, subject to certain limitations.

Employers are prohibited from retaliating against for asserting their rights under these laws or otherwise participating in protected activity (e.g., filing a complaint or participating in an investigation).

The Federal purpose license is a policy in existing federal regulations: “The Federal awarding agency reserves a royalty-free, nonexclusive and irrevocable right to reproduce, publish, or otherwise use the work for Federal purposes, and to authorize others to do so.”

A 2022 White House directive would make a change to this policy in response to calls for more open access expansion; i.e., making federally funded research freely available to the public immediately after publication of the research. If the directive goes forward, authors who use grant funding to produce research will be required to deposit their work into agency-designated public-access repositories as soon as it is published. That process would eliminate the current option for authors or their publishers to place a 12-month embargo on public access to government-funded research publications (rule in place since 2013). The purpose of lifting the embargo as described in the 2022 White House memo (known as the Nelson memo) is to promote “equity and advance the work of restoring the public’s trust in Government science, and to advance American scientific leadership.” These changes will impact authors, their institutions, and the offices that deal with research funding and grant compliance. Academic libraries, especially at doctoral-granting institutions, will play a key role in advancing open access through multiple pathways and engaging authors in scholarly publishing and research data management.

The Nelson memo calls for implementation of the policy change by 2026.  Groups supporting the change include open-access advocates and library groups. They believe the change will provide grant recipients with a clear understanding of their obligations as authors and facilitate better compliance with funder requirements. Opponents include many members of Congress and many academic publishers who contend that the new policy would limit researchers’ ability to maintain control of their published work and would cut into the multi-billion-dollar academic publishing industry’s profit margins.

The current academic publishing industry’s business model relies on authors’ willingness to submit their work for free—or even pay to publish it—and the publisher’s ability to sell that research to academic libraries through expensive journal subscriptions. The Nelson memo raises a dilemma for publishers: determine how the industry can and should respond to increasing calls for open-access expansion by adapting their business models and aligning more closely with what authors and funding agencies want; or staying with the existing model that depends on retaining as much exclusive rights as they can for authors’ articles.

Are used to store and transport data. Many open data standards are expressed in one or more of the following:

• JSON – a lightweight data-exchange and file format easily understood by computers and humans.
• JSON-LD – Linked Data (LD). enabling a network of standards-based, machine-readable data across the web.
• RDF-Resource Description Framework which includes a subject, object, and their relationship. Each piece has a URL. RDF is commonly expressed in Turtle format (compact, stackable, human-friendly) and can be expressed (serialized) in JSON-LD, among other formats.
• OWL-Web Ontology Language, a complex language for taxonomies/classifications built on RDF.
• XML – Extensible Markup Language, a language and file format similar to HTML that describes data with tags that the developer defines.
• SKOS – Simple Knowledge Organization System, built on RDF/RDFS.
• YAML – Yet Another Markup Language, which can parse other formats into a human-friendlier layout.
• EDI-based standards – Electronic Data Interchange (EDI), a format to transport business documents between organizations and across industries.
• CSV-Comma-separate Values, a text file that has a specific format that allows data to be saved in a table structured format.

Refers to data that typically includes an individual’s wage and salary information, bank account information, savings information, and credit score.

Refers to the structures, processes, policies, and accountability practices through which an organization oversees, documents, and justifies financial decision-making and resource management. It ensures that financial activities align with organizational mission and strategy, comply with legal and regulatory requirements, protect assets, manage risk, and maintain transparency for stakeholders.

Effective financial governance goes beyond compliance to establish continuous, evidence-based oversight, including documentation of decisions, regular review cycles, defined roles and responsibilities, and systematic evaluation of financial partners and service providers. Strong financial governance enables organizations to demonstrate both what decisions were made, and how and why they were reached. This can reduce exposure to financial, operational, and reputational risk and build institutional trust.

In practice, financial governance often incorporates fiduciary governance principles which requires leaders, boards, and designated officers to act in the best interests of the organization and its stakeholders, maintain defensible records of oversight activities, interpret financial data transparently, and sustain governance processes that persist beyond individual personnel. In employment and benefits contexts, this includes documented review of vendors, fees, plan performance, and cost drivers, along with clear communication of financial decisions to leadership and participants.

Two federal consumer protection safeguards against unaffordable debt or insufficient earnings for postsecondary students took effect July 1, 2024. The Federal Financial Value Transparency Regulations, along with a revamped set of gainful-employment rules require that by the beginning of 2026, students will have to acknowledge having read information about the debt burden associated with a certificate or graduate program before enrolling. The aim of these regulations is to ensure that postsecondary education remains an engine for equal opportunity, upward mobility, and global competitiveness.

The Financial Value Transparency (FVT) Framework will give students in all programs detailed information about the net costs of postsecondary programs, and the financial outcomes they can expect. It will help prospective students understand the potential risks involved in their program choices by requiring them to acknowledge viewing this information before enrolling in certificate or graduate programs whose graduates have been determined to face unaffordable debt levels.  The framework estimates acknowledgments for an estimated 400 graduate programs that enroll about 120,000 students. The framework applies to all programs in all sectors but doesn’t affect financial-aid eligibility.  The Department of Education will post every program’s results on a pair of debt-to-earnings tests which compare graduates’ annual income as well as their discretionary income. If a program’s rates fail, incoming students will need to sign a disclosure before enrolling, acknowledging they’re aware of their prospective return. This new accountability mechanism will remain in play until the program’s rates return to a passing level, or if three years pass since the program was last notified of a failing score, whichever comes first.

The Gainful Employment Program Accountability Framework (Gainful Employment – GE) rule will protect approximately 700,000 students a year from career training programs that leave graduates with unaffordable loan payments or earnings no better than what someone who did not pursue postsecondary education earns in their state. Under the rule, the Department assesses whether programs offered by private for-profit institutions and certificate programs at all types of colleges meet the statutory requirement to prepare students for gainful employment in a recognized occupation using two separate measures:

• Share of annual earnings typical graduates need to devote to paying their debt (i.e., “debt-to-earnings ratio”) must be less than or equal to 8% or less than or equal to 20% of their discretionary earnings (defined as annual earnings minus 150% of federal poverty guideline). This metric captures whether a program’s debt is affordable.
• At least half of graduates have higher earnings than a typical high school graduate in their state’s labor force who never enrolled in postsecondary education. This “earnings premium” assesses whether the program enhances its students’ earnings potential.

Refers to a spectrum of learners who meet different combinations of parents and caregivers, whether they attended college or graduated, what degree they completed, and more factors. Because there are significant differences in knowledge and other resources between students with one parent who earned a bachelor’s degree and students whose parents, grandparents, and great-grandparents went to college, multiple definitions are often used to identify first-generation students:

• Neither parent earned a bachelor’s degree (federal definition)
• One parent earned a bachelor’s degree
• No bachelor’s degrees among living parents (focus on those who can provide support to the student)
• No bachelor’s degrees among caregivers (considers others in the household beyond biological parents such as stepparent)
• No domestic bachelor’s degree among caregivers (degrees from other countries may be less relevant in helping students in the U.S. higher education system)
• No bachelor’s degrees earned by caregivers before the student was born (excludes those who earned degrees more recently and may not yet have received some of the more socioeconomic benefits of a college degree)
• No associate degrees by either parent
• No college attendance by either parent

Refers to individuals who are actively looking for their first job. The term emphasizes the lack of previous employment experience, making it a common descriptor for new school graduates or those transitioning into the workforce from other life circumstances.

In higher education, refers to the achievement and positive outcomes of learners during their first year at a college or university. This includes their academic performance, social integration, emotional well-being, and retention rates. Students who experience first-year success are more likely to feel confident in their abilities, develop a sense of belonging to the academic community, and persist in their educational journey. Examples of efforts that promote first-year success:

• Freshman Orientation Programs: Some institutions offer comprehensive orientation programs (workshops, social activities) to familiarize incoming learners with campus resources, academic expectations, and support services.
• First-Year Experience Courses: Some institutions offer courses specifically tailored to the needs of first-year learners. These typically focus on improving core academic skills (e.g., computer literacy, study skills, library skills), time management techniques, seeking learner supports (career and academic advising, childcare, financial aid assistance, tutoring), and fostering a sense of community among faculty and peers (e.g., use of freshmen learning communities/cohort models). They often provide personalized support and guidance to help learners thrive academically and socially in their first year.

Refers to a budgeting practice in which an organization, program, or public agency receives the same nominal dollar amount in a new fiscal year as it received in the previous year, with no increase for inflation, enrollment growth, expanded services, or rising operational costs.

Although flat funding may appear to maintain financial stability because total appropriations do not decline, in real (inflation-adjusted) terms it typically results in a reduction in purchasing power. Over time, sustained flat funding can require organizations to reduce services, delay innovation, limit hiring, postpone infrastructure investments, or reallocate resources internally to absorb cost increases.

In education and workforce systems, flat funding can affect institutional capacity, student support services, tuition levels, staffing, technology investments, and program development. Because many public institutions operate within multi-year cost structures (e.g., personnel contracts, facilities maintenance, financial aid commitments), flat funding may function as a de facto budget cut when expenses rise faster than appropriations.

Flat funding differs from:

• Budget cuts, which involve a reduction in nominal dollars.
• Level funding with adjustments, which includes inflationary or enrollment-based increases.
• Performance-based funding, which ties appropriations to outcomes or metrics.

Within the learn-and-work ecosystem, flat funding can influence the pace of innovation, credential development, workforce alignment efforts, and the capacity of institutions to respond to shifting labor market demands.

Refers to a work arrangement designed to bridge the transition to fulltime retirement. Flextirement is characterized by reduced workloads (typically 10, 20, or 30 hours per week), flexible hours, and a phased approach to retirement. Flextirement offers employers a cost-effective way to attract experienced employees and retain them and their institutional knowledge. It offers workers (flextirees) the flexibility to adjust to an income shift and explore the preparation to full retirement.

Refers to a pedagogical model and instructional strategy that reverses, or “flips”, the typical cycle of lecture attendance and homework completion. This technique increases learner engagement and the effective use of classroom time by assigning exploration of new content (such as viewing recorded lectures online) as pre-class homework, and using in-class time to engage in live problem-solving.

This model shifts the passive acquisition of knowledge outside of the classroom, and improves engagement through active learning inside the classroom. Learners in a flipped classroom have immediate access to instructor feedback when completing tasks, unlike learners who complete traditional homework in isolation.

Alternate term: Flipped course

For-profit postsecondary institutions (also known as proprietary institutions) rely on investors and students who pay the costs of educational and training programs. The institution makes a profit in the delivery of its services. Many vocational and technical schools, career colleges, and online universities are for-profit institutions; they typically offer career-oriented programs such as business, technology and coding boot camps, culinary arts, health care, and visual arts. The growth of for-profit education has been fueled by government funding as well as corporate investment, including private equity.

As workplaces become less traditional, some companies are turning to the practice of hiring employees for a fraction of the time fulltime employees would work. This hiring management strategy, usually applied to executive-level roles, enables companies to diversify their employment by bringing on talent on a quarterly, monthly and even weekly basis; make shorter-term hiring decisions; attract valuable talent and benefit from their skills and expertise without paying the same costs as a fulltime employee; and expand staffing capacity. From the employee’s standpoint, the employee could be hired to work for multiple organizations throughout the week. In these situations, employees function like a freelancer or contractor.

Refers to forged, altered, fake, or misrepresented credentials such as degree, diploma, certification, or other official documentation. Examples include:

• Presenting a credential from a recognized institution that has been falsified.
• Presenting a credential from an unrecognized institution (one that may be made up or a diploma or degree mill).
• Falsely claiming having earned a credential on a letter of application, resume, e-portfolio, and/or during an interview.
• Falsely indicating the level or outcome of a credential.
• Presenting an expired credential such as an industry certification or license.

Fraud in the credentialing marketplace can undermine the credibility of educational and professional institutions, lead to unqualified individuals being hired, and create an unfair advantage for those involved in the deception. To address issues of fraud, governmental entities, employers, higher education institutions, and others are implementing measures such as improving verification processes; increasing awareness about the problem; establishing networks and databases to share known instances of credential fraud; and instituting laws and regulations to penalize the use of fraudulent credentials.

See Topic: Fraudulent Credentials

See: Degree Mills / Diploma Mills / Honorary Degrees | Learn & Work Ecosystem Library (learnworkecosystemlibrary.com)

The Free Application for Federal Student Aid or FAFSA is a form completed by current and prospective college students in the U.S. to determine their eligibility for student financial aid. Completing FAFSA is a critical step in accessing federal and state financial aid programs. Both traditional-age and adults are required to complete the FAFSA if they are applying for financial aid assistance.

Programs in states and local “promise programs” that are designed to make college more affordable and accessible. They typically cover the cost of tuition and fees for eligible students, with the goal of ensuring that higher education is attainable without the burden of unmanageable debt. Programs vary in their design. Some have merit or income requirements; others provide additional support for stipend for books and other expenses. There are currently more than 400 state and local “promise programs” across the nation.

The College Promise movement has gained momentum across the U.S. in the past decade. These programs typically commit to fund a college education and provide student supports for eligible students on their path to earning a college degree, postsecondary certificate, credits that transfer to a 4-year university, or competencies needed for success in college, career, and community. There are intermediaries such as the non-profit College Promise that supports programs like these. The Catalog of Local and State College Promise Programs outlines  key features of these programs, including eligibility requirements, the colleges involved, support services provided, and whether fulltime attendance is required to receive the Promise.

Each state or local Promise Program has different features; for example:

• Since 2015, Tennessee has covered full tuition and fees for any high school graduate who wishes to attend a state-funded two-year college under its Tennessee Promise initiative.
• The New Mexico Opportunity Scholarship Act (2020, 2021) makes college tuition-free for most New Mexicans. The program includes both recent high school graduates and returning adult learners; accommodates part-time students; includes career training certificates, associate degrees and bachelor’s degrees; and covers summer courses. In addition to covering full tuition and fees at in-state public colleges and universities, the scholarship lets students stack federal aid such as Pell Grants, local scholarships, and private scholarships so that they can use these funds to pay for books, materials, housing, food, transportation, childcare and other college costs.
• Vermont launched the 802 Opportunity grant in 2021. The program initially offered free tuition at the Community College of Vermont for students from families with an annual household income under $50,000. In 2023, legislators voted to expand eligibility for the scholarship, to cover those with incomes up to $75,000.
• Michigan established The Michigan Reconnect program in 2021 to offer free community college tuition for adults 25 and older with no college credentials.
• Connecticut offers the Pledge to Advance Connecticut, or PACT, that applies to first-time community college students taking at least 6 college credits.
• In 2024, Pikes Peak State College (Colorado) launched First Nations Promise, for Native students who attend Pikes Peak State. The program is designed for members of federally recognized American Indian tribes residing in El Paso, Teller, or Elbert counties. The funding can be applied to direct costs like tuition and student fees. Academic coaches will work closely with First Nations Promise scholars to offer resources that support students in their time at the college.

Refers to individuals who work independently (self-employed), offering services to clients on a project-by-project basis. Freelancers are paid for their knowledge or expertise in fields such as writing, graphic design, programming, coding, marketing, social media management, tutoring, consulting, and more. Freelancers often work remotely and communicate with clients through online platforms, email, and phone. Benefits for freelancing include the flexibility to choose work projects and work schedules.

Some higher education institutions are incorporating freelance work into their curriculum, along with increasing their offerings of microcredentials, certificates, digital badges, and other short-term credentials to students’ array of options. In this context, freelancing is an additional type of work-based learning (other types are job shadowing, apprenticeships, cooperative education, service learning, internships, and career and technical education). Through freelancing, students can use skills acquired in the classroom (and outside) in the workplace, boost their resumes/portfolios, and make money.

A number of companies (e.g., Podium, Riipen, Parker Dewey, Forage, Handshake) offer digital platforms to make it easier for students and employers to connect to provide projects students can participate in for freelancing (and job simulations, microinternships, and full internships). Examples of freelancing sites for students:

• Fiverr – Clients come to the student seeking students with unique talents
• Upwork – For students with well-developed skills who are looking for serious, longer-term projects
• Freelancer.com – For students seeking to build a portfolio when starting out and needing help to find work

 See: Work-based learning – Work-based Learning | Learn & Work Ecosystem Library (learnworkecosystemlibrary.com)

Refers to the situation in which individuals are unemployed when moving between jobs or looking for a first job. A person who resigns from a job without securing another job is categorized as entering frictional employment.

Frontline workers and essential workers refer to employees whose primary duties involve direct engagement with customers, patients, or the public or those responsible for maintaining essential services, often under challenging conditions. These workers typically serve as the first point of contact in their respective industries. Their roles are fundamental in healthcare, education, retail, logistics, law enforcement, and emergency services.

Frontline workers are often called essential workers although the two terms are not synonymous: not all frontline workers are essential workers, though some essential workers are frontline workers.

• Frontline workers provide vital services to the general public. They hold roles typically requiring close interaction with the public or with their colleagues. Examples include landscapers, construction workers, hotel employees, electricians, plumbers, and mechanics.
• Essential workers also provide vital services. They sustain critical infrastructure, provide services and operations, and ensure community well-being in sectors like healthcare, transportation, logistics, food and agriculture, law enforcement, public safety, and more. Examples include teachers, public health officials, social services administrators, transportation planners, and emergency management coordinators.
• Many worker roles overlap between frontline and essential categories depending on the context, such as doctors, nurses, police officers, firefighters, grocery store employees, food preparation and delivery employees.

Frontloading embedded non-degree pathways is a method to address workers and learners who are looking to re- or upskill quickly to transition jobs. It entails reordering degree pathways to frontload embedded non-degree credentials.

According to iCIMS (provider of talent acquisition software), for talent acquisition teams, full cycle recruiting offers an end-to-end hiring process that delivers results, without frustrating candidates or overwhelming hiring managers. In today’s competitive talent market, it delivers a more efficient and more human approach to hiring.

Philanthropy and investment firms form funder collaboratives to increase their impact (also called “multiplier impacts”). By pooling resources, funder collaboratives can get more money into a space, rather than just being the sum of donations that would have otherwise gone to the same issue. Working together, funders are able to put larger amounts of money behind impactful ideas, take a wider view of an issue, and take bigger risks.

When a funder collaborative establishes an alliance to direct money into a space, it can also get other philanthropies to put new funding into the cause. By combining larger scale and risk-taking, some funder collaboratives have been able to introduce innovative, effective investment programs that governments and more traditional foundations would not think to support.

Funder collaboratives vary in their legal, financial, and operational structures but generally are structured as “pooled funds” or “co-granting.”

• Pooled funds are funder collaboratives where donors put their money into a shared pot, and then engage in internal processes to figure out where that funding should go.
• Co-granting is where donors do not have any formal commitments to provide funding but are engaged in the process of sourcing investments.

Examples:

• The END Fund was founded in 2012 by philanthropists and investment firms interested in ending neglected tropical diseases, such as trachoma and roundworms. The END Fund has 7,000 investors from 68 countries.
• Since 2019, Farming the Future (UK-based funder collaborative) has helped foundations invest in innovative food nonprofits and agricultural solutions. The collaborative focuses on making the industrial food system more sustainable.
• Blue Meridian Partners (BMP) is a funder collaborative whose platform allows philanthropists and funders to invest in social change together—in areas such as youth issues affecting all Americans, including health, education, and justice. BMP designs high-impact strategies worth tens of millions of dollars and then conducts “capital calls” of a pool of “general partners” to raise the required funds for the investment. Among its partners are multibillion-dollar endowments.

Funding models refer to the ways in which higher education institutions acquire revenue to operate. As described by Ithaka S+R, there are significant differences between funding models for public two- and four-year institutions in the United States. The three largest revenue sources for four-year institutions are tuition and fees (20%); government appropriations (18%), and sales and services from hospitals (15%). Community colleges receive nearly half of their revenue from government appropriations, the majority from state governments. Non-operating grants and contracts, including revenue from Pell grants, represent 18% of total revenues, and tuition and fees comprise an additional 16% of revenue. Funding models from state governments for public higher education institutions typically include Incremental Funding, Formula Funding, Performance-based Funding (PBF), and State Financial Aid Programs.

Interest is growing in understanding both the evolving, future nature of work and the skills, knowledge, and credentials workers need to succeed within it. These interconnected concepts offer complementary perspectives on how work and employability are changing in response to technology, economic shifts, and societal trends.

• Future of Employment: Focuses on individuals’ ability to obtain, maintain, and advance in meaningful work. It emphasizes the skills, adaptability, credentials, and lifelong learning strategies that help workers remain relevant as the labor market evolves.

• Future of Work: Focuses on the evolving structure, organization, and nature of work itself. It examines how jobs, tasks, workplace models, and labor market dynamics are changing, often driven by technology, automation, artificial intelligence, and shifting work norms.

Refers to the projection of how work will get done in the future, often viewed over a ten-year timeline. “FOW” thinking typically incorporates the impact of technological advances, changing demography, increasing globalization, and market forces on the work we do and how we do it. Most FOW studies note the proliferation of low wage work, the digital divide, workforce shortages, and increased workforce transitions as likely characteristics of the work of the future, for which solutions will be needed.

An employment situation in which a person receives steady work and payment from the employer that allows for self-sufficiency.

The Higher Education Act (HEA) requires that all career education programs receiving federal student aid “prepare students for gainful employment in a recognized occupation.” On May 19, 2023, the US Department of Education Secretary published proposed new regulations to promote transparency, competence, stability, and effective outcomes for students in the provision of postsecondary education – and invited comments to the proposed regulations (comment period closed June 20, 2023).  The regulations would make improvements in six areas of gainful employment (GE); financial value transparency; financial responsibility; administrative capability; certification procedures; and Ability to Benefit (ATB). (Federal Register)

The revised Gainful Employment Program Accountability Framework (Gainful Employment-GE) rule took effect July 1, 2024. Under this rule, the Department assesses whether programs offered by private for-profit institutions and certificate programs at all types of colleges meet the statutory requirement to prepare students for gainful employment in a recognized occupation using two separate measures.

• Share of annual earnings the typical graduates need to devote to paying their debt (“debt-to-earnings ratio”) must be less than or equal to 8%, or less than or equal to 20% of their discretionary earnings (defined as annual earnings minus 150% of the federal poverty guideline). This metric captures whether a program’s debt is affordable.
• At least half of graduates have higher earnings than a typical high school graduate in their state’s labor force who never enrolled in postsecondary education. This “earnings premium” assesses whether the program enhances its students’ earnings potential.

Refers to any online material that is accessible only after a user takes a specific action, such as filling out a form, registering for an account, or paying a subscription fee. This content is typically not freely available to the general public and is used by publishers, organizations, or platforms to collect user information or monetize access.

Common gating mechanisms include:

• Requiring contact details (name, email, organization)
• Subscription or membership payment
• Institutional or enterprise login
• Limited free access followed by a paywall (e.g., “read 5 articles free”)

Related terms for subscription-based or restricted content include:

• Paywalled:  Access requires payment — common for newspapers and academic journals
• Subscription-based: Content is available only to individuals or organizations with a paid membership
• Premium content: Special or enhanced material reserved for paying users
• Licensed content: Access is granted through institutional or organizational licenses (e.g., university libraries accessing JSTOR or ProQuest)
• Behind a registration wall: General term meaning limited to a specific audience — may include password-protected, member-only, or time-limited access
• Soft paywall:  Allows partial access (e.g., read 3 articles for free, then must subscribe). Often used by news outlets

Awareness of “gating” is important for understanding who can access information and under what conditions, especially in the learn-and-work ecosystem, where information about workforce development, credentials, labor market data, and education innovations may be critical to navigating the ecosystem well.

See Topic Brief: Gated Content | Gated Information | Learn & Work Ecosystem Library

A gateway course is the first credit-bearing college-level course in a program of study. These courses commonly refer to the requirements of a degree program and may be called introductory courses or prerequisites. Each student majoring in a given discipline generally passes through gateway courses. Examples include introductory courses or prerequisite courses required to complete before moving forward in majors like Business, Chemistry, and Psychology. Where colleges have particular mathematics and/or foreign language requirements for graduation, gateway courses may also include Algebra, English Composition, or Spanish I or other foreign languages.

Research has found that underrepresented college students are disproportionately held back by gateway courses, and this leads to lower graduation rates.

GED or General Educational Development Test (also can mean General Educational Diploma, Graduate Equivalency Degree, or General Equivalency Diploma) is an alternative to a high school diploma for people who did not complete high school or do not possess a high school completion credential. The GED is obtained by passing a series of standardized tests that an individual takes to assess whether or not the individual has a high school level of education.

The GED Testing Service, in partnership with the American Council on Education, began offering the GED exam nationwide in the early 1940s.  As the Official GED website, GED Testing Service offers a variety of online GED test prep materials available in various formats: online, in-print, and in Spanish or English.

Several services provide products to prepare for GED testing. Products typically include courses or guides for studying for the Language Arts, Reading, and Math sections of the GED exam; tutoring; practice tests, and personalized learning plan.

Refers to AI (artificial intelligence) able to generate text, images, or other media in response to prompts. Generative AI models process large data sets of natural language, code language, and images to create new content in these forms (natural language, code language, images) and other data forms. Examples include ChatGPT, Bing Chat, and Bard. Many applications use generative AI in the fields of art, marketing, writing, software development, product design, healthcare, finance, gaming, fashion, and education. In education, uses are increasing in teaching, learning, student support services, and administrative supports.

Related terms include machine learning (ML) and deep learning.

Refers to strategies used to improve how content is discovered, interpreted, summarized, and cited by generative artificial intelligence (AI) systems which include large language models and AI-powered search and assistant tools. GEO builds on rather than replaces traditional Search Engine Optimization (SEO). While GEO is gaining importance, SEO remains foundational but is no longer sufficient—it is being absorbed into the broader information stack  with two key implications: (1) users increasingly receive answers instead of links; and (2) content visibility now depends on whether it is considered authoritative enough to be included in an AI response and whether it can be summarized accurately without distortion. SEO optimizes for indexing and ranking in search engines while GEO optimizes for selection, synthesis, and citation by generative systems.

See: Search Engine Optimization (SEO) / Key Word Optimization | Learn & Work Ecosystem Library

Refers to the study of teaching methods specifically designed for older adults, considering the unique physical, cognitive, and social changes associated with aging. Geragogy focuses on creating learning environments that are inclusive, flexible, and adaptable to these age-related changes. Coined by Kehrer (1972), the term derives from the Greek words geraios (“old” or “older adult”) and ago (“to lead” or “to guide”).

Refers to the practice by which individuals or criminal actors enroll in postsecondary courses or programs under fictitious or stolen identities (i.e., ghost students), occupy class seats, and sometimes obtain financial-aid benefits — yet never intend to participate in the courses or complete the credential. These fraudulent or fictitious enrollments exploit institutional admissions and aid systems, diverting spots and resources meant for actual learners, and can impose significant operational and financial risk on colleges and universities.

In ghosting situations:

• The “student” profile may be entirely synthetic (fabricated) or created using stolen personally identifiable information such as Social Security numbers, names, and birthdates.
• After admission and enrollment, the fake student may register for one or more courses (often online or asynchronous) but will not engage (e.g., no attendance, login, assignment submission) — or may engage just enough (sometimes using automation or AI tools) to avoid early detection.
• Once enrolled, the actor uses the enrolled status to apply for and receive financial aid (federal, state, institutional) or other funds, then disappears (or becomes dormant) before meaningful academic progress is made.
• The presence of ghosting students can displace legitimate students (by filling class seats), create distortions in enrolment data (giving a false sense of demand or success), trigger regulatory or audit risks (especially around aid disbursement), and cause identity theft harms to victims whose credentials were used without their knowledge.

In an era of increasing online- and hybrid-learning options, streamlined application processes, and large public financial-aid programs, higher education institutions are more vulnerable to ghosting students. For instance, in the U.S., community-colleges with open admission and minimal identity verification have reported large volumes of suspicious applications and ghost enrollments. The effects ripple across the learn-and-work ecosystem: financial-aid funds intended for actual upskilling and reskilling are diverted, actual learners may be blocked from essential courses or delayed, and institutional trust is eroded. In addition, organizations that rely on accurate institutional data (e.g., for workforce-development partnerships, credential tracking, analytics) may obtain misleading signals.

Note: The term should not be confused with the more colloquial term, “ghosting” when a student simply drops out or ceases participation while enrolled.

See Topic Brief: Ghosting Students (Ghost Students) | Learn & Work Ecosystem Library

Refers to a workplace behavior in which employees give the appearance of being busy—such as keeping work applications open or engaging in nonproductive tasks—while not actually performing meaningful work. The practice, reported by more than half of surveyed employees in a 2025 Resume Now study, often stems from disengagement, lack of recognition, or diminished belief in the value of one’s contributions. The Resume Now report found:

• 23% of employees admitted to walking around the office with a notebook to look busy
• 22% have typed randomly to appear engaged
• 15% have held a phone to their ear with no real caller on the other end
• 15% have kept a spreadsheet open while browsing unrelated content
• 12% have scheduled fake meetings to avoid real work
• Just 12% of respondents said they never fake productivity.

Refers to a market system in which many employers hire independent workers for short-term jobs, and temporary positions are common.  In the U.S., gig work occurs in traditional and digital workplaces; is sizable in the numbers of workers; is comprised of workers across age, education, demographic, and skill categories; and an increasing number of jobs are done remotely through digital platforms. The latter enables the independent or freelance workforce to be employed on projects anywhere in the world, and enables employers to tap a substantially larger talent pool. Notable change has occurred in the market in several industry sectors, e.g., transportation (on-demand rideshare companies, short-haul truck drivers); food service (food delivery services); construction laborers; hospitality (housekeeping services); and healthcare (home health aides). Jobs in the gig economy are often viewed as low-wage, characterized by contract firm, on-call, or temporary workers (low-wage workers whose earnings are variable, who work in nonprofessional or semiprofessional occupations, and who accept volatile hours and a short-term time commitment from the organization paying for that work). Gig work also includes high-paid project-to-project work done by professionals.

See:  Gig Workers

The Internal Revenue Service (IRS) does not distinguish between gig workers and independent contractors. It classifies these workers as “self-employed individuals” who earn income by selling goods or services on their own behalf. The relationship between a self-employed individual and a company is often project-based—the job ends when the project is completed.

Some gig workers are temporary or contingent workers, though some are fulltime, taking on a range of projects from different companies. Few gig workers use gig work as a primary source of income; many have a regular full- or part-time job and use gig work as a side job. Overall, gig workers in the U.S. work in both traditional and digital workplaces; constitute a sizable number; and cover the gamut across age, education, demographic, and skill categories.

Gig economy workers do not receive a salary as workers do in traditional part-or full-time jobs. They typically invoice clients upon completion of their work, billing via an hourly rate (the time it takes to complete a project); fixed price (charging for goods or services delivered regardless of how long it takes to complete the project); and mixed (invoicing to cover the scope of the project plus charging for the cost of raw materials or hourly costs for labor).

Since many workers in gig work are classified as independent contractors rather than employees, as independent contractors they typically lose rights to a minimum wage, overtime, a safe and healthy work environment, and protections against discrimination and harassment. They also lose access to unemployment insurance, workers’ compensation, and paid sick leave now required in many states.

See: Gig Economy

Refers to the worldwide marketplace in which individuals seek employment and employers seek workers, transcending national boundaries. It encompasses the supply of, and demand for, labor across countries, regions, and industries, driven by globalization, technological advancements, and the mobility of workers and capital.

A globally interoperable micro-credential is a digital credential designed to be issued, stored, shared, and verified across different technical platforms and regional (including cross-national) credentialing systems.  Interoperability is achieved by aligning credential data models and cryptographic verification methods (codes that keep information secret) with multiple recognized standards so that credentials are not locked into a single proprietary ecosystem.

These micro-credentials address a persistent challenge in the ecosystem: credentials that cannot travel across institutions, digital platforms, or national systems. Emerging implementations such as the Velocert Globally Interoperable Micro-credentials illustrate how technical and regulatory standards can be combined to improve portability, trust, and long-term verifiability of learning achievements. The Velocert micro-credential supports simultaneous issuance in Open Badges v3 and European Digital Credentials for Learning (EDC) formats. This approach enables a single credential to remain verifiable in both global badge-based systems and in European digital credential and wallet infrastructures.

Characteristics of these micro-credentials:

• Standards-aligned — Support multiple credential standards to enable cross-platform portability
• Machine-readable and verifiable — Use cryptographic methopds to ensure authenticity and detect tampering
• Learner-controlled — Designed for storage in digital credential wallets where individuals manage sharing
• Cross-border recognition — Align with European and international credential frameworks
• Metadata-rich — Can include learning outcomes, workload, assessment criteria, and skill taxonomies

See Initiative: Velocert Globally Interoperable Micro-credential Initiative – United Kingdom | Learn & Work Ecosystem Library

Good jobs are defined as those that provide family-sustaining pay, adequate benefits, and equal access to opportunity.

Google provides a streamlined job search feature that helps users find relevant employment opportunities more efficiently. When individuals search for jobs on Google, they are presented with a organized interface that allows them to explore listings and filter results based on their specific criteria.

Governor Executive Orders are formal directives issued by the governor of a U.S. state to direct the operations of state government. These orders carry the force of law unless overturned by legislation or judicial review. While they do not require legislative approval, they must conform to the state constitution and statutes and can be challenged in court or overridden by legislation. These orders can establish policies, create commissions or task forces, mandate interagency coordination, allocate emergency resources, or implement legislative mandates.

Governor Executive Orders differ from Federal Executive Orders in that they are issued by state governors and apply only within their respective states. These orders address state-specific issues, including workforce development, education, and economic policies, often aligning with federal initiatives but tailored to local needs.

Executive orders trace back to colonial governors and have gained formal recognition through evolving state constitutions. Traditionally used for emergency management or routine administrative actions, they have increasingly become instruments of strategic policymaking. In the 21st century—especially since 2020—their use has expanded significantly in areas such as workforce readiness, education reform, and digital infrastructure.

In the learn-and-work ecosystem, governors may use executive orders to launch statewide skills initiatives, create task forces, mandate cross-agency collaboration, and/or prioritize workforce development efforts. Executive orders can fast-track actions in areas like credential transparency, apprenticeship expansion, and digital learning records.

See Topic: Governor Executive Orders in the Learn-And-Work Ecosystem | Learn & Work Ecosystem Library

GradPLUS loans are offered by the federal government to make it more accessible and affordable for people to seek graduate-level education. To receive a loan, students must be a graduate or professional student enrolled at least half-time at an eligible school in a program leading to a graduate or professional degree or certificate; not have an adverse credit history; and meet the general eligibility requirements for federal student aid. The maximum loan amount a student can borrow is the cost of attendance (determined by the higher education institution) minus any other financial assistance the student receives.

See Topic: GradPLUS Loans

Refers to in-depth training and specialized instruction after the undergraduate level of education. Studying and learning are usually more self-directed at the graduate level than the undergraduate level. The main credentials are academic certificates, degrees (e.g., master’s degrees, doctoral degrees) and professional degrees (e.g., medical school, law school, business school, and other institutions of specialized fields such as nursing, speech–language pathology, engineering, and architecture). Producing original research is a significant component of graduate studies in the humanities, natural sciences, and social sciences. This research typically leads to the writing and defense of a thesis or dissertation. In professional graduate training, the degrees (e.g., MPA, MBA, JD, MD), may consist of coursework without a research or thesis component.

See Topic: Graduate/Professional Education in the Learn-and-Work Ecosystem | Learn & Work Ecosystem Library (learnworkecosystemlibrary.com)

A sum of money or other assistance provided by a government, private organization, or charitable institution to support research, education, or other public services. Grants are used to fund specific projects or activities and are often targeted toward a particular research area, population, or location. The amount and scope of a grant are usually determined by the funding entity and may be granted over a certain period of time. Grants may be awarded based on competitive proposals or through other mechanisms. Many initiatives in the learn-and-work ecosystem are grant funded.

Refers to an economy which balances environmental stability with economic growth. The focus is on environmentally friendly private and public investment, governmental policy/regulations, and socially responsible consumerism. Examples: Circular Economy (recycling, reusing, reducing waste); Conservation/Restoration (protect ecosystems, restore degraded lands, preserve biodiversity); Eco-Tourism (travel that supports local communities and protects natural habitats); Energy Efficiency (reduced energy consumption and greenhouse gas emissions); Environmental Policies (regulation/policy to incentivize sustainable practices, disincentivize harmful ones); Green Building (energy-efficient, environmentally friendly buildings); Green Finance (green bonds, sustainable funds, ethical banking); Public Transportation (public transportation systems that reduce reliance on private cars and decrease air pollution); Renewable Energy resources that are sustainable and have lower environmental impact (transitioning from fossil fuels to renewable sources such as solar, wind, hydro, geothermal power); Sustainable Agriculture (organic farming, crop rotation, and agroforestry to minimize chemical inputs, protect soil health, conserve biodiversity).

A green skill or sustainable skill refers to the knowledge and abilities people need to support environmental sustainability in the workplace and the economy. These skills include things like energy efficiency practices, sustainable agriculture, waste reduction, renewable energy technology, and environmental compliance. They are becoming essential across many industries, not just in traditional “green” jobs because of growing demand for environmentally responsible practices in sectors like construction, manufacturing, transportation, and education. Developing green skills helps prepare learners and workers for careers that contribute to a low-carbon, resource-efficient future.

According to DiNapoli (2023), refers to three dispositional factors, often used interchangeably, that embody the principle of learners’ continuing efforts in the face of challenges. The terms are used in learning science, for example, to determine how students learn mathematics with understanding. Though each term refers generally to not giving up in the face of challenges, perseverance goes more deeply into how students spend their time navigating obstacles than grit or persistence; grit research establishes associations between accomplishments and consistency of interest and effort; and research incorporating persistence describes the time a student spent engaging with a challenge and the opportunity they had to learn from that challenge.

Also referred to as automatic admission or assured admission, this term refers to a policy in place at many state public institutions of higher education which provides a direct path into college for state residents who meet specific criteria.  Criteria can include having earned a high school diploma or GED, satisfying a set grade point average threshold or other academic achievement metric, and additional requirements unique to each state and institution.  These direct admission policies are intended to boost enrollment while encouraging residents to remain in-state for their continued education.  Students are still expected to submit applications and complete required documentation for the institution they wish to attend. 

Guided Pathways is a movement that seeks to streamline a student’s journey through college by providing structured choice, revamped support, and clear learning outcomes—ultimately helping more students achieve their college completion goals. The reform recognizes that the current self-service model of community colleges leads many students to unintended dead ends or unforeseen detours in the form of excess or out-of-sequence credit. There are four pillars of guided pathways: (1) clarify pathways to end goals, (2) help students choose and enter pathways, (3) help students stay on path, and (4) ensure students are learning.

The H-1B visa is a U.S. nonimmigrant work visa that allows employers to temporarily hire foreign workers in “specialty occupations”—roles that typically require at least a bachelor’s degree or equivalent expertise in a specific field. The visa is employer-sponsored, subject to annual numerical caps and a selection process, and granted for a limited duration (generally up to six years, with some exceptions). Historically, when applications exceeded the annual cap, U.S. Citizenship and Immigration Services (USCIS) used a random lottery system to allocate available visas.

The H-1B visa category was established under the Immigration Act of 1990 and has become a central mechanism for connecting U.S. employers to global high-skill talent. It is used most extensively in technology-related fields, along with healthcare, engineering, finance, and higher education. The majority of H-1B visa holders originate from India, followed by China and a smaller number of other countries.

Within the learn-and-work ecosystem, the H-1B visa functions as a key policy instrument shaping global talent mobility and employer access to specialized skills. While the statutory cap on H-1B visas has remained stable (generally 85,000 new visas annually), recent policy changes have reshaped how visas are allocated. Beginning with the FY 2027 cap season, the selection process is shifting from a random lottery to a wage-weighted system, in which applicants associated with higher wage levels receive greater likelihood of selection. These changes place increased emphasis on wages, raise application and compliance requirements, and tighten selection criteria.

The shift has the effect of prioritizing higher-paid and more experienced workers, while potentially reducing access for entry-level roles and lower-wage sectors. It also introduces a stronger connection between employer compensation strategies and immigration outcomes, making workforce planning, wage-setting, and immigration sponsorship more closely aligned.

While sometimes described informally as a “credential,” the H-1B visa is not a credential. It does not validate skills or competencies, but rather provides legal authorization to work in a qualifying occupation. However, because eligibility is tied to specialized knowledge and degree-level preparation, it is often associated with credentialed or highly skilled workers.

See:  International Students | Learn & Work Ecosystem Library

See: Wage-Weighted H-1B Selection System | Learn & Work Ecosystem Library

Also known as self-determined learning, heutagogy is a modern, learner-centered approach introduced by Stewart Hase and Chris Kenyon in 2000. It extends the learning approach known as andragogy by emphasizing learner autonomy, capability, and adaptability in complex and technology-rich environments. In heutagogy, learners take full responsibility for deciding what, how, and when they learn. This approach is particularly relevant in the 21st-century learning and work ecosystem, where continuous learning, digital access, and flexible pathways are essential. Key features include:

• Self-paced, on-demand learning: Learners control the timing, pace, and format of their studies, often using digital platforms and resources.
• Experiential and generative learning: Students learn by doing—through projects, simulations, and creating original content that demonstrates understanding.
• Peer-based learning: Collaboration and knowledge exchange among peers are integral to the process.
• Authentic assessment: Evaluation focuses on real-world problem-solving, creativity, and the demonstration of skills in context rather than standardized tests.

See Topic Brief: Changes in Teaching and Learning in the 21st Century—Pedagogy, Andragogy, and Heutagogy | Learn & Work Ecosystem Library

See Glossary: Learner Agency | Learn & Work Ecosystem Library

Refers to individuals who are willing and able to work but are systematically excluded from employment opportunities due to hiring practices—most notably the use of automated applicant tracking systems (ATS), credential filters, and rigid job requirements that screen out qualified candidates. As a result, these workers remain “hidden” from employers despite possessing relevant skills, experience, and potential.

Research in 2023 from Harvard Business School’s Project on Managing the Future of Work shows that increased reliance on technology and changing demographics have shaped the way companies hire. Hiring processes are designed to find top candidates in an efficient manner, but in doing so systematically exclude several categories of qualified workers, including caregivers, veterans, the formerly incarcerated, those with disabilities, etc. Companies who have hired one or more of these groups of hidden workers report that these workers are more loyal and perform better on several key metrics compared to traditional sources of talent. With many companies facing a talent shortage, hiring these hidden workers is proposed as a solution by researchers.

Refer to industry sectors that are experiencing the fastest growth rates and typically provide workers with higher salary, increased job stability and more career advancement opportunities. Examples of high-demand industries in the United States include healthcare, cybersecurity, information technology, skilled trades, business, advanced manufacturing, and transportation/logistics.

HIPs refer to evidence-based teaching and learning strategies that have been shown to significantly improve student engagement, retention, and learning outcomes. The term was introduced and popularized by George D. Kuh in his 2008 report High-Impact Educational Practices: What They Are, Who Has Access to Them, and Why They Matter, published by the Association of American Colleges and Universities (AAC&U). Kuh’s work built upon decades of research on student engagement and the National Survey of Student Engagement (NSSE), which documented the correlation between certain educational experiences and higher levels of learning and student success.

AAC&U identifies 11 core HIPs:

• First-Year Seminars and Experiences
• Common Intellectual Experiences (integrated general education or core curricula)
• Learning Communities
• Writing-Intensive Courses
• Collaborative Assignments and Projects
• Undergraduate Research
• Diversity/Global Learning (e.g., study abroad, intercultural engagement)
• Service Learning and Community-Based Learning
• Internships
• Capstone Courses and Projects
• ePortfolios (added by AAC&U in later frameworks)

HIPs are now a central part of institutional effectiveness strategies and student success initiatives in higher education. Many colleges and universities formally track student participation in HIPs and use them as indicators of educational quality, engagement, and postsecondary learning outcomes. The movement also intersects with skills-based education and work-integrated learning, aligning HIPs with workforce readiness goals.

According to iCIMS (provider of talent acquisition software), a high-volume hiring strategy remains an ongoing challenge for many companies looking to scale rapidly. Businesses in retail, hospitality, healthcare and other high-turnover sectors often need to recruit dozens or even hundreds of employees under tight deadlines. Without careful planning, this fast-paced hiring can end in poor applicant evaluation, staggering costs and negative candidate experiences that harm the employer’s brand.

The prevailing view in building a healthy learn-and-work ecosystem—and a robust economy—is that higher education must be aligned with the needs of today’s and tomorrow’s workforce. When higher education programs align with workforce needs and meet industry demands they are viewed as “aligned.”

Increasingly, many higher education institutions are using industry data to inform their planning, implementation, and updating of courses programs. Industry data can provide insight into the current and future direction of the workforce and the skills needed for specific roles. The use of industry data by higher education contributes to efforts to align education and employer interests.

Key approaches to alignment include:

• Aligning academic program outcomes with in-demand skills needed by employers.
• Institutions offering reskilling and upskilling courses and programs (often short-term) which allow employees to learn skills needed to help prepare individuals to fill high-demand, open positions.
• Institutions making innovation and employer partnerships core to their culture.
• Institutions embracing models that serve diverse populations of students well (e.g., career advising/navigation, work-based learning, flexible scheduling).
• Institutions measuring main outcomes to inform continuous improvement (e.g., completion rates, return on investment, equity in access and attainment).
• Working closely with policymakers, especially at the state level, in collaborations between educators and business leaders to participate in funding, building, and adapting the educational infrastructure for the future of work. These efforts can help policymakers better understand how to expand access to higher education and workforce training programs to help state and local economies flourish while encouraging greater alignment between higher education and current workforce needs.

These approaches help ensure that individuals have a clear pathway to opportunity while aligning workforce demands with talent supply.

See:  Higher Education & Employer Partnerships | Learn & Work Ecosystem Library

Partnerships between higher education institutions and employers are increasingly useful in addressing talent development in key job categories. Partnership agreements are especially prevalent in areas such as data science, digital technologies, nursing, programming, and renewable energy.

Employers partner with higher education institutions often as a strategy for recruiting talent in different geographic areas of the country, and recruiting talent to diversify their workforce, especially focused on race/ethnicity, gender, age, and disability diversity.

Agreements are typically struck between higher education institutions and employers through a formal partnership.  Employers may commit to hiring a certain percentage of graduates in one or more disciplines while institutions commit to increasing their number of graduates in various disciplines.

According to the Boston Consulting Group, three prevalent types of partnerships include:

• Workforce Planning. The partnership forecast talent baselines —the number of expected graduates in various disciplines and the number employers expect to hire in those disciplines. The partnership identify and prioritize the “hard and soft skills” that these graduates need.
• Academic Program Design. The partnership codesigns the curriculum and jointly appoint faculty for programs of interest. They also determine the number and purpose of employer-sponsored internships and applied training programs; the facilities, labs, faculty, and support services needed to accommodate increasing numbers of students; and joint research and innovation hubs that involve students.
• Student Recruitment. The partnership outlines the steps needed to increase the number of graduates; e.g., conducting market research on targeted student populations, securing funding for student financial assistance, and recruiting students.

Refers to immigrants with at least a university degree.  These individuals make up a substantial proportion of the foreign born in many economically advanced countries. For example, immigrants in the generally high-income countries of the Organization for Economic Cooperation and Development (OECD) tend to have more education than their native-born peers —often working in essential workforce sectors with substantial shortages such as health care and education which also typically require some sort of credential or license.

There is no universally agreed-upon definition of the highly skilled immigrant. Some lawmakers define using a salary scale; others use the level of education.

Many highly skilled immigrants face underemployment or unemployment. These are typically conditions impacted by undervaluing education and professional credentials, language skills, legal status, and discrimination.

While many native-born workers are also overqualified for the jobs they hold, rates tend to be higher among immigrants. As of 2021, approximately one-third of highly educated immigrants in the OECD countries and European Union Member States were overqualified for their jobs, according to the OECD, with highest rates in South Korea (73%), Canada (57%), and Costa Rica (56%).

In the U.S. 35% of the 40.7 million immigrant adults (ages 25 and older) in the country as of 2022 held a bachelor’s degree or higher (Kreimer 2024). The largest numbers came from India (14%), China and Hong Kong (8%), the Philippines (7%), and Mexico (6%).

During the pandemic, to address teacher shortages and prevent staffing reductions while new university graduates waited to take the general teacher licensure test, many U.S. states temporarily eased requirements. Some subsequently changed their education licensure processes permanently. Virginia offers a provisional license to eligible internationally licensed educators; and Nebraska eliminated use of the basic skills test altogether, streamlining the licensure process for foreign- and U.S.-trained educators alike.

The offering of microcredentials and certificates (typically short, nondegree, certified learning courses that lead to a certificate or badge, that with additional credentials or courses may lead to a larger credential) has expanded in recent years to provide rapid credentials for workers with particularly needed skills. Microcredentials can allow highly skilled immigrants to obtain host-country credentials quickly and affordably. While available to native- and foreign-born workers alike, some microcredential programs such as Google’s Career Certificate Program, which offers short-term certificates in high-growth and in-demand fields, have been specifically targeted to immigrants among others.

Alternate terms: highly-educated migrants, highly-qualified migrants

Refers to an institution of higher education where Hispanic students constitute at least 25% of the total fulltime undergraduate enrollment. HSIs are designated by the U.S. Department of Education based on specific enrollment criteria related to Hispanic student populations. These institutions receive certain federal funding and resources to support their mission of serving Hispanic students and promoting their success in higher education. There are approximately 600 HSIs located in numerous states across the country including California, Texas, Florida, and New York. The number of states with HSIs vary over time as new institutions receive HSI designation and demographic shifts occur within states.

Emerging Hispanic Serving Institutions (EHSIs) have Hispanic student populations constituting more than 15% but less than 25% of the total fulltime undergraduate enrollment. These institutions are recognized for providing support to Hispanic student populations and often become eligible for full HSI designation in the future.

The Higher Education Act of 1965, as amended, defines an HBCU as: “…any historically black college or university that was established prior to 1964, whose principal mission was, and is, the education of Black Americans, and that is accredited by a nationally recognized accrediting agency or association determined by the Secretary [of Education] to be a reliable authority as to the quality of training offered or is, according to such an agency or association, making reasonable progress toward accreditation.” HBCUs offer all students, regardless of race, an opportunity to develop their skills and talents. There are currently 107 HBCUs serving more than 200,00 students in 19 states, the District of Columbia, and the U.S. Virgin Islands. HBCUs can be either public or private colleges. While HBCUs historically only served Black students, nearly a quarter of students enrolled in HBCUs as of 2019 were non-Black.

Academic diplomas legitimately awarded by higher education institutions based on recognition of authority or experience, without traditional academic study in a discipline.  Some universities also award holders of a lower degree such as a bachelor’s degree an honorary higher degree such as a master’s degree without formal study.

Refers to the movement and application of skills, knowledge, and competencies across roles, industry sectors, academic disciplines, or organizations at a comparable level of responsibility or seniority, without requiring vertical advancement or additional formal credentials. It emphasizes the portability of expertise and the capacity of individuals to apply what they know in new contexts. This practice can support lateral career mobility, cross-sector collaboration, and skills-based workforce strategies.

Although frequently assumed to be a natural feature of a dynamic labor market, horizontal expertise transfer occurs less often in practice than expected. A primary constraint is the limited ability of employers, educators, and workforce intermediaries to accurately identify, assess, and trust skills that have been acquired in different settings. Job titles, degrees, and industry-specific experience continue to function as proxies for competence, even when underlying skills are demonstrably transferable.

Horizontal expertise transfer is most visible—but still uneven—across industry sectors where skills are modular, tool-based, or functionally defined, such as:

• Technology and digital roles, such as software development, data analytics, cybersecurity, artificial intelligence, and UX/UI design, where skills frequently transfer across industries including finance, healthcare, education, and government,
• Healthcare and health-adjacent fields, including transitions from clinical roles into health IT, informatics, quality improvement, or compliance, and from public health into policy, analytics, or community workforce roles.
• Advanced manufacturing and engineering, where competencies in lean practices, quality assurance, process optimization, robotics, and safety are applied across automotive, aerospace, energy, and logistics sectors, with industry certifications sometimes facilitating lateral mobility.
• Education, workforce development, and nonprofit organizations, where skills in instructional design, assessment, program management, learner support, and partnership coordination support movement among K–12 systems, higher education institutions, corporate training environments, and workforce boards.
• Professional services, including consulting, project management, operations, and change management, where skills are commonly valued for their cross-sector applicability rather than for industry-specific experience.

Despite this apparent suitability, successful horizontal expertise transfer depends heavily on how skills are documented, translated, and validated. Individuals pursuing lateral transitions must often reframe experience using cross-industry skill language, provide evidence through portfolios or work artifacts, and rely on alternative credentials, microcredentials, or skills profiles. Employers, in turn, must be willing to assess competencies directly rather than defaulting to role-based or sector-based screening criteria.

As a result, horizontal expertise transfer is not solely a function of individual capability. It is shaped by the surrounding skills infrastructure, including competency frameworks, skills taxonomies, assessment practices, credentialing systems, hiring technologies, and internal talent marketplaces. Its uneven realization highlights persistent gaps between skills-first aspirations and implementation within the learn-and-work ecosystem.

Related terms: transferable skills, lateral mobility, skills portability, cross-functional mobility, career lattices, skills-based hiring 

An HR Officer is responsible for managing and supporting an organization’s workforce-related functions, policies, and programs. Traditionally, the role has focused on personnel administration, including hiring, employee relations, compensation, compliance, and benefits management. However, in recent years, the role has been expanding to encompass broader responsibilities tied to organizational strategy and workforce development.

Increasingly, the role is evolving beyond traditional administrative tasks to a more strategic focus on talent development, workforce planning, skills development, employee engagement, and organizational culture. This evolution reflects the increasing importance of aligning workforce capabilities with business goals, especially in dynamic labor markets and skills-based economies.

In some organizations, senior HR leaders may hold titles such as Chief People Officer (CPO) to reflect the broader focus on employee experience, leadership development, learning, reskilling, and building organizational agility.

Refers to technology solutions and tools that help manage various aspects of human resources, including recruitment, employee engagement, performance management, payroll, and other workforce management tasks. The term encompasses a wide range of digital platforms and software applications aimed at automating, optimizing, and enhancing HR functions to improve efficiency, employee satisfaction, and strategic decision-making.

The merging of human resources (HR) functions with information technology (IT) systems to build a digital workplace. The integration of previously separated units within a company can streamline workforce management; support data-driven decisions that improve efficiency, compliance, and the employee experience; centralize data; and automate routine tasks. It typically connects HR processes such as recruitment, payroll, training, performance management, and employee engagement—with digital platforms, analytics, and enterprise systems to create a seamless flow of information and operations across the organization.

Refers to a structured model or system that outlines the skills, knowledge, behaviors, and attributes needed to perform effectively within an organization or industry. The HCF is commonly used in talent management, recruitment, performance evaluation, and career development processes. Features of an HCF typically focus on:

• Human/talent (human capital, employees, workforce, people, competence)
• Capability/organization (the team, workplace, culture)
• Leadership (bridges talent and organization, refers to individual leaders and collective leadership)
• Human resources (HR department, practices, people)

HCFs commonly include a focus on five personality traits – the labels for these “five big factors” use the acronym “OCEAN:”

• Openness to experience (inventive/curious vs. consistent/cautious)
• Conscientiousness (efficient/organized vs. extravagant/careless)
• Extraversion (outgoing/energetic vs. solitary/reserved)
• Agreeableness (friendly/compassionate vs. critical/judgmental)
• Neuroticism (sensitive/nervous vs. resilient/confident)

Incorporating the five personality traits into an HCF allows organizations to better understand and leverage individuals’ strengths, preferences, and potential areas for development— alongside the other elements of the HCF (e.g., skills, knowledge, and behaviors).

As defined by the Stanford Center on Longevity & Center for Advanced Study in the Behavioral Sciences, human capital refers to 1) the skills that human beings offer to employers in labor markets; 2) the capacities that enable personal growth and self-discovery; and 3) the many tasks and talents entailed in attending to the care and flourishing of others. Longer human lives that are more prosperous, equitable, and fulfilling require better nurturing and investment of human capital on these dimensions.

Human capital accrues from the earliest years (early children) to late adulthood. The longer the life and faster the speed of skill changes and job disruptions, the more likely individuals will experience multiple transitions between education and work across multiple jobs and career stages. The nation’s current learn-and-work ecosystem (especially the organization of education and employment) makes these transitions difficult and costly.

Refers to a set of activities that convert traditional Human Resource (HR) functions into opportunities that lead to increased efficiency, interest and revenue for the organization. In HCM, organizations commit to maximizing the value of human capital through proper management and investments.

Common functions of HCM include:

• Workforce planning: evaluating the company’s labor and skills needs to meet future staffing needs
• Compensation planning: providing competitive compensation to keep up with inflation and allow employees to live a quality life
• Recruiting and hiring: finding, evaluating and selecting top candidates for open roles
• Onboarding: integrating new employees to the company through new hire orientations, mentorship and reboarding
• Training: providing the opportunities and resources employees need to improve their skills and capabilities
• Time and attendance: tracking work hours through a timecard or punch card for payroll and benefits
• Payroll: providing compensation, calculating employee hours, and keeping track of financial documentation and transactions
• Performance management: ensuring employees contribute to the organization through continuous feedback, goal setting, and performance review

As described in Forbes (June 2024, What Is Human Capital Management?) a number of industry terms are associated with HCM:

• Human Resources (HR): functions such as recruitment, onboarding and managing employee-employer relationships
• Workforce Management: absence management, time and labor, and workforce health and safety
• Talent Management: activities involved in the talent life cycle such as recruitment, onboarding, performance management, career development, and succession planning
• Human Resources Information System (HRIS): keeps track of employee records and HR policies or procedures

Refers to the structured and dynamic interaction between humans and machines, particularly AI-enabled systems, in which each contributes distinct capabilities to perform tasks, solve problems, or make decisions. Rather than replacing human labor, machines in collaborative arrangements augment human judgment, efficiency, and insight; and humans provide context, oversight, ethical reasoning, and accountability.

Although the phrase has existed in in the literature for decades (as HCI / Human–Computer Interaction and in robotics literature), it is experiencing an updated understandings due to generative AI and autonomous systems:

• AI systems now reason, generate, recommend, and decide; they do not just execute tasks.
• Collaboration is no longer sequential (“human → machine” or “machine → human”) but it is continuous and interdependent.

As AI systems become more autonomous and generative, human–machine collaboration increasingly functions as a continuous partnership rather than a handoff between human and machine roles.

A related term is Machine-Human Workforce which focuses on composition and structure; Human–Machine Collaboration focuses on interaction and process.

See Glossary Term: Machine-Human Workforce | Learn & Work Ecosystem Library

See Topic Brief: https://learnworkecosystemlibrary.com/topics/machine-human-workforce/

Also known as hybrid flexible, the term refers to an instructional mode for a hybrid college course that permits flexible learner attendance. Learners may attend along a spectrum of options, from fully online through full in-person (face-to-face / F2F). There are commonly four pillars of HyFlex:

• Learner Choice: Learners (not the instructor) determine when and how they will participate within guidelines. Participation options often change by topic, week, module, lesson, etc.
• Equivalency: Learning activities regardless of delivery mode support the same learning outcome(s).
• Reusability: Use of the same learning objects for all students in all participation modes.
• Accessibility: Learners need the technology and skills to use the technology to access course materials. The instructor ensures learners have access to the required technology, skills training, and support to be successful.

Some institutions include a pillar for equity; that learners in all modalities have equal opportunities to engage in course activities, receive feedback, and ask questions.

I-9 (Employment Eligibility Verification) is a form used by the Federal government to verify the identity and eligibility status of all workers in the U.S. Some states mandate that this form be completed before any employee is hired.

Refers to the extent to which research, reports, or organizational activities reflect a particular political orientation rather than maintaining neutral objectivity. In the learn-and-work ecosystem, ideological skew can influence how evidence is interpreted, how policy debates are framed, and how the value of credentials, workforce strategies, or reforms are communicated. Recognizing ideological skew allows users to critically evaluate information and better understand the perspectives shaping it.

A 2025 American Enterprise Institute (AEI) study, which asked leading AI models to evaluate think tanks, identified 12 dimensions that can be used to assess ideological skew more broadly:

• Institutional Character
  • Independence from Funders or Political Influence
  • Moral Integrity
  • Purity of Motive
• Public Engagement
  • Clarity and Accessibility of Communication
  • Ideological Diversity
  • Policy and Public Debate Influence
• Research Integrity
  • Accuracy and Reliability of Past Claims
  • Methodological Rigor
  • Objectivity
  • Research Quality
  • Staff Expertise
  • Transparency and Openness

Refer to individuals who are either immigrants themselves or born in the United States to immigrant parents. This category encompasses a diverse group of students with varying backgrounds, including first-generation immigrants (born abroad who immigrated to the U.S. to live); and second-generation immigrants (U.S.-born individuals with at least one immigrant parent.) The vast majority of immigrant-origin students are U.S. citizens. As of 2018, 68% were citizens by birth and another 16% were citizens by naturalization.

Immigrant-origin students comprise a significant portion of the U.S. student population. In recent years, first- and second-generation immigrant students have estimated to account for 20% to over 30% of college enrollments (in 2021, 5.6 million students, or 31%).  Immigrant-origin students drove 80% of the growth in U.S. higher education enrollments between 2000 and 2021.

Improvement science is used to accelerate how a field learns to improve.  In the field of education, it is a user- and problem-centered approach to improving teaching and learning. It deploys rapid tests of change to guide the development, revision, and continued fine-tuning of tools, processes, work roles, and relationships—with the goal of improving how the field learns to improve.

A student loan in which students receive money to fund their education or training. Students agree via a contract agreement to pay the ISA provider a fixed percentage of their income for a set period of time after they finish school and pass a specific income threshold. They may repay more or less than the amount received, depending on the agreement’s terms. If the student later loses his/her job, the terms typically permit the individual to stop making payments. Although ISA providers often advertise their products as an alternative to loans, the Consumer Financial Protection Bureau (a federal regulatory agency) has found that ISAs are student loans.

Incremental credentials capture learning as it is acquired along the learning pathway and formally recognizes and connects that learning to a larger context. Incremental credentials can be non-credit or credit-bearing; undergraduate or graduate level; of any size, from small units of learning up through degrees. The purpose of incremental credentials is to ensure learners are recognized for what they know and can do as they acquire the learning and not leave learners without formal documentation of that learning.

Incremental credentialing is the overall design and process used to develop and connect credentials to further learning and employment.

The Incremental Credential Framework was developed through a 2019-2021 planning, research, and testing project in a Lumina Foundation grant to SUNY Empire State College (Credential As You Go, Phase I). The Framework was developed from an environmental scan, prototyping, and feedback from national leaders. The Framework includes six approaches of credentialing that can be used to design incremental credentials and auto-awarding of credentials to reduce the additional step students typically go through to apply for a credential or graduation, plus a focus on prior learning assessment.

Industry refers to a group of companies operating within a particular field which share common characteristics.

Industry sectors refer to a large economic segment that contains multiple industries. The economy has traditionally been divided for purposes of study into sectors to enable analysis of how the various sectors of the economy are functioning.  In the U.S., industry sectors are often classified within 5 categories:

1. Primary (extraction and production of raw materials such as agriculture, forestry, fishing, mining).
2. Secondary (manufacturing and construction such as factories, construction).
3. Tertiary (services such as retail, banking, transportation, tourism, healthcare, education).
4. Quaternary (knowledge-based activities / intellectual development such as research, Information Technology, academic institutions, government, libraries).
5. Quinary (industries that have a major impact on society’s organization and efficacy such as key decision-makers in major corporations and domestic businesses that help keep society functioning including executives in the government, science and academic fields, public services like fire and police departments, domestic endeavors like childcare and housekeeping).

The largest industry sectors are automotive; chemical; electronics; machinery; steel; aerospace; textile; and metalworking. Other large sectors based on their contributions to the national gross domestic product (GDP) include: real estate rental and leasing; utilities; state and local; administrative/waste management services; healthcare and social assistance; professional, scientific and technical services; educational services; management of companies and enterprises; durable goods and manufacturing; arts, entertainment and recreation; construction.

Refers to specialized educational institutions that integrate academic learning with technical and vocational training tailored to specific industries. These high schools aim to prepare students for direct entry into the workforce or further education in fields such as healthcare, technology, skilled trades, or the arts. Programs often include partnerships with local businesses, trade organizations, and postsecondary institutions, offering students hands-on experience, industry-recognized credentials, and work-based learning opportunities such as internships or apprenticeships

Common features typically include:

• Curriculum Alignment: Programs designed to meet industry standards and workforce needs.
• Work-Based Learning: Opportunities for internships, apprenticeships, or cooperative education.
• Partnerships: Collaboration with employers, trade associations, and higher education institutions.
• Career Pathways: Clear routes to employment or further education in targeted industries.

Alternate Terms:

• Career Academies
• Vocational High Schools
• Pathways in Technology
• Early College High Schools (P-TECH)

Related topics and terms in the Library may provide foundational insights:

• Apprenticeships: https://learnworkecosystemlibrary.com/glossary/
• Competency-Based Education: Industry-specific high schools often align their curricula with competency-based frameworks.  https://learnworkecosystemlibrary.com/topics/competency-based-education/
• Mentorships and Mentors: Mentorship programs are often a key component of industry-specific high schools.
• Trade Schools: A glossary term and topic page exist for trade schools, which share similarities with industry-specific high schools in their focus on vocational training.  https://learnworkecosystemlibrary.com/topics/trade-schools/

See Topic Brief: Industry-Specific High Schools: Bridging Education and Workforce Needs | Learn & Work Ecosystem Library

The Learn & Work Ecosystem Library functions, in part, as an information aggregator database, as it primarily comprises resources that consist of processed, organized, and structured information. This information is data that has been analyzed, categorized, and linked to other relevant content, making it meaningful and actionable for a variety of stakeholders exploring the learn-and-work ecosystem.

To fully grasp the concept of an information aggregator database, it is essential to understand the key elements: data, databases, aggregation, information, and knowledge:

• Data refers to raw, unprocessed facts or figures, often numerical or descriptive, that lack inherent meaning by themselves. These are typically collected from observations or measurements.
• Database is an organized collection of structured data, typically stored electronically. Databases support various functions such as accessing, managing, updating, organizing, linking, and analyzing data. Organizations often maintain multiple databases for distinct purposes, like tracking sales, inventory, or transactions, while some specialize in aggregating data from various sources to support deeper analysis and reporting.
• Data aggregation is the process of gathering and compiling data from different sources into a single, unified dataset. This is commonly used in business intelligence, analytics, and data science to derive insights, identify trends, and support strategic decision-making. Aggregated data is often summarized to facilitate easier analysis and interpretation.
• Information is data that has been processed or structured in a way that makes it meaningful and useful. It consists of facts or details about a specific topic or event that can be communicated and understood.
• Knowledge is the result of interpreting and synthesizing information within a particular context. It represents the human capacity to process and combine information, transforming it into deeper understanding. Individuals express their knowledge by encoding it as information—through books, websites, or other mediums—that others can use to build their own knowledge.

In essence, the Learn & Work Ecosystem Library organizes and curates diverse data and information to create a comprehensive, interconnected resource. This makes it easier for users to navigate, understand, and act on the vast landscape of learning and workforce development.

See Glossary: Web Scraping & Information Aggregators | Learn & Work Ecosystem Library

See Topic: Library as Information Aggregator Database: Continuum Model of Data to Information, Information to Knowledge, Knowledge to Wisdom | Learn & Work Ecosystem Library

As defined by the American Library Association, information literacy is the ability to recognize when information is needed and to locate, evaluate, and use information effectively and ethically. It includes understanding the nature and extent of information needs; accessing and assessing sources critically; integrating information into one’s knowledge base; and applying information to accomplish specific purposes within legal, economic, and social frameworks.

See Glossary Tem: Digital Literacy | Learn & Work Ecosystem Library

See Topic Brief: Converging Terms:  Digital Literacy & Information Literacy | Learn & Work Ecosystem Library

The structural advantage held by individuals or organizations that have paid, institutional, or professional access to information resources that are otherwise restricted or unavailable to the general public. Information privilege commonly arises through subscription paywalls, proprietary databases, institutional licenses, or membership-only platforms that gate access to news, research, data, and analysis.

In higher education and workforce contexts, information privilege shapes who can readily access authoritative reporting (e.g., subscription-based higher education journalism), peer-reviewed scholarship, and labor-market intelligence. Faculty, administrators, and students affiliated with well-resourced institutions often benefit from this access through library subscriptions, while independent researchers, practitioners, policymakers, learners, and the public may face financial or structural barriers. As more journalism and scholarly content moves behind paywalls, information privilege can reinforce inequalities in knowledge production, interpretation, and decision-making—prompting growing calls for open access publishing, public-interest journalism, and alternative dissemination models.

According to EDUCAUSE, refers to the act or process of building on existing research, knowledge, and practice through the introduction or application of new ideas, devices, or methods to solve problems or create opportunities where none existed before.

As defined by Duke University’s Future Universities Alliance, an innovation mindset treats the university as a design studio. Its leaders treat their institutions not as static bureaucracies to be managed, but as dynamic organizations to be intentionally designed, tested, and iterated upon. This mindset values hypothesis-testing and structural innovation over incremental improvement.

Refers to a grantmaking approach used primarily in philanthropy in which funders use structured questions, evidence gathering, and ongoing learning to inform funding strategies, support grantee learning, and adapt investments over time. Inquiry-driven grantmaking typically emphasizes:

• Framing funding decisions around clear learning questions rather than fixed assumptions.
• Using qualitative and quantitative evidence before, during, and after grantmaking.
• Learning with grantees rather than evaluating them solely for accountability.
• Adapting goals, strategies, and metrics based on emerging insights and system conditions.

Refer to a spectrum of formal and informal approaches through which higher education institutions collaborate, integrate, or unify to achieve financial sustainability, improve student outcomes, strengthen workforce alignment, and advance shared missions. In contrast to traditional one-to-one mergers or acquisitions, these models emphasize ecosystem-based partnerships, shared governance, and coordinated service delivery. The term reflects growing discourse in higher education policy and leadership circles about the need for more flexible, mission-driven approaches to institutional integration.

Common models include:

• Economic Ecosystem Integration – Institutions integrate at a regional or system level to align education, workforce development, and economic development priorities.
• Student Experience Integration – Institutional boundaries are redesigned to create clearer, more efficient pathways for learners across credential levels, including associate and bachelor’s degrees.
• Co-Location and Shared Campus Models – Institutions share physical space, infrastructure, or student services without full governance or accreditation mergers.
• Systemness-Oriented Collaboration – Institutions adopt disciplined, systemwide coordination such as shared courses or administrative services to improve efficiency and collective impact.
• Shared Purpose and Mission-Based Integration – Institutions collaborate around a common mission or identity, pooling resources and developing joint academic and innovation initiatives.

Refers to a movement that has gained popularity recently, in which universities by policy do not comment on current affairs that do not affect their direct interests. The genesis of institutional neutrality is credited to a 1967 University of Chicago report, which posited that institutions must remain neutral to be a home to a diversity of views. The premise of this approach is that institutions could undermine their commitment to open inquiry by suggesting that those who disagree are unwelcome, and that would-be dissenters believe voicing disagreement could jeopardize college admission, grades, or advancement.

While decisions about institutional neutrality are often up to university administrations (e.g., presidents and chancellors), state lawmakers are increasingly mandating such policies. In 2024, seven state legislatures introduced bills to require institutional neutrality.  Three of those became laws, in Indiana, Iowa and Utah.

There are also questions raised about the role of institution’s department heads and faculty in commenting on current affairs, even if their campus administrations do not (or cannot).  This is generally viewed as a free speech issue: department heads and faculty can speak as individuals but not represent the official views of the institution.

Many disagree with policies of institutional neutrality, based on the belief that this could be an abdication of leadership.

Institutional neutrality policies may differ for institutions with unique missions, such as service academies or religiously affiliated universities.

A comprehensive technology solution for managing a variety of credentials. The system streamlines the entire life cycle of credentialing—from a credential’s proposal and development to its issuance and verification.

Integrated majors combine two existing higher education majors (degrees) into one. The aim is typically to increase diversity and skill sets that are well-suited to 21st century workforce needs. Many integrated majors combine computer science with another major. Examples of integrated majors: computer science and English, design and psychology, and journalism and sociology.

Drivers of this movement:

• In every industry and academic discipline, there are increasingly computer science roles and opportunities—every field needs people who can understand the discipline and also know how to create the software and tools needed for a digital world.
• Combined majors may encourage students who might not be otherwise interested in computer science—and especially those who are underrepresented in the field, including women and Black and Hispanic students—to consider studying it.

The development of combined curricula has developed at many higher education institutions from faculty members—and collaboration with local employers weighing in on which programs proposed by faculty might meet the area’s workforce needs.

More than 170 countries and regions have published national digital strategies, and more than 50 countries have also developed AI strategies.  Strategies typically include a focus on building and maintaining an architecture to help align government and public service approaches for data sharing, adoption of technology, and exploiting AI (artificial intelligence).  The layers of this architecture consist of intelligent sensing, intelligent connectivity, intelligent foundation, intelligent platform, AI foundation model, AI large model, and intelligent application.  These layers are designed to help provide more inclusive and people-centric public services to promote collaboration and proactiveness in areas such as (1) more equitable access to smart healthcare, (2) more access to intelligent education, and (4) faster responses to emergencies.

Intensive tutoring is a targeted, structured, and frequent academic support strategy. Key characteristics:

• Typically occurs at least 3-5 times per week, with sessions lasting 30-60 minutes.
• Often provided in small groups (1:1 to 1:3 ratios), allowing for personalized attention.
• Directly tied to the school or course curriculum to reinforce classroom instruction.
• Delivered by well-trained educators, paraprofessionals, or other skilled individuals, ensuring expertise in the subject matter.
• Regular assessment and feedback guide instruction, ensuring progress monitoring and adjustment to individual needs.

 While intensive tutoring is often highlighted as a K-12 intervention, it is also relevant in higher education:

• K-12 Focus: Frequently employed to close achievement gaps, support struggling students, or provide equitable learning opportunities. Programs such as those supported by federal funds focus on foundational skills in literacy and math.
• Higher Education Context: Colleges and universities use intensive tutoring to support underprepared students, particularly in entry-level or gateway courses (e.g., math, science). Programs like Supplemental Instruction (SI) or intensive academic support initiatives target specific at-risk populations such as first-generation students and those from underserved communities. Higher education institutions also use intensive tutoring for retention strategies, ensuring students stay on track for graduation.

The aims of intensive tutoring:

• For K-12 Students: Closing learning gaps, improving test scores, building foundational skills in critical subjects.
• For Higher Education: Enhancing retention, improving course completion rates, supporting underrepresented students in STEM and other challenging disciplines.

Alternative names for intensive tutoring:

• High-Dosage Tutoring
• High-Impact Tutoring
• Targeted Tutoring
• Acceleration Academies (when part of larger school-day or summer programming)
• Tutoring Interventions (in research contexts)

 Regular tutoring is generally less frequent and less structured. Key differences:

• Often ad-hoc or scheduled once or twice a week.
• May involve larger groups, making individual attention less feasible.
• Typically reactive, addressing immediate academic struggles or specific assignments rather than preemptively targeting learning gaps.
• May include peers, volunteers, or less specialized instructors.
• While personalized, it may not be as systematically aligned with curriculum goals or informed by ongoing assessment.

Refer to interest-free (zero-interest) working capital loans for training providers that enable support for wraparound services and other services. An example is the Colorado Pay It Forward Fund, operated by the nonprofit Social Finance with funds from a collection of philanthropies. The fund also offers interest-free loans for learners to cover living expenses so they can work fewer hours and spend that time on training.

See Outcomes-based Loans

Occurs when children who grow up in families with incomes below the poverty line are themselves poor as adults. Rates of intergenerational poverty in the United States are significantly higher for Black (37%) and Native American (46%) children than other children. Intergenerational poverty affects the overall economic output of the nation and individuals, and particularly burdens educational, criminal justice, and healthcare systems.

Refers to the financial well-being of generations as well as the broader societal implications including race/ethnicity gaps, social and economic mobility, geographical disparities, and the well-being of future generations. Examining the conditions experienced by the generations within society typically include studying economic wealth, educational and employment opportunities, and outcomes across generations. Studies of Intergenerational Economic Mobility typically compare parents’ income and their children’s income in the next generation. Studies of Intergenerational Equity examine the fair distribution of economic, social, and environmental well-being among generations, with a focus on racial gaps in upward mobility. Studies of Geographical Disparities examine mobility impacts based on where children grow up in America.

Intermediary organizations are often viewed as a distinct class of third-party entities. They tend to support the provision of services by another organization rather than providing direct services. They tend to be technical assistance providers or capacity-building organizations. They include nonprofit and for-profit entities, governmental and quasi-governmental entities, and membership organizations. Some are global in focus; others focus within nations, states, or cities. Some focus on the research and policy arena while others are subject- or discipline-specific. An intermediary organization can function in one or many capacities: It can be both a think tank and advocacy organization or both a technology company and consultancy. Intermediaries can also be networks or coalitions of organizations working toward a similar goal.

Refers to an employer’s practice of enabling employees to move into new or expanded roles within the same organization. It includes promotions, lateral transfers, cross-functional changes, rotational assignments, and project-based or gig-style internal work.

Employers use internal mobility strategies to deploy talent more effectively, retain employees, and support employee development.

Modern internal mobility systems increasingly rely on AI-enabled talent platforms that analyze employees’ skills, experiences, and learning histories to match them with current or emerging roles. These platforms help identify internal candidates who might otherwise be overlooked, recommend personalized career pathways, and surface internal opportunities more efficiently and equitably. While internal mobility can support career advancement, the term specifically refers to movement within the organization, not necessarily upward movement.

Internal recruiting is the process of filling open job positions in an organization by hiring from within an existing workforce. Some companies allow their employees to apply to open positions alongside external candidates at the same time. Others provide their employees with exclusivity in the application process for a time before they post the job externally.

According to the UN Refugee Agency, an IDP is a person who has been forced to flee their home but never cross an international border. IDPs include people displaced by internal strife and natural disasters. These individuals seek safety wherever they can—in nearby towns, schools, settlements, internal camps, forests and fields. Unlike refugees, IDPs are not protected by international law or eligible to receive many types of aid because they are legally under the protection of their own government.

Related terms: Refugee, stateless person, asylum seeker

Launched in Australia in 2020 and scaling internationally including to the U.S., the International Big Picture Learning Credential (IBPLC) began as an alternative to the traditional Australian diploma. Public universities in Australia accept the IBPLC as equal to the traditional high school diploma for university admissions consideration. The IBPLC stands for deeper learning through competency-based learning pathways (acquired inside and outside of school) along with personalized approaches that are hallmarks of what is known as “Big Picture Learning.”  The IBPLC is viewed as an exemplar for next gen credentials because:

• It focuses on learners as drivers of their own journeys through education.
• Students have voice and choice in what is included on their Learner Profiles.
• It documents complex learning within and outside of the school setting.
• Its multi-faceted assessments guarantee learners of all types can show what they know to earn proficiency.
• It offers a new concept of a high school credential that provides greater value to the learner in showing what they know and can do (compared to traditional diploma).

The concept behind Big Picture Learning is designing schools where individuals learn how to learn, not where they are instructed in discrete topics, subjects, or information. Five learning goals typically frame how learners approach their work: 1) empirical reasoning, 2) quantitative reasoning, 3) communication, 4) social reasoning, and 5) personal qualities. Authentic assessments (public displays of learning that track growth and progress in the learner’s area of interest) are how learning is demonstrated.  These can include oral presentations, art projects, business plans, or stories. Learners engage in advisories and internships, stay together in cohorts for years to build bonds and relationships, and build social capital. The design also focuses on Learning through Interests and Internships (LTIs). LTIs are personalized, relevant, and contextualized learning. Learners identify their area of interest, then find an opportunity to work with an expert in the community. This approach aligns individual interests with real-world learning.

An international classification system developed and maintained by the International Organization for Standardization.  ICS are used to catalog and classify standards,  often for use in databases and libraries.  ICS currently includes 40 fields. Standards are organized according to:

• sectors of the economy (e.g., agriculture, mining construction, packaging industry)
• technologies (e.g., telecommunications, food processing)
• activities (e.g., environmental protection , safety assurance and protection of public health)
• fields of science (e.g., mathematics, astronomy)

The latest editions of the ICS are downloadable free of charge from the ISO website. Anyone may propose revisions or additions to the ICS.

An international nongovernmental organization founded in 1947 and headquartered in Geneva, Switzerland. It works in 168 countries (as of 2023). Official languages are English, French and Russian. Membership is open to only national standards institutes or similar organizations that represent standardization in their country (one member per country). Individuals or businesses cannot join ISO.

Comprised of various national standards bodies, ISO develops and publishes proprietary, industrial, and commercial standards.  The ISO standards are internationally agreed upon by experts in the relevant fields, and describe the best way of doing something. Examples of ISO Standards:

• calibration of thermometers
• food safety regulations
• manufacturing of wine glasses
• shoe sizes
• security management
• environmental management.

In addition to producing standards, ISO also publishes technical reports, frameworks, guidelines, and various types of specifications. It has published more than 24,500 international standards covering almost all aspects of technology and manufacturing. It has more than 800 Technical committees and subcommittees working on standards development.

The ISO helps to facilitate international trade by providing common standards among different countries.

ISO is not an acronym; it derives from the ancient Greek word ísos, meaning equal or equivalent. Because the organization would have different acronyms in different languages, the founders of the organization decided to call it by the short form ISO.

A related organization is European Committee for Standardization (CEN/CENELEC), which publishes some standards in parallel with ISO. Standards with the designation EN are mandatory for CEN members. An agreement is in place (Vienna Agreement) between ISO and CEN to share information, attend each other’s meetings, and collaborate on standards at international and European levels.

Refers to individuals who come to the United States from other countries to pursue higher education at American colleges and universities. These students typically hold non-immigrant visas, such as F-1 visas for academic studies, and contribute to the cultural and intellectual diversity of U.S. campuses. International students comprise more than 5% of all students in higher education, and about 20% of students at the graduate level.  Enrollment of international students has decreased in recent years, in part as a result of federal administrative policy changes that make it more difficult for international students to come to the U.S.; and as universities in other nations attract more international students.

Related Term: Foreign Nationals, defined by ed.gov as “citizens of countries other than those in which they reside”

Refers to a network of physical devices, vehicles, appliances, and other physical objects that use sensors, software, and network connectivity to collect and exchange data over the internet or other communications networks.  IoT devices are often known as “smart objects” because they have interconnection capacity to share data. Such devices typically include sensors and actuators; connectivity technologies; cloud computing; big data analytics; and security and privacy technologies. IoT applications are prevalent in healthcare, manufacturing, retail, agriculture, and transportation.

Refers to the internet saturated in misinformation and AI “garbage.” According to MIT Technology Review, large language models are trained on data sets built by scraping the internet for text. This text includes silly, false, and malicious things humans have written online. The finished AI models regurgitate this content as fact, and their output is spread everywhere online. Tech companies scrape the internet again, scooping up AI-written text used to train bigger, more convincing models, which humans can use to generate even more nonsense before it is scraped again and again—continuing the cycle of “internet poisoning.” AI feeding on itself and producing increasingly polluted output also extends to images, resulting in the internet forever contaminated with images made by AI.

See: Digital Decay | Learn & Work Ecosystem Library

Refers to an emerging organizational model developing at a time of growing attention to work-based learning as a critical component of education and workforce preparation. An Internship Service Provider (ISP) is an intermediary organization that facilitates the design, delivery, and scaling of internships by coordinating relationships among employers, education providers, and learners.

ISPs reduce administrative and operational barriers by managing key functions such as recruitment, onboarding, compliance, supervision support, and performance evaluation. In some models, the ISP may serve as the employer of record for interns, allowing companies to host interns without directly handling hiring, payroll, or human resources processes.

Typically supported by shared technology platforms, including AI-enabled tools, ISPs provide integrated services for multiple stakeholders:

• Employers: internship program design, candidate matching, onboarding, management, and evaluation
• Colleges and universities: alignment with academic programs, learning outcomes, and compliance requirements
• Learners (interns): onboarding, placement matching, and structured support during the internship experience

The ISP model reflects a broader shift toward intermediary infrastructure in the learn-and-work ecosystem, similar to the role played by apprenticeship intermediaries in expanding registered apprenticeship programs. By aggregating supply (learners) and demand (employers), ISPs aim to expand access to high-quality work-based learning opportunities and enable scaling across industries and fields of study.

Early examples are beginning to emerge. For example, Witness Alchemy has announced plans to launch an internship service provider that aggregates employer demand and learner supply and provides shared infrastructure for managing internship programs.

As the model evolves, ISPs may also support outcomes-based funding approaches, improved data collection on internship participation and results, and stronger connections between education, skills development, and employment.

See Initiative: Internship Service Provider Model — Alchemy | Learn & Work Ecosystem Library

An internship is a time-limited, structured work experience (often tied to a student’s academic or early-career goals) designed to provide exposure to an industry, role, and workplace practices, typically with learning objectives, supervision, and feedback.

Internships can occur during high school or postsecondary education as part of broader work-based learning programming, and they may be sponsored by schools or coordinated through partnerships with employers and other workforce stakeholders. Internships can also be offered outside educational institutions; for example, directly by employers or through intermediary platforms—rather than being tied to an academic calendar or for-credit coursework.

Internships can be paid or unpaid, may be part-time or full-time, and may offer academic credit depending on the school and program design. Typically, when internships offer academic credit, they are unpaid.

Internships are commonly operated (run day-to-day and funded/managed) by:

• Private-sector employers (companies of all sizes)
• Nonprofits (mission-driven organizations with capacity for supervision)
• Government agencies (local, state, federal offices and programs)
• Educational institutions (career services–coordinated placements, co-ops, practicum-style internships)
• Intermediaries/host partners (industry associations, workforce organizations, staffing/placement partners) that help source candidates and coordinate placements

Why they operate internships:

• Talent pipeline building (early identification and recruiting of future hires)
• Short-term capacity for projects or seasonal work (with training as a secondary goal)
• Workforce branding (improving visibility with students and early-career talent)
• Social impact and access goals (career exposure for underrepresented groups, community investment)
• Professional training (giving novices real-world practice in a lower-risk setting)
• Academic-to-career alignment (helping learners apply classroom knowledge)

Whether an internship is legally allowed to be unpaid depends on jurisdiction and facts (e.g., who primarily benefits, the structure of training, and whether it displaces paid work). When in doubt, organizations typically seek HR/legal guidance.

Examples:

• High school internships: Often embedded within industry-aligned high schools and pathway models, typically supported by employer partnerships and other work-based learning components.
• College internships: Commonly aligned to academic programs and may follow institutional schedules (e.g., semester-based experiences).
• Micro-internships: Short-term, project-based internships (often 10–40 hours) that can be offered year-round, may be remote, and typically require minimal employer overhead—used as a precursor or alternative to traditional internships and sometimes as skills-based hiring auditions.

Internships are often confused with apprenticeships.

An apprenticeship is an industry-driven, high-quality career pathway that combines classroom instruction with paid on-the-job experience, enabling individuals to earn a nationally recognized, portable credential; employers may register programs (e.g., in the U.S., with the Department of Labor) to signal they meet quality standards.

Practical way to choose between an internship or apprenticeship:

• An internship when the goal is exploration, building a resume/portfolio, or testing fit in a field.
• Apprenticeship when the goal is a paid pathway to a recognized occupational credential with a clearer training-to-job outcome.

Internship vs. Apprenticeship (key differences)

Dimension

Apprenticeship

Related terms:

• Work-Based Learning (WBL)
• Industry-Specific High Schools
• On-the-Job Training (OJT)
• Intermediary Organization / Apprenticeship Intermediary

 See Topic Brief: Apprenticeship / Apprenticeships | Learn & Work Ecosystem Library

Interoperability is the ability of different information systems, devices or applications to connect, in a coordinated manner, within and across organizational boundaries to access, exchange and cooperatively use data amongst stakeholders.

Four higher education interstate compacts in the United States facilitate cooperation among their member states to address common challenges, leverage resources, and improve educational opportunities for students within their respective regions. The compacts are nonpartisan, non-profit organizations that were established in 1948, Southern Regional Education Board (SREB); 1953, Western Interstate Commission for Higher Education (WICHE); 1955, New England Board of Higher Education (NEBHE); and 1991, Midwestern Higher Education Compact (MHEC). Established by either Congress or by agreements among the states themselves, the compacts together represent 47 states and territories and 6 state affiliate partners.

A cloud-based solution that streamlines digital transformation (modernization) by connecting disparate systems and data sources through a centralized integration hub. Widely used in both public and private sectors, iPaaS platforms offer pre-built connectors, API management tools, and low-code/no-code workflow builders that simplify the integration of internal systems and external data sources. This unified approach improves process efficiency, service delivery, and data consistency across the enterprise.

Government agencies and other organizations can use iPaaS to:

• Centralize and manage data integrations through a single hub
• Access a broad library of pre-built connectors to integrate diverse systems
• Gain visibility and control over APIs and their usage
• Establish enterprise-wide integration governance policies
• Transform, harmonize, and standardize data from multiple platforms
• Automate business processes using a visual drag-and-drop builder
• Enable non-technical users to build workflows without coding
• Reduce reliance on scarce or specialized IT resources

By reducing integration complexity, iPaaS supports scalable modernization efforts and accelerates service delivery in complex IT environments.

An issuer registry, a type of trust registry which can be used in W3C Verifiable Credential Ecosystems, is a dataset which contains a list of “known issuers” and their unique identifiers like URLs (uniform resource locators) or DIDs (decentralized identifiers). In the case of education or training credentials (sometimes referred to as Learning and Employment Records, or LERs) issued as VCs (W3C Verifiable Credentials), these issuers could include universities, employers, or professional training programs which issue diplomas, certificates, or badges. The issuer registry can be maintained and governed by an accreditation body, a government, a nonprofit, or another kind of entity. The main purpose of the issuer registry is to provide an additional layer of trust in the credential through a mechanism which checks and verifies the identity of the issuer.”

Search engines that gather job postings from job boards, employer websites, industry and professional association websites,  and other internet sites. Postings are consolidated into a single searchable interface.  Job Aggregators typically sort job listings by various categories such as part-time/fulltime; hourly/salary; start date; and location. Examples include: Indeed,  SimplyHired,  CareerJet, LinkedIn Jobs, Linkup.

A website where employers list job vacancies and job seekers apply for positions in field(s) they are interested in.  Examples include: JazzHR, SmartJobBoard, Manatal, JBoard, VIVAHR, ZipRecruiter, Zoho Recruit, Fountain, Workable, TalentReef.

A related term is a career site, which is an online platform where organizations and companies provide information to enable job seekers to learn more about job openings and the company (e.g., employee benefits, salary structure, policies, job location).  This is helpful information before job seekers apply for a position with an organization or company.

See:  Job Aggregator

Refers to the trend of employees staying in their roles due to economic uncertainty and fear of job loss, rather than loyalty or satisfaction. This shift stands in contrast to job hopping, often viewed as a marker of ambition and pathway to advancement. Recent data from the Bank of America Institute show that pay increases for job hoppers are no higher than for those who stay with their employers, removing one of the primary incentives for switching employers.

For employers, low turnover may look positive on the surface, but it can mask employee disengagement and stagnation. Job hugging signals the need for organizations to look beyond retention rates and focus on engagement, ensuring that employees are not simply holding onto jobs out of caution but are thriving and contributing meaningfully to organizational growth.

The American Job Quality Study (2025) conducted by Jobs for the Future, Gallup, The Families & Workers Fund, and W.E. Upjohn Institute for Employment Research defines job quality based on five dimensions that research shows matter most to workers and businesses:

1. Financial well-being: Fair pay, stable employment, and benefits that meet basic needs and reduce financial stress.
2. Workplace culture and safety: A safe, respectful environment free from discrimination or harassment.
3. Growth and development opportunities: A clear path to build skills, gain experience, and advance in one’s career.
4. Agency and voice: Influence over decisions that shape one’s job, such as pay, working conditions, and implementation of technology.
5. Work structure and autonomy: A stable, predictable schedule, a manageable workload, and meaningful control over when and how work gets done.

The study found that most U.S. workers (60%) lack quality jobs, resulting in lower well-being and satisfaction, factors that affect retention, productivity, and business performance. The study of more than 18,000 people was the first nationally representative survey of job quality across the entire U.S. workforce.

Refers to the level of contentment that workers feel with their job. There are several indicators of job satisfaction often cited in the literature:

1. The company cares about its employees
2. The workplace has room for employees to engage in their hobbies or personal interests
3. Opportunity for promotions on a regular interval (5.2 years is the average)
4. Employees feeling respected by their peers
5. Culture of two-way positive and constructive feedback
6. Culture that promotes positive work-life balance
7. Relationships with reporting heads/supervisors
8. Fair and inclusive policies to ensure fairness toward all employees, regardless of age, gender, race/ethnicity, or disability
9. Culture that nurtures creativity in jobs
10. Job security, especially as technology/automation threatens legacy models of working

Research by the Pew Research Center studies 11 components of job satisfaction in addition to satisfaction with the “overall job”:

1. Relationship with co-workers
2. Relationship with manager or supervisor
3. The commute
4. Day-to-day tasks at work
5. Flexibility to choose when to work required hours
6. The benefits the employer provides
7. Amount of feedback workers receive
8. Opportunities for training/ways to develop new skills
9. Flexibility to work remotely
10. How much workers are paid
11. Opportunities for promotion at work

The probability that individuals will keep their job. Key factors that can threaten job security include outsourcing, global hiring, downsizing, economic recession, and technologies impacting job tasks.

Refers to the ways jobs are classified such as part-time and full-time, exempt and non-exempt, employees and contractor, and permanent/temporary employees. Job structures refer to when people work, where they work, expectations for how much they work, and the extent to which they have some choice over when/where/how much they work.

When demand for workers is high and the supply of workers low, job switching can be a strategy for workers demanding and often getting higher pay. When job switching is high, it can be referred to as a job-switching movement.

According to the AACRAO Higher Ed Glossary, a synchronized transcript of training, experience, and education acquired during military service in the United States. The JST is available to current and former military-service members in hard copy or an online delivery format.

Refer to individuals (or the population) in four categories:

• Have been incarcerated or detained in a prison, immigration detention center, local jail, juvenile detention center, or other incarceration setting
• Have been convicted but not incarcerated
• Have been charged but not convicted
• Have been arrested.

With the criminal justice system impacting so many individuals (research suggests that about one-third of adults in the U.S. has some form of criminal record, with higher proportions in poor communities and communities of color), there has been growing attention to the educational and employability experiences of the  justice-impacted population to ensure that policies and practices do not unintentionally exclude applicants from educational opportunities and employment.

Also known as key person insurance, refers to a life insurance policy that a business takes out on a key employee or executive whose loss would significantly impact the company’s operations or financial stability. The company pays the premiums and is the beneficiary of the policy. This type of insurance is designed to offset losses related to the death or disability of a key contributor, fund recruitment or transition plans, and/or reassure investors and lenders.

A knowledge graph is a design pattern that can help users better understand data.  A graph is formed of nodes and relationships:

• a node is a person, object, location, or event
• relationship refers to the interaction among nodes.

Representing data to depict these connections (relationships among nodes) can enhance the value of information.

According to Wikipedia, the Knowledge Graph was launched by Google in May 2012 to enhance the value of information returned through Google searches. By May 2016, knowledge boxes appeared for some one-third of the 100 billion monthly searches the company was processing.  The Google Knowledge Graph depicts relevant information in an infobox next to its search results. This enables users to see the answer in a glance. Data is generated automatically from multiple sources, and it covers places, people, organizations, and more.

Knowledge graphs are at the core of human-facing technologies such as search, question answering, dialogue, and recommenders.  They are particularly useful in fields characterized by data silos (e.g., healthcare and financial services). Knowledge graphs can help with data governance, fraud detection, knowledge management, searches, chatbot use, developing recommendations, and developing intelligent systems across different organizational units.

Refers to methods used to create, share, use, and manage the knowledge and information of an organization; and the technology that makes it possible to store, access, and update information. KM technology is a portal that links many databases together and enables the exploration of one or multiple sources simultaneously.

KM in the information-age requires an increasingly large proportion of the working population to consist of information workers. In this context, KM is a movement to create an information environment conducive to successful research and development—one that depends on extensive and deep open communication and information access that can be shared broadly across an organization.

Many organizations develop a knowledge management system to conduct their business efficiently and effectively. Common steps include: 1) knowledge creation; 2) organization of knowledge; 3) sharing of knowledge; 4) acting on knowledge; and 5) updating knowledge.

Main KM strategies include: 1) collecting and recording information (codification), 2) spreading information (dissemination), and 3) tailoring information to specific audiences (personalization). Most organizations with knowledge management systems use a blend of these strategies.

In the KM literature, knowledge is often categorized as:

• Explicit—information or knowledge that is set out in tangible form; structured information easy to document, share, and learn from.
• Implicit—information or knowledge that is not set out in tangible form but could be made explicit; the application of explicit knowledge.
• Tacit—information or knowledge gained from experience or intuition.

The ways in which knowledge—scientific, social, and cultural—is produced has undergone significant and fundamental changes in the last century.

Knowledge production is often defined in a higher education context. It typically refers to related activities in a higher education institution, research center, or enterprise that is engaged with producing new knowledge. The term refers broadly to basic research as well as the more applied type of research associated especially with industry.

As described by the International Encyclopedia of Higher Education Systems, knowledge production has become a central task in modern universities. This represents a shift from the university’s origins in medieval Europe, which informed what became known in Europe and America as a “liberal education” —historically widely viewed as the purpose of universities, which in turn was closely associated with the supply of individuals prepared with the skills to meet the needs of the state, commerce, and the church.

There is growing awareness that knowledge production is moving beyond the main purview of higher education institutions, as industry invests more in research and development, driven in large part by rapidly accelerating science and technology developments.

A type of digital tool or system that helps people find information—and make sense of it by synthesizing ideas that come from different sources. These platforms are designed to help users understand patterns, explore relationships, and generate insight from complex information. Unlike a basic search engine or database, a KUP supports users in integrating, organizing, and applying knowledge. It often uses artificial intelligence (AI), natural language processing, and semantic tools to show how topics, people, policies, or trends are connected.

In the past, the biggest challenge for many individuals often was finding information. Today, the challenge is understanding what matters, how ideas relate, and how to act on what we know.

KUPs are increasingly used in research, education, workforce, business enterprise, and digital library settings to help make complex information ecosystems navigable and actionable through synthesis and insight. Examples of applications:

• Unify fragmented information across silos
• Make implicit knowledge explicit
• Identify relationships among concepts, people, and resources
• Provide tools for visualizing and reasoning about knowledge

The term “Knowledge Understanding Platform” is related to the evolving fields of “Knowledge Management Systems” and “Knowledge Graphs” which emerged with the rise of AI-powered knowledge infrastructure in the 2010s and especially post-2020.

Large Language Models (LLMs) are a class of artificial intelligence (AI) systems that consist of very large and diverse collections of text (and often code, images, and other data) that recognize patterns in language and generate human-like responses. LLMs are designed as general-purpose models capable of performing a wide range of tasks, such as summarizing, drafting, translating, reasoning, and answering questions across many domains.

LLMs do not “know” facts in a human sense; rather, they predict responses based on statistical patterns learned during training. Examples of LLMs include ChatGPT (OpenAI), Gemini (Google), LLaMA (Meta AI), Bard (Google AI), and Claude (Anthropic).

Depending on how they are deployed, LLMs may be further refined through techniques such as fine-tuning, feedback loops, or reinforcement learning, often with governance controls that limit how user interactions are retained or used.

In the context of the learn-and-work ecosystem, LLMs are useful for their breadth, flexibility, and ability to provide information and support exploration by users. Examples of use include career navigation, content generation, and knowledge synthesis. Their general-purpose design results in varying levels of accuracy, consistency, and explainability. For this reason, human oversight and organizational guardrails are needed to work effectively with LLMs.

See Glossary: Closed-Circuit AI Models (Domain-Specific or Enterprise AI Models) | Learn & Work Ecosystem Library

See Glossary: Opacity (in Artificial Intelligence) | Opaque AI | Learn & Work Ecosystem Library

See Topic Brief: AI Architectures in the Workplace: Large Language Models & Closed-Circuit AI Systems | Learn & Work Ecosystem Library

Refer to entry-level positions that are accessible to individuals without a college degree that offer some combination of: (1) higher-than-average starting pay and continued wage premiums 10 years on; (2) higher likelihood of providing health insurance; (3) high promotion rates (transitions from a first job that are a step up in responsibility or pay); (4) strong pathways to better opportunities; (5) some level of protection from technological disruption (low automation risk).

See: Research: “Launchpad Jobs: Achieving Career and Economic Success Without a Degree” — American Student Assistance & Burning Glass Institute | Learn & Work Ecosystem Library

Launched in Fall 2022, the Learn & Work Ecosystem Library collects, curates, and coordinates digital resources to help users understand the nation’s complex ecosystem of education, training, employment, and work. Grounded in a commitment to open access and community stewardship, the Library invites users to suggest new resources and contribute to quality assurance by recommending edits. It was developed in collaboration with researchers from George Washington University’s Program on Skills, Credentials & Workforce Policy. The goal: to create a living, continually updated hub of knowledge—built by the community, for the community.

The Library functions as an information aggregator, organizing a wide array of resources—such as glossary terms, innovation initiatives, special topic reports, organizational profiles, and an archive of key documents and websites—designed to support a diverse set of ecosystem stakeholders. Key features include:

• Different types of content: Glossary, Key Initiatives, Topic Reports, Organizations, Library Lens reports, Index, Archive
• Time-stamping to indicate recency of content
• Digital accessibility compliance for disabled users
• Content available in multiple languages (12)
• Search options: key word, filter by stakeholders and types of content, AI chatbot for natural language queries/synthesis
• “About” section: describes the Why, How, and What around the Library, staff team, national advisory board, commitment to open access (Creative Commons License)
• “Top 5” monthly searches
• Links to external websites
• Newsroom for articles, blogs, reports
• Graphic depictions (maps) of relationships among searchable artifacts

In addition to its core collection, the Library partners with organizations to develop and host specialized resource collections. Through formal project agreements, the Library can ingest and curate partner-owned materials, organize them within a consistent structure, and maintain them over time. These services ensure high visibility, quality, and long-term value while relieving partners of the burden of ongoing content management.

See: Learn & Work Ecosystem Library | Learn & Work Ecosystem Library

The learn-and-work ecosystem is a connected system of formal and informal learning (education and training) and work. The ecosystem is composed of many building blocks. The Learn & Work Ecosystem Library identifies 12 building blocks (key components) comprising the ecosystem:

• Alliances & Intermediaries
• Career Navigation
• Communications & Technology
• Credentials & Providers
• Data, Databases, Standards (Data Ecosystem)
• Employers & Workforce
• International Developments
• Policy
• Quality & Value
• Research
• Transparency
• Verification / Recordkeeping

When all the building blocks are working together, individuals are able to move more seamlessly through the marketplace using a variety of credentials to communicate the skills and knowledge acquired in multiple settings (e.g., school, work, service, self-study). Employers have more detailed and externally validated information during their hiring and upskilling processes. Schools are better able to count learning obtained outside of academic settings toward a degree or other credential. And the public is informed about our learn and work ecosystem. For the ecosystem to function effectively, all parts of the system must be connected and coordinated.

Alternate term: Education-to-employment ecosystem

An inclusive term that encompasses many types of learners; for example, those taking part in the educational process whether a degree program or microcredential. Learners acquire new competencies and skills as enrolled students at a school or postsecondary institution, and as those seeking to enhance their knowledge and skills to secure employment opportunities or advance their careers. Many learners are also working learners. The ACT Foundation defines “working learners” as individuals who are both working for pay and enrolled in formal learning programs that lead to a recognized credential. They are the majority of part-time students and more than a third of the full-time student population.

Refers to a person’s capacity and willingness to take an active role in their own learning—shaping what, how, and why they learn. It involves self-direction, goal setting, reflection, and collaboration with others to create meaningful learning pathways. Exercising learner agency means that individuals are not passive recipients of instruction but co-creators of their educational and professional development.

Increasingly, learner agency is viewed as a cornerstone of 21st-century education and work. It enables individuals to act intentionally, make informed choices, and adapt to continuous change—skills essential in a rapidly evolving global ecosystem of learning and work.

• Essential skills for the future: Internationally, the Organisation for Economic Co-operation and Development (OECD) in Learning Compass 2030 highlights agency as essential for developing the skills, knowledge, and values needed to thrive in a complex, interconnected world. They identity three core foundations of learner agency which taken together prepare individuals to thrive in complex, interconnected work and civic environments:
  • Skills (social, emotional, and “learning to learn”)
  • Knowledge (disciplinary, interdisciplinary, epistemic, and procedural)
  • Attitudes and values (such as curiosity, responsibility, and respect).
• Higher education and workforce development
  • In higher education and workforce development, learner agency is increasingly linked to future-ready and self-determined learning. Research by Ehlers and Kellermann (2019) identifies a shift toward learner-centered models emphasizing autonomy, self-organization, reflection, and personalized learning pathways. This aligns with heutagogical principles, in which learners design and manage their own learning experiences to build adaptability and lifelong learning skills.
• Workplace learning and professional development
  • Learner agency extends into workplace learning and professional development. Studies in vocational education and training show that self-determined learning approaches—such as problem-based learning, increased learner responsibility, and reciprocal feedback—can enhance professional growth and performance. As learners gain confidence and control over their learning, dependency on supervision diminishes, and self-direction increases.

See Topic Brief: Changes in Teaching and Learning in the 21st Century—Pedagogy, Andragogy, and Heutagogy | Learn & Work Ecosystem Library

See Glossary: Heutagogy | Learn & Work Ecosystem Library

Refers to learners’ achievement of educational and career goals, including persistence, completion, transfer, employment, and personal development. It encompasses academic progress, skill mastery, and successful transitions to the workforce or further education through training in the workplace.

Learner and student success reflects both institutional effectiveness and individual achievement. It is influenced by access to high-quality instruction, student supports, inclusive learning environments, and equitable policies that remove barriers to participation and completion.

Success is often measured in multiple ways: success that is learner-centric and success that is institution- and policymaker-centric.

• A general definition that is learner-centric: determination of goals and personal situations as measured by each individual learner / student. Success by individuals is often viewed as being able to support themselves after completing the educational process, through acquiring a good job and remaining employable in the workplace.
• A definition which is higher education- and policymaker-centric: graduation rates, course completion, retention rates, academic achievement, degree attainment, credits completed, student advancement.

Learner and student supports are the academic, financial, social, and personal assistance services designed to help individuals persist and succeed in education and training. These include advising, tutoring, mentoring, counseling, financial aid, childcare, transportation, and technology access. Effective support services are essential to promoting student success, particularly for adult, nontraditional, and historically underserved learners. Holistic support approaches integrate both academic and nonacademic needs to create conditions that enable learners to thrive.

An open-source mobile wallet designed to hold verifiable credentials of learning achievement (diplomas, certificates, badges, and other credentials). It was developed by the Digital Credentials Consortium, a network of leading international universities designing an open infrastructure for academic credentials.

See: Achievement Wallet

The burgeoning field of learning sciences research informs educators that learning can be improved by recognizing learner variability; i.e., that all learners have unique strengths and challenges that can be addressed through personalized learning approaches. These strengths and challenges can be depicted within a framework or set of tools that recognizes a “whole child” approach; i.e., that depicts the ways that the many factors that influence learning are interconnected and vary according to context. This recognition is used in educational practices to address learner variability and personalized learning, especially for learners from pre-Kindergarten through grade 12 in literacy and mathematics, as well as adult learners.

Examples of frameworks/tools that address learner variability:

• Digital Promise’s Learner Variability Navigator: Interactive tool that provides research-based strategies and resources tailored to key factors that influence learning, such as learner background, social and emotional learning, cognition, and content area.
• CAST’s Universal Design for Learning (UDL) Guidelines: Provides a framework to improve and optimize teaching and learning for people based on scientific insights into how humans learn. Guidelines support educators, curriculum developers, researchers, parents, and others to apply the framework to practice. The guidelines are applicable to any discipline or domain.
• Common Sense Education: Offers reviews and ratings of digital tools and apps that support personalized learning.
• Khan Academy: Provides a range of free educational resources (practice exercises, instructional videos, personalized learning dashboard) to support individualized learning paths for students and build skill mastery. Resources are available for students, teachers, and parents. Students practice at their own pace.  Subjects cover kindergarten through early college – including math; science; reading; computing; history; art history; economics; financial literacy; national tests such as SAT, MCAT.

According to Education Design Lab, refers to the learning institution, organization, or agency that is responsible for designing digital credentials and validating skills against a skills framework.

According to Education Design Lab, refers to individuals with a wallet that contains digital credentials that comprise their learning and employment records.

According to Education Design Lab, refers to a database that holds credential information such as jobs and skills titles, descriptions, and functions.

According to Education Design Lab, refers to the entity that reviews an individual’s credentials to verify trust.

According to Education Design Lab, the term refers to the organization that delivers a credential to an individual’s digital wallet. This term is also known as an issuer.

Enhances the traditional resume by bridging the divide between employer hiring systems and Learning and Employment Records (LERs), enabling the use of LERS when applying for jobs and in employee advancement. LER-RS are designed to:

• Make an individual’s resume narratives, skills, and experiences machine-readable and verifiable through digital credentials.
• Empower individuals to self-assert.
• Enable the exchange information between an individual’s digital wallet and Human Resources (HR) recruiting technology platform of an employer.
• Support employer initiatives, including skills-based hiring initiatives.

Comprehensive digital records of an individual’s skills, competencies, credentials, and employment history that may be able to show a complete picture of an individual’s education and work experiences. They have the potential to highlight verified skills, reduce hiring biases, and match people to employment opportunities. A LER can document learning wherever it occurs.

Refer to pathways that voluntarily affiliate with the Linked Learning movement and seek validation of their pathway quality. This can include programs participating in Registered Linked Learning Pathways that have submitted evidence that indicate a level of pathway quality can become “Linked Learning Certified.” There are three tiers of certification:

• Candidate Pathway: Basic program elements are in place to provide students with an integrated college and career preparation experience.
• Silver Pathway:  Core components of a Linked Learning pathway are in place and can use basic data about its pathway to inform program design and improve student outcomes.
• Gold Pathway: Program has developed beyond core components and is providing an exceptional pathway experience for students. The pathway has evidence of achieving positive student outcomes in domains related to readiness for college, career, and life.

Learning frameworks are tools that specify learning outcomes and/or competencies that define, classify, and recognize educational, learner, and industry expectations of knowledge, skills, and abilities at increasing levels of complexity and difficulty. They are not standards, and they are not limited to academia, but they do allow for alignment, translation, and mapping of learning through various spaces in order to capture learning that can be valued and recognized by education, industry, and the military. These frameworks can support quality assurance mechanisms for reviewing aligned curriculum and training, provide guideposts for awarding credentials, and serve as end points from which learning experiences can be backward-designed. In addition, learning frameworks enable consistency; provide a common language within their user group(s); and assist in transferability within and across education providers, alternative learning pathways, military learning, and industries (including employer-developed industry expectations and career readiness skills).

Alternate Term: Framework

Refers to both the system-level and individual-level ability to move learning and learners seamlessly across educational, training, and work contexts.

• Learning mobility focuses on the systems, policies, practices, programs, and initiatives designed around the needs and best interests of the learner of today and the future at every stage of their journey. Learning mobility enables the recognition, validation, and transfer of learning—such as credit mobility, prior learning assessment, and interoperable records of achievement.
• Learner mobility centers on the individual’s experience navigating these systems, moving across institutions, geographies, or career pathways while building on previously acquired knowledge and skills.

Together, learning mobility and learner mobility represent a learner-centered approach in which education and workforce systems are designed around the needs and best interests of learners, supporting equitable access and continuous progress along often nonlinear learning and work journeys.

Alternate terms: credit mobility, transfer, prior learning assessment

In European education, the term refers to setting an objective of a specific percentage of learners from particular age groups and from particular levels of education, having been engaged in learning mobilities during their studies.

Learning (learner) mobility is a key objective in education in Europe. The term refers to the process of enabling learners throughout their lifetime to access their right to education founded on the belief that education is a public good, which includes the experience of learning mobility also as a public good. It is addressed in policy through the European Union (EU) Strategic Framework for Cooperation 2021-2030, which includes lifelong learning and learning mobility as priorities.

The Lifelong Learning Platform is an umbrella that gathers 44 European organizations from education, training and youth, representing more than 50 000 educational institutions and associations covering all sectors of formal, non-formal and informal learning. Members work together on education and training to harmonize their work over the next decade.  No benchmark has been established to measure the EU’s progress in “making learning mobility a reality for all” though learner mobility is a key objective of the Framework and the European Education Area, and one of the main requests by citizens through the Conference on the Future of Europe (CoFoE). This topic is critical because a key target of the previous strategy – 20% of mobile higher education graduates – was not reached. The current Lifelong Learning Platform presents recommendations from its members to address shortcomings and to widen access to learning mobilities across the EU.

See: LEARNING MOBILITY FOR ALL: A LIFELONG LEARNING APPROACH TO TARGETS, PROCESS AND MONITORING – Lifelong Learning Platform | LLLP (lllplatform.eu)

The American Association of Collegiate Registrars and Admissions Officers (AACRAO), defines learning mobility as ensuring equitable and accessible programs and opportunities that prepare all learners for the workforce and beyond. In December 2021, AACRAO, the American Council on Education (ACE), and the Council for Higher Education Accreditation (CHEA) issued a Joint Statement on the Transfer and Award of Credit directed to college and universities about the award of academic credit for learning acquired elsewhere. The statement recognizes that in a rapidly evolving higher education landscape and increase in student mobility and extra-institutional learning, it is critical that higher education institutions support credit award policies based on equity-minded practice and principles. Institutions are encouraged to conduct an audit of their credit award policies and practices, including surveying transfer students to learn about their experiences navigating policies at their institutions; then use that information and a framework outlined in the Statement to help learners who increasingly rely on nonlinear paths to earn a college credential.

An AACRAO 2023 study found that a majority of the higher education institutions in their membership consider innovative credentialing of some type and learning mobility as priorities. This confirms AACRAO’s focus on facilitating seamless evaluation and documentation of learning, since AACRAO members play a key role in implementing policies and practices aimed at alleviating issues related to transfer, credit mobility, and the recognition of learning – the main indicators of learning mobility.

See:

• https://www.aacrao.org/signature-initiatives/learning-mobility
• https://www.aacrao.org/docs/default-source/statements-and-letters/2021-joint-statement-award-of-credit0c480d4a6f724decbf3433f190b42ee8.pdf?sfvrsn=dccf29d8_1

Descriptions of what students will learn in a course, program, or training, and how that learning will be assessed. Creating clear and measurable learning outcomes are necessary for assessment and evaluation.  Well-stated learning outcomes include a verb to describe an observable action, a description of what the learner will be able to do and under which conditions, and the performance level the learner should be able to reach. Learning outcomes is a general term for what students will learn and how that learning will be assessed, and includes goals and objectives. Related terms include:

• Learning goals – often used to describe the general outcomes for a course or program.
• Learning objectives  – refer to the more focused outcomes for specific learning lessons or activities.
• Learning taxonomies – describe how a learner’s understanding develops from simple to complex when learning different subjects or tasks.

Two taxonomies are commonly used in developing learning outcomes:

• Bloom’s Taxonomy – model describing how learning occurs hierarchically, as each skill builds on previous skills towards increasingly sophisticated learning. It includes three domains of learning: cognitive, psychomotor, and affective.
• Finks Taxonomy of Significant Learning – model describing learning as holistic and extending beyond the course or training.  The right-hand side of the taxonomy refers to the same kinds of cognitive learning described in Bloom taxonomy but the left-hand side goes beyond cognitive learning to include six intersecting domains (foundational knowledge, application  skills, integration, human dimension, caring, and learning how to learn.

Refers to a portable, digital record that captures a learner’s achievements, credentials, and experiences.  The record (a detailed portfolio) can follow the learner across various educational institutions, educational and training programs, workplaces, and even countries. It aims to make learning portable and verifiable, allowing for easier transitions between educational settings and the workforce.

An example is UNESCO’s Learning Passport, which helps displaced students or refugees carry evidence of their learning achievements.

Related Terms:

• Competency Passport: A record focused on documenting specific competencies and skills acquired by an individual.
• Skills Passport: Similar to a learning passport, but with a focus on documenting professional and vocational skills.
• Credential Wallet: Another term for a digital wallet used to store and share credentials.
• Microcredential Portfolio: Collection of small, verified credentials that certify the completion of specific learning or skill-based activities.
• Learning Record Store (LRS): System that stores and tracks learning experiences, typically used in conjunction with xAPI standards to collect and store learning data.
• E-Portfolios: Digital portfolio where learners showcase their work, learning experiences, and credentials in a way that’s accessible to educators and employers.

Refers to a structured sequence of educational experiences that are designed to enable learners to acquire a set of skills or competencies. Each step is intentionally designed to build upon prior learning experiences and often validated through assessments resulting in the award of a microcredential. Some individual sequences are known as a “micro-pathways.”

A learning society is a system-level vision in which a society intentionally organizes its institutions, policies, and resources to support continuous learning and human talent renewal across the lifespan. Learning is understood as lifelong, life-wide, and life-deep, extending well beyond formal education in youth and embedded across work, community, and civic life.

In a learning society, education systems, employers, community organizations, and public policy are aligned to enable ongoing reskilling, upskilling, and personal development throughout adulthood. This coordinated approach supports workforce mobility, economic resilience, civic participation, and social inclusion in the context of longer lives and rapid change.

See also: 100-Year Life Course Approach | Learn & Work Ecosystem Library

Refers to the practice of giving preference in the college admission selection process to alumni relatives such as a parent, grandparent, or sibling. Many highly selective and prestigious higher education use legacy admissions as a factor in their selection process because they place a value on the connections and loyalty that come with having generations of families associated with their institution.

Alternative terms: legacy preferences, alumni connections

A license is a credential awarded by a government agency that constitutes legal authority to do a specific job. Licenses are based on some combination of degree or certificate attainment, certifications, assessments, or work experience; are time-limited, and must be renewed periodically.

Refers to the ongoing, voluntary, and self-motivated pursuit of knowledge for either personal or professional reasons. It includes all learning activities throughout life, ranging from formal education to informal experiences. Lifelong learning emphasizes that learning occurs in diverse contexts and forms across the entire lifespan. In the U.S., the term is widely used in regard to learning in older adulthood.

Open-source library of 32,000+ skills gathered from hundreds of millions of online job postings, profiles, and resumes—updated every two weeks. The Skills Taxonomy collects real-time data from over 40,000 sources every day, contributing to a database with over 1 billion job postings and billions of other data points. These data are combined with curated input from other statistical sources, like government agencies, to provide the most complete view possible of the fast-changing labor market.  This information is used in businesses, communities, and education providers who need the granular details and big-picture trends for their planning and improvement efforts. The Taxonomy focuses in three areas: specialized skills, common skills, and software skills. These are broken down into 30 categories and multiple sub-categories.

Enable linking or binding of data across multiple Learning and Employment Records (LERs) as well as for individual or organizations like employers, to validate one or more assertions in an individual’s LER.

Refers to a California statewide initiative that focuses on real-world approaches to integrate rigorous academics in secondary schools with high-quality career-technical education, work-based learning, and student supports and that meet college-ready standards. Courses and programs are typically organized around industry-sectors, with industry themes woven into lessons taught by teachers who collaborate across subject areas with input from employers and working professionals.  The aim of Linked Learning is to make academic learning relevant to students through real-world applications.

See: Linked Learning Pathways and Linked Learning Certification Pathways

Refers to pathways delivered through various models that are designed to prepare students for success in both college and career. Examples include:

• NAF Academies: Industry-sponsored partnerships that support industry-specific curricula in high school classroom and work-based learning experience, including summer internships and career academies within high schools. Academies focus on themes in fields of study that facilitate college preparation and technical training on career paths in finance, hospitality and tourism, information technology (IT), engineering, and health sciences.
• P-TECH Schools: Students earn a high school diploma, industry-recognized associate degree, and gain relevant work experience in a growing field.

Refers to a policy limit on the total amount of student loan funding that an individual borrower may receive through a particular loan program, typically set by government statute or regulation. In U.S. federal student aid programs, loan caps establish maximum annual and lifetime borrowing limits for different categories of students (e.g., undergraduate, graduate, professional students, or parents borrowing on behalf of students). These limits are intended to constrain excessive borrowing, reduce long-term student debt burdens, and discourage institutions from relying on unlimited federal lending to support rising tuition prices.

Loan caps are most commonly associated with federal student loan programs, where borrowing limits are set by Congress and administered by the U.S. Department of Education. Caps may apply annually (amount that can be borrowed in a single academic year) and in the aggregate (total amount that can be borrowed over a student’s academic career). Different caps may exist for subsidized loans, unsubsidized loans, graduate or professional education, and parent borrowing programs.

In policy debates, loan caps are often discussed as a mechanism for introducing financial discipline into higher education financing systems. Advocates argue that caps can help reduce unsustainable debt levels and encourage institutions to manage program costs. Critics contend that strict borrowing limits may restrict access to high-cost programs—particularly in fields such as medicine, law, or other professional education—unless additional grant aid or alternative financing mechanisms are available.

Loan caps apply primarily to federal loan programs; students may still seek additional financing through private loans, institutional aid, scholarships, or employer-sponsored education benefits when federal borrowing limits are reached.

Refers to academic programs—typically degree or certificate programs—that enroll or graduate a number of students below thresholds set by a state higher education authority, system office, or institutional governing board. These thresholds are used to monitor program viability, ensure efficient use of public resources, and guide decisions about program support, consolidation, redesign, or discontinuation. While definitions vary by state, low-producing programs are commonly identified through multi-year enrollment or completion minimums (e.g., average annual graduates over a 3–5 year period). Programs flagged as low-producing often undergo a formal review process that considers workforce demand, regional needs, economic development priorities, program quality, cost, and mission alignment before any action is taken (continuation, restructuring, or closure).

Examples of State policy language:

• Texas Higher Education Coordinating Board (THECB): A low-producing program is a degree program that “fails to meet minimum standards for numbers of graduates—typically fewer than 25 graduates over 5 years at the bachelor’s and master’s levels, and fewer than 15 graduates over 5 years at the doctoral level.” Programs under review must justify continuation based on workforce need, institutional mission, or demonstrate a plan for improvement.
• Virginia State Council of Higher Education for Virginia (SCHEV):  A low-producing program is one that “does not meet established productivity thresholds over a rolling 5-year period.” Bachelor’s programs are expected to produce at least 7 graduates per year (average), master’s at least 5 graduates, and doctoral programs at least 3 graduates. Programs falling below the threshold must submit a productivity review and improvement plan, merge with another program, or be phased out.
• North Carolina Community College System (NCCCS): A curriculum program is low-producing “when enrollment fails to meet viability benchmarks over a 3-year period.” Programs with fewer than 10 completions in 3 years may be recommended for termination unless the college demonstrates strong local or regional labor-market justification.
• Colorado Department of Higher Education (CDHE): Colorado identifies low-demand or low-productivity programs through its statutory program review process. Programs that “do not meet minimum enrollment or completion standards, or that demonstrate limited alignment with state workforce needs,” may be flagged for corrective action. Institutions must report how such programs contribute to state priorities or detail steps for improvement, consolidation, or discontinuation.
• Kentucky Council on Postsecondary Education (CPE): Considers a program low-producing if it “fails to meet minimum thresholds for average annual degrees conferred over a 5-year period”—generally 10 graduates for bachelor’s programs and 5 for master’s. Programs not meeting standards undergo a review that evaluates costs, demand, duplication, and alignment with statewide strategic objectives.

A workforce model in which human workers and technology “intelligent systems”—such as agentic AI, digital assistants, and autonomous software agents—collaborate with employer teams and are integrated within. These AI systems can address traditional task automations functions plus interpret context, learn from data, take the initiative on efforts, and participate actively in decision-making.

Managing a machine-human workforce requires new leadership capabilities at companies, digital fluency, and updated organizational structures that support collaboration within the newly emerging machine/human workforce.

There is no standardized term yet for this workforce model but other terms are in use, such as:

• Hybrid Human–AI Workforce
• AI-Augmented Workforce
• Human–Machine Collaboration
• Digital Workforce.
• Superagency Systems

See Topic Brief: Machine-Human Workforce | Learn & Work Ecosystem Library

Refers to the use of marketing channels and tools in combination that are commonly used by businesses including higher education institutions to message to their markets. Channels and tools include:

• Advertising: messaging target audiences including developing awareness of brand and customer participation in products/services
• Personal Selling: direct interactions between sales representatives/potential customers
• Direct Marketing: personalized communications aimed at specific individuals
• Sponsorship: associating a brand with events, causes, other
• Public Relations: managing organization’s image, reputation/risk management through media relations, press releases, and other communications
• Social Media: engaging audiences through platforms like websites, Facebook, Instagram, etc.
• Customer Journey: mapping the touchpoints where customers interact with a brand
• Promotion: various activities to boost engagement, sales, and brand visibility

According to the AACRAO Higher Ed Glossary, secondary-education (high school) alternative to a traditional transcript. Currently in use only by private institutions. Does not include standard letter grades but assigns mastery credits.

Matching and hiring platforms are different from job boards though they both aim to connect employers with job seekers.

• A job board is a tool that displays job listings from various employers in a central location. Employers can post their job openings for a fee or sometimes for free. When a job seeker applies to a job listing on a job board, it is the employer’s responsibility to manage the rest of the process with their own screening, scheduling, and tracking tools.
• Matching and hiring platforms offer a more comprehensive solution for employers. In addition to job listings, the platforms may offer features such as applicant tracking, interview scheduling, communication tools and pre-employment assessments. They may also use machine learning to help match job seekers with relevant job openings based on their skills and experience.

According to Indeed, businesses are turning more to matching and hiring platforms to streamline the recruitment process from job posting to job offer.

MCP stands for Meaning–Context–Probability, a concept that explains how information gains meaning when it’s used by people or by artificial intelligence (AI) systems. It helps us understand that information by itself is not enough—its value depends on how it is interpreted and used.

• Meaning refers to what the information represents.
• Context is the situation or setting in which the information is used.
• Probability reflects how likely it is that the information is relevant or accurate in that situation.

AI systems—and people—use all three parts together.  In today’s AI-driven world, understanding MCP helps users recognize that information is dynamic. The same data can have different meanings in different contexts—such as a “certificate” in higher education versus a “certificate” in cybersecurity training. AI tools rely on MCP principles to interpret search requests, connect related information, and deliver relevant results. For example, the Learn & Work Ecosystem Library Assistant bot (search tool) interprets a query based on what you typed (meaning), what part of the Library you’re searching (context), and what information is most likely to answer your question (probability). By keeping meaning, context, and probability in balance, the Library—and other AI-powered information systems—can help users find information that is not only accurate but also useful and understandable for their purpose.

Tips for Library users to apply MCP when searching with the AI Assistant bot at our site:

• Be clear about what you want to know. Example: Instead of “Tell me about credentials,” try “What are examples of stackable credentials in healthcare?” [meaning]
• Add where or how you’ll use the information. Example: “Explain credential transparency policies in community colleges.”  [context]
• Include what matters most to you—such as “most recent,” “U.S. examples,” or “employer-focused initiatives.”   [probability]

Related Terms

• Artificial Intelligence (AI) – the simulation of human intelligence in machines that can process information, learn, and make decisions.
• Digital Literacy – the ability to use digital tools and technologies to find, create, and communicate information.
• Information Architecture – the structured design of how information is organized, connected, and retrieved in a system.
• Information Literacy – the ability to locate, evaluate, and use information appropriately and responsibly.
• Metadata – data that describes other data to help systems and users find, understand, and use information effectively.

Refers to a rising number higher education institutions with very large enrollments and which commonly focus on flexibility, affordability, accessibility, and teaching and learning. Many of the mega-universities are optimized for working adults and have developed flexible online platforms for learning, often enhanced with artificial intelligence. Examples:

• Western Governor’s University (WGU): With over 156,000 students, its competency-based education model enables students to demonstrate skill acquisition and complete courses at their own pace, instead of within fixed credit hours. WGU has developed the Aera Decision Cloud platform to enable AI to predict student outcomes and recommend interventions to faculty.
• Arizona State University (ASU): With more than 150,000 students, it has expanded its online platform, ASU Digital Immersion, and with its partnership with OpenAI has launched an AI Innovation Challenge for faculty and staff to promote AI solutions across in all areas on campus.
• Southern New Hampshire University (SNHU): With approximately 158,000 students, its AI-powered chatbot “Penny” is designed to advise students on academics, finances, and wellness and has already increased student retention and performance.

Student experiences often differ in a mega-university from a traditional university. Students at a mega-university may never meet their professors because they’re taking asynchronous courses that provide little if any interaction with faculty. Students may also have few, if any, opportunities for interactive experiences on campus.

The role of faculty members may also differ. Institutions like SNHU and WGU have unbundled the role of the faculty member, to instead provide a life coach who stays with the student throughout matriculation. Additional features at these universities include teaching coaches plus expertise in instruction within the faculty such as WGU’s faculty who are dedicated to assessment.

See Topic: The Rise of Mega-Universities | Learn & Work Ecosystem Library

A new approach to hiring and workplace diversity is gaining traction across corporate America, challenging DEI (diversity, equity, and inclusion) initiatives. The emerging movement advocates for MEI (merit, excellence, and intelligence), which emphasizes selecting candidates for jobs based solely on their qualifications, abilities, and intelligence. Proponents argue that MEI offers a more equitable and effective method for building high-performing teams, moving away from demographic considerations to focus exclusively on individual merit.

Refers to information that describes, organizes, and connects other information. It is often called “data about data” because it helps explain what something is, how to find it, and how it is structured. Metadata is used in libraries, websites, and digital systems to make information easier to search for and use.

There are different types of metadata, including:

• Descriptive metadata (like a book’s title, author, and keywords)
• Access metadata (information that helps people find and use a resource)
• Structural metadata (details about how information is organized or linked)

Metadata is also known as cataloging information, indexing data, or linked data when it connects different pieces of information in a way both people and computers can understand.

At the Learn & Work Ecosystem Library, all public resource descriptions are metadata.

As defined by Education Design Lab (the Lab), micro-pathways are:

• Co-designed with learners and employers
• Consist of two or more stackable credentials
• Include a durable skills micro-credential
• Flexibly delivered to be achieved within less than a year
• Result in a job at or above the local median wage
• Start learner-earners on the path to an associate degree.

A micro-pathway can be packaged as a validated market signal connecting learners to employment in high-growth careers.

Example: Scaling Learner Opportunity and Economic Growth Across Arkansas – Education Design Lab | Learn & Work Ecosystem Library

Microcredentials (micro-credentials) are a record of focused learning achievement verifying what the learner knows, understands, or can do. They include an assessment based on clearly defined standards and are awarded by a trusted provider. They have stand-alone value and may also contribute to or complement other micro-credentials or macro-credentials, including through recognition of prior learning. They meet the standards required by relevant quality assurance.

In recent years, there has been a growth in alternative, highly tailored, and smaller education programs for children. These are often referred to as microschools. In 2022, 1.1 to 2.2 million learners were estimated to attend microschools as primary replacements for typical K-12 schooling environments.

A microschool often has fewer than 15 students of different ages (some schools grow larger) and a teacher to organize curriculum and guide the learning process. Depending on the policy and regulatory frameworks of a state and local governments, microschools can be organized as centers serving learners following rules for home-schoolers, private schools (accredited or nonaccredited), and sometimes as public charter or entities within traditional public schools.

Hybrid models are growing as well. Hybrid schools allow learners to attend less than 5 days per week — to divide learning among multiple sources such as attending a microschool for a certain number of days per week, and adding learning from private classes, tutors or other experiences.

Facilities for microschools include storefronts, adapted rooms in a home, commercial or office space, houses of worship, or places of business.

According to the National Microschooling Center, three common models are:

• Independent Microschools – resemble one-room schoolhouses. These are often created by an individual, team, or group of families.
• Partnership Microschools – a host partner such as an employer, local government, nonprofit, or house of worship working in partnership with a technical partner who oversees teaching and learning.
• Provider Network Microschools – offering varying degrees of flexibility for instruction with different types of institutional supports.

A standalone website or single web page. These sites are typically used to promote a company’s products, services, campaigns, events, or brand. Microsites often have a different domain or subdomain from the company’s main website.

In postsecondary education, microsites are increasingly being used to house information (course offerings, payment, shopping cart experience, etc.) for specific audiences such as those seeking continuing studies and professional development offerings.

Example:  In Ontario, Canada, Western University’s Continuing Studies microsite houses non-credit and credit-bearing certificates and professional education offerings. The site recognizes that a distinct web experience designed to attract and engage customers seeking continuing studies programs and courses is needed—that customers for the microsite are different than visitors the main campus website is set up to serve.

Middle skills jobs require more than a high school diploma but less than a four-year college degree and pay over $15 per hour. Jobs that currently require only a high school diploma are projected to experience negative job growth in the coming years and be replaced by middle-skills jobs. Middle-skills credentials are typically defined as postsecondary sub-baccalaureate certificates and associate’s degrees.

According to a 2024 report by the Georgetown University Center on Education and the Workforce (Opportunities: Credential Shortages in Programs Aligned with High-Paying Middle- Skills Jobs in 55 US Metro Areas), high-paying middle-skills occupations are those in which more than half of early-career middle-skills workers have a job with annual earnings of more than $53,000 (in 2022 dollars). Workers in these jobs outearn most young workers with a bachelor’s degree plus experience considerable earnings growth over time, with median annual earnings that rise to $80,000 by mid-career (ages 36–49). The report identifies 107 occupations as high-paying for middle-skills workers – such as firefighters, facilities managers, information security analysts, power plant operators, and radiologic technicians. These high-paying occupations are found in five occupational groups: (1) blue-collar; (2) management; (3) protective services; (4) science, technology, engineering, and mathematics (STEM); and (5) healthcare.

Classification of protected categories for individuals with military service include:

• Disabled veteran: A veteran of the U.S. military, ground, naval, or air service who is entitled to compensation (or who but for the receipt of military retired pay would be entitled to compensation) under laws administered by the U.S. Secretary of Veterans Affairs; or a person who was discharged or released from active duty because of a service-connected disability.
• Recently separated veteran: A veteran during the three-year period beginning on the date of such veteran’s discharge or release from active duty in the U.S. military, ground, naval, or air service.
• Active-duty wartime or campaign badge veteran: A veteran who served on active duty in the U.S. military, ground, naval, or air service during a war, or in a campaign or expedition for which a campaign badge has been authorized under the laws administered by the Department of Defense.
• Armed forces service medal veteran: A veteran who, while serving on active duty in the U.S. military, ground, naval, or air service, participated in a U.S. military operation for which an Armed Forces service medal was awarded pursuant to Executive Order 12985.

Refers to the system used by the U.S. military to classify and identify specific job roles and skills within the armed forces. Each MOS code corresponds to a particular job or specialty, such as infantry, logistics, or medical services. These codes are often used to translate military experience into civilian job qualifications.

The MOS Skills Code provides a critical bridge between military experience and civilian career opportunities, enabling the translation of specialized skills and training acquired during military service into terms that are understandable and applicable in the civilian workforce. This alignment is vital for several reasons:

• Facilitating Career Transitions: Helps veterans and active-duty service members transition into civilian careers by mapping their military experience to equivalent civilian job roles. This ensures that their unique skills are recognized and valued in the labor market.
• Credential Interoperability: By linking MOS codes to civilian credentials, industry certifications, and educational pathways, the codes enable military personnel to receive credit for their prior learning and experience. This can reduce redundancy in training and accelerate progress toward educational and career goals.
• Supporting Skills-Based Hiring: Employers increasingly rely on skills-based hiring practices, and MOS codes provide a standardized way to identify and evaluate the competencies of military personnel. This helps employers match veterans to roles that align with their expertise, fostering a more inclusive and efficient hiring process.
• Enhancing Workforce Development: State agencies and educational institutions use MOS codes to design programs and policies that support veterans. For example, military crosswalks use these codes to align military training with state job classification systems, ensuring that veterans can seamlessly integrate into the workforce.
• Promoting Equity in Education: Policies like the Higher Education Opportunity Act, which allows active-duty military personnel and their families to pay in-state tuition, emphasizes the importance of recognizing and leveraging MOS codes to support educational access and affordability.

Diagram that shows the relationships among ideas to help users better understand, remember, and communicate information. Mind maps generally organize information into a hierarchy, showing relationships among pieces of the whole. A central concept or idea is usually placed in the middle of a spider diagram, with associated concepts/ideas that are connected branching out from the center (key words are called nodes).

See Relational Map (used at Learn & Work Ecosystem Library)

Refers to a broad category of types of institutions that serve minority populations. This includes Hispanic-serving institutions (HSIs), Historically Black Colleges and Universities (HBCUs), Tribal Colleges and Universities (TCUs), and Asian American and Native American Pacific Islander-serving Institutions (AANAPISIs). Each of these institutions serves a particular minority population.  There are approximately 700 MSIs in the U.S.

Refers to a quantitative study that incorporates qualitative research methods in order to provide a richer understanding of a program’s implementation and effectiveness (e.g., interviews, focus groups, case studies, observations).

Refer to postsecondary students who move among higher education institutions and who may be seeking to transfer academic credit they have acquired (college level learning) laterally, vertically, or through reverse transfer while completing a degree or credential.

Modular learning unbundles the traditional learning “packages”—Associate’s, Bachelor’s, and Master’s degrees—into more manageable learning chunks that are also tied to real career and life outcomes. Modular learning enables working professionals to learn new skills in shorter amounts of time, even while they work, and those seeking a degree are able to do so in a much more attainable way. They also earn credentials for the smaller modules of learning, thereby garnering value and positive feedback early in the process of advancing towards full degrees.

Massive Open Online Courses (MOOCs) are online courses designed for large-scale participation and open access, typically delivered through digital platforms and available without formal admissions requirements. Since their emergence in the late 2000s, MOOCs have influenced discussions about access, affordability, pedagogy, credentialing, and the relationship between higher education and workforce development.

Most MOOCs remain noncredit, offering certificates of completion and, in some cases, digital badges. However, some institutions and platforms enable MOOCs to carry academic credit or stack into certificates, microcredentials, or college degree programs, positioning them as flexible on-ramps within lifelong learning and workforce development ecosystems rather than standalone replacements for higher education.

Within the learn-and-work ecosystem MOOCs are best understood not as a single, fixed model, but as an evolving set of instructional and credentialing approaches shaped by rapidly changing labor market demand, technological change, and global participation patterns by individuals seeking higher education and continuing professional development.

See Topic Brief: MOOCs (Massive Open Online Courses) | Learn & Work Ecosystem Library

In the learn-and-work ecosystem, multi-organization collaborations refer to groups of organizations that voluntarily work together—formally or informally—to pursue shared goals related to research, innovation, policy, workforce development, credentialing, and/or system improvement. Common characteristics include shared purpose and mutual benefit, cross-organizational participation, varying levels of formality, and may be ongoing or time-limited

Forms of multi-organization collaborations include:

• Alliance – A collaborative partnership that may be formal or informal, often used as a flexible term for multi-stakeholder, cross-sector efforts focused on advancing shared missions.
• Coalition – A group of organizations temporarily or permanently aligned to advocate for a policy, cause, or system reform.
• Collaborative-A group working together on joint projects or shared objectives, often emphasizing co-creation or co-design. The collaborative often emphasizes shared ownership and work.
• Consortium/Consortia– A group of organizations (often educational institutions, but can include employers, government agencies, nonprofits, and others) that voluntarily come together to collaborate on shared goals such as research, innovation, policy development, resource sharing, and/or joint programming. Consortia often have a formal structure, governance, shared funding, and clearly articulated objectives.
• Network – A loosely connected group of individuals or organizations sharing information, best practices, or informal collaborations. They may be ad hoc, and have low or no formal governance
• Partnership – Two or more organizations working together under a formal or informal agreement. Partnerships can range from simple agreements to complex joint ventures.
• Task Forces / Blue Ribbon Committees – A panel of experts convened (typically short-term) to study or make recommendations on particular issues.

See Topic Report: Multi-Organization Collaborations in the Learn-and-Work Ecosystem | Learn & Work Ecosystem Library

Multi-state data collaboratives are regional networks (coalitions) of state workforce, education, human services, and other agencies that partner with each other and with regional postsecondary education partners to produce data products that policymakers, practitioners, and citizens use to inform policy developments and answer questions critical to society. An example is the Multi-State Data Collaborative (MSDC) led by the National Association of Workforce Agencies (NASWA).

According to EDUCAUSE, refers to a group of two or more colleges or universities—each with substantial autonomy and headed by a chief executive or operating officer—that fall under a single governing board served by a system chief executive officer who is not also the chief executive officer of any of the individual higher education institutions. Such a system is to be distinguished from a “flagship” campus with branch campuses and also from a group of campuses or systems, each with its own governing board, that is coordinated by some state body.

According to Credential Engine, refers to specifications for secure digital wallets for sharing transcripts and credentials, built on PESC standards, and managed by the Association of Registrars of the Universities and Colleges of Canada.

Eight competencies for career readiness:

1. Career & Self-Development: Proactively develop oneself and one’s career through continual personal and professional learning, awareness of one’s strengths and weaknesses, navigation of career opportunities, and networking to build relationships within and without one’s organization.
2. Communication: Clearly and effectively exchange information, ideas, facts, and perspectives with persons inside and outside of an organization.
3. Critical Thinking: Identify and respond to needs based upon an understanding of situational context and logical analysis of relevant information.
4. Equity & Inclusion: Demonstrate the awareness, attitude, knowledge, and skills required to equitably engage and include people from different local and global cultures. Engage in anti-oppressive practices that actively challenge the systems, structures, and policies of racism and inequity.
5. Leadership: Recognize and capitalize on personal and team strengths to achieve organizational goals.
6. Professionalism: Knowing work environments differ greatly, understand and demonstrate effective work habits, and act in the interest of the larger community and workplace.
7. Teamwork: Build and maintain collaborative relationships to work effectively toward common goals, while appreciating diverse viewpoints and shared responsibilities.
8. Technology: Understand and leverage technologies ethically to enhance efficiencies, complete tasks, and accomplish goals.

According to the AACRAO Higher Ed Glossary, a project-and-skills-based educational program. Once competency is demonstrated, a learner is issued a type of recognition of learning, affirming mastery of skills.

NASPA stands for the National Association of Student Personnel Administrators. It is a U.S.-based professional organization for student affairs administrators in higher education.

Over 15,000 members globally, primarily from colleges and universities. Includes professionals who work in areas like student life, housing, academic advising, career services, diversity and inclusion, student conduct, wellness, leadership development, and more.

NASPA’s mission is to support the advancement, health, and sustainability of the student affairs profession. It provides:

• Professional development (conferences, webinars, certificate programs)

• Research and publications

• Policy advocacy

• Standards of professional practice

• Networking and leadership opportunities

Major Activities include:

• Annual Conference: One of the largest student affairs gatherings globally.

• Knowledge Communities: Focus groups around specific student affairs topics (e.g., First-generation students, Equity & Inclusion, Mental Health).

• Publications: Journals like The Journal of Student Affairs Research and Practice (JSARP) and Leadership Exchange.

• Advocacy: Engages in public policy affecting higher education and student services.

As defined by CredLens, refers to an entity with a defined legal and technical framework for managing and governing data on behalf of a group or community, with a focus on national or large-scale public interest data. ‍The trust acts as a steward of the data, often with the goal of enhancing transparency, fostering innovation and supporting public good initiatives while ensuring compliance with privacy laws and regulations.

As described by the Education Strategy Group, refers to an approach that improves access to high-quality college and career planning support in high schools.  Programs (1) enlist students who are further along in their educational journey as guides and mentors to support newer students, and (2) provide targeted resources and guidance to improve learner persistence. Various models are used to structure near-peer mentoring programs: some employ recent college graduates, some use current college students to support high school students. Examples include:

• College Advising Corps (CAC) – Places fulltime college advisors in under-resourced high schools across the country. As of 2022, CAC reached over 200,000 high school seniors and 71% of seniors submitted one or more college applications.
• AdviseMI – Hosted by the Michigan College Access Network, advises MI partners with 16 higher education institutions statewide to place recent college graduates in high schools with low college-going rates and provide near-peer advising to support students as they transition into postsecondary.
• College Access and Research (CARA)‘s peer-to-peer advising programs (New York-based) resulted in a 19% increase in postsecondary enrollment in schools that offer year-round advising. Since college student mentors have recent experience navigating the complex college admissions process, they are often well equipped to talk students through both the logistical components and emotional barriers that come up when applying to and enrolling in a postsecondary institution.

An acronym used by governments that stands for young people (typically ages 15 – 24) who are “Not in Education, Employment, or Training.”  Individuals categorized as NEET by governments are commonly unemployed, not enrolled in an education or vocational training program, not engaged in housework, and not seeking work.  The term is used to describe young people in order to exclude people in the retirement category (an older-age group).

The term emerged in the late 1990s in the United Kingdom and is now widely used among many countries including the United States.

Related Terms linked to unemployment: anti-unemployment, displaced, frictional unemployment, idle (not active or working)

A tuition policy that allows students from states geographically adjacent (i.e., contiguous or bordering) to a college or university to pay reduced tuition rates—often equivalent to in-state or near in-state levels. These programs are designed to increase regional access to higher education, improve enrollment, and support cross-border collaboration. The specific eligibility criteria and tuition discounts vary by institution and state policy.

Examples of states and institutions offering neighboring state rates:

• Kansas — Many Kansas public institutions offer the Resident Tuition for Missouri Residents rate.
• For example, Pittsburg State University and Fort Hays State University extend in-state rates to students from bordering states like Missouri, Nebraska, and Oklahoma.
• West Virginia — The Metro Rate offered by institutions like Marshall University and West Virginia University provides discounted tuition to residents of neighboring states such as Ohio, Pennsylvania, Kentucky, and Maryland.
• Arkansas — The Border State Waiver Program allows students from contiguous states (like Texas, Louisiana, Mississippi, Missouri, Tennessee, and Oklahoma) to pay in-state tuition at several public colleges and universities.
• South Dakota — South Dakota State University and others offer in-state or discounted rates to students from neighboring states like North Dakota, Nebraska, Iowa, and Minnesota through Reciprocity Agreements and Reduced Tuition Programs.
• Nevada — The Good Neighbor Policy at some institutions, like College of Southern Nevada, provides in-state tuition to students from neighboring counties in California and Arizona.

Neighboring State Rates are often distinct from regional reciprocity programs like the Midwest Student Exchange Program (MSEP) or Academic Common Market, though institutions may participate in both.

See Topic Brief: Tuition Reciprocity & Regional Tuition Exchange Programs | Learn & Work Ecosystem Library

Since 2011, each postsecondary institution participating in Title IV federal student aid programs (enrolling fulltime, first-time degree- or certificate-seeking undergraduate students) has been required to post a net price calculator on its website, in accordance with the Higher Education Act of 1965 (HEA), as amended on October 29, 2011.  The calculator uses institutional data to provide estimated net price information to current and prospective students and their families based on a student’s individual circumstances.  The calculator also allows students to calculate an estimated net price of attendance at the institution (defined as cost of attendance minus grant and scholarship aid) based on what similar students paid in a previous year. Both input and output elements are included in the calculator:

• Input elements include data elements to approximate the student’s Expected Family Contribution (EFC), such as income, number in family, and dependency status or factors that estimate dependency status.
• Output elements include:  Estimated total cost of attendance; Estimated tuition and fees; Estimated room and board; Estimated books and supplies; Estimated other expenses (personal expenses, transportation, etc.); Estimated total grant aid; Estimated net price; Percent of the cohort (full-time, first-time students) that received grant aid; and Caveats and disclaimers, as indicated in the HEA.

A network is a group or system of interconnected people or things.

Networks approaches to addressing issues in the learn-and-work ecosystem bring together individuals, organizations, or entities in an interconnected whole to address complex social or systematic challenges. Unlike traditional organizations or hierarchical structures, networks are typically decentralized, flexible, and adaptive, allowing participants to share knowledge, resources, and strategies in a coordinated way.

Neurodiversity recognizes that everybody’s brains work differently and these differences are normal. Neurodivergent is an umbrella term that refers to people who have autism spectrum disorder, ADHD, dyslexia, or other atypical ways of thinking, learning and interacting with others. Neurodivergent is not a medical diagnosis.

Organizations in the learn-and-work ecosystem are developing strategies to help make education and training more hospitable to neurodivergent learners, be they students or employees. Strategies typically focus on empowering learners and employees with ways to address the challenges they may face related to executive functioning, socialization, and self-care. Strategies are also needed to equip teachers and employers to work effectively with neurodiverse populations.

A measure that organizations use to assess the strength of their recruiting process. The new hire retention rate calculates the percentage of new hires (e.g., middle management, specialists, operational workers, office staff, senior management, executives) who are still employed at the business entity 12 months after accepting a job offer.

Refers to individuals entering the workforce for the first time. This group typically includes recent graduates from high school or college seeking their initial employment opportunities. These individuals often lack significant work experience in their chosen fields but may have relevant educational qualifications or internships.

Refers to the emerging realities and implications of significantly longer human life spans, driven by advances in healthcare, technology, and quality of life. It encompasses the social, economic, and personal transformations that result from people living well into their 80s, 90s, and beyond.

This concept challenges traditional models of life stages—such as fixed periods for education, work, and retirement—and prompts rethinking how society supports individuals across a longer life course. The new longevity influences workforce participation, lifelong learning, caregiving structures, retirement planning, and health system design.

Key elements of new longevity:

• Increased lifespan and focus on extending “healthspan” (years of healthy living)
• Redesign of education, work, and retirement timelines
• Growth of multigenerational workforces and new career trajectories
• Demand for lifelong learning and upskilling
• Expansion of longevity-related products, services, and policies

Related Terms: Lifelong Learning, Age-Friendly Universities, Multigenerational Workforce, Longevity Economy, Healthspan, Second Careers

A term coined by the Institute for the Future (IFTF) in 2021 referring to a wholesale reimagining of higher education and how it functions with and within a larger regional system to improve social mobility and equity for all its citizens. IFTF interviewed a cross-section of California learners at different stages of their learning journeys to explore the successes and challenges learners encounter in pursuing a higher education degree. The key findings determined to positively or negatively directly impact higher education in the future: 1) expected increase in demand for degrees, 2) ongoing seismic reorganization of work, and 3) widening inequality and racial wealth gap. The study determined a new model is needed: a place-based higher education system (A New Multiversity) to serve as an engine of community wealth-building and socio-economic mobility.

The term, new-collar workers, was introduced by the CEO of IBM in 2016, to refer to “middle-skill” occupations in technology, such as cybersecurity analysts, application developers, and cloud computing specialists. The term added to the existing terms — white collar-workers and blue-collar workers.

New-collar workers is used more broadly now to refer to individuals who develop technical and soft skills needed to work in many industry sectors through nontraditional education paths.  These are often alternative paths that do not require a traditional baccalaureate degree though they may include an internship or apprenticeship to develop technical and soft skills; or may include re-skilling or upskilling of current employees. Examples of jobs are:  sonographer; pharmacy technician; medical assistant; field service engineer; dental assistant; and a range of technology-oriented jobs.

Trends in new-collar hiring are linked to skills-first hiring, which refers to employer hiring practices that create on-ramps for people previously overlooked in traditional hiring— to build a pipeline of capable non-degreed workers.  A skills-first approach creates entry points and on-ramps for newcomers of varied backgrounds, and also creates a skills-based approach to promotion and development for all employees. These processes enable companies to advance overlooked talent and increase racial and socioeconomic diversity in the entire workforce and the leadership pipeline.

The Aurora Institute defines next gen credentials as a modern type of credential that more accurately and transparently reports the knowledge, skills, and competencies attained by individuals. They are often digital. Over time, these credentials are designed to communicate what individuals know and what skills they have attained in a lifelong Learner Education and Employment Record (LER). The learner record may be thought of as the “backpack” for a learner, with next gen credentials the verified attestations of knowledge, skills, academic credits, and competencies they can show to employers and education and training providers.

Refers to an undergraduate degree offered in the State of Georgia in the U.S. It requires fewer credit hours than a bachelor’s degree which is commonly 120 credit semester hours. The nexus degree is often viewed as a degree that falls between the associate degree and the bachelor’s degree. The nexus includes experiential learning and upper-division coursework typical of bachelor’s degrees, and emphasizes connections between industry, skilled knowledge, and hands-on experience in high-demand career areas such as cybersecurity and financial technology. The nexus is designed for individuals who have not earned a degree, who have a degree but are seeking to transition into a high-demand career field, or who are pursuing a bachelor’s degree who want to add a targeted credential to their coursework.

Refer to specialized higher education programs that are typically designed to cater to specific high-demand industries or unique interests. These programs are generally not widely available elsewhere.

Examples of nearly 30 niche programs offered by higher education institutions:

1. Automotive Restoration—Combining business and management skills, preparing students for careers in the automotive industry.
2. Esports Management—Electronic sports equip students with skills for jobs in game design, team management, and broadcasting areas.
3. Ceramic Engineering—Materials science with a focus on the artistic techniques of ceramics, preparing students for industries such as manufacturing, tech, and healthcare.
4. Industrial and Labor Relations—Blends economics, law, and social sciences to prepare students for human resources management, labor and industrial relations, and other careers.
5. Environmental Studies & Sustainability—Emphasizes sustainability and environmental studies that combine ecological science with social justice initiatives.
6. Human Ecology—Interdisciplinary field that blends social science, humanities, and natural science to prepare students for sustainability, conservation, and policymaking job roles.
7. Sustainable Agriculture—Regenerative farming practices and renewable energy program that prepare students with hands-on experience in wind, solar, and hydroelectric power generation.
8. Aerospace Engineering—Pathways to careers in aviation and space exploration.
9. Creative Writing for Entertainment—Tailored for students who want to write for film, television, video games, and other media, with a focus on blending storytelling with technical skills.
10. Optical Sciences and Engineering—Focuses on the science of light, optics, and photonics, equipping students with the skills to work in cutting-edge industries such as telecommunications, defense, and biomedical technology.
11. Game Design—Focus on programming and narrative design, providing students with the tools to work in the gaming industry.
12. Industrial Archaeology—Combines archaeology, history, anthropology, and engineering elements to preserve and interpret America’s industrial heritage such as the physical remains of industrial and technological activities (e.g., former mining sites and industrial complexes).
13. Craft Programs—Students learn techniques combined with contemporary applications and business skills in traditional crafts such as blacksmithing, fiber arts, and woodworking programs
14. Electric Vehicle Technology—EV systems, battery technology, and specialized diagnostics.
15. Therapeutic Recreation—Using recreational activities to improve the physical and mental health of individuals with disabilities or illnesses.
16. Packaging Science—The design, development, and sustainability of packaging materials, to prepare students for jobs in industries ranging from food to pharmaceuticals.
17. Adventure Education—Preparing students to lead outdoor expeditions and teach leadership skills through activities like rock climbing, kayaking, and wilderness survival.
18. Turfgrass Science—Combining agronomy, biology, and business to prepare for careers in golf course management, sports field maintenance, and lawn care.
19. Comic Art—For aspiring comic book artists, storytelling, illustration, and graphic novel creation.
20. Fermentation Science—Combining chemistry, biology, and entrepreneurship, with a focus on the science behind brewing beer, fermenting foods, and producing biofuels.
21. Viticulture & Oenology—The science of grape growing and winemaking, preparing students for careers in the wine industry.
22. Luthiery — String instrument repair and construction program.
23. Theme Park Engineering—Designing and engineering attractions for the entertainment industry, combining mechanical engineering with creative design.
24. Ecotourism—Focused on sustainable travel and outdoor recreation management.
25. Puppetry—Training in performance, design, and construction of puppets for theater, film, and television.
26. Citrus Science—Preparing for jobs in cultivation, management, and the business of citrus production.
27. Nautical Archaeology—Underwater archaeology, training students to explore and preserve shipwrecks and submerged cultural sites.
28. Space Law—Courses on space exploration and commercialization’s legal and regulatory aspects.
29. Mortuary Science—Combining science, ethics, and business management, preparing student for careers in mortuary science.

Refers to federal regulation (90/10 rule) overseen by the U.S. Department of Education that governs for-profit higher education institutions. The rule is a proxy for measuring educational value at proprietary institutions with the intent to ensure that schools do not overly rely on federal aid and to encourage diversification of funding. Through a cap on federal funding, a proprietary school can receive a maximum of 90% of its revenue from federal financial aid sources such as Pell Grants and federal loans. The remaining 10% of revenue a proprietary school can receive must come from alternative sources, excluding federal funds. Federal policy subsequently updated the 90/10 rule to require proprietary institutions to receive at least 10% of their revenue from nonfederal educational assistance sources every fiscal year. This change was made to enhance financial stability and accountability within the for-profit education sector.

Refers to a job market in which hiring and layoffs have slowed and resignations are down (also referred to as a no-hire, no-fire market), resulting in less churn in the market.

Refer to the practice of companies agreeing not to recruit or hire each other’s employees.  No-poaching agreements may unfairly treat low-wage workers by keeping them locked in low-paying jobs without opportunities for advancement. These agreements are illegal under federal and state antitrust law. Employers that enter into these agreements may face civil and criminal penalties.

No-poach agreements also pertain to higher education institutions. They may face lawsuits for “no-poach” agreements that restrict faculty hiring across institutions.

See: Antitrust Lawsuits in Higher Education and Businesses | Learn & Work Ecosystem Library

As defined by the Velocity Network Foundation, notary operators are independent organizations certified by a Network’s governance to maintain the Network’s core infrastructure. They run validation nodes, participate in the consensus protocol, and ensure the network’s integrity.

Notary issuers are non-primary source orgs certified to issue credentials after a primary source check. certified by the Network to verify and issue verifiable career and education credentials.

A specification in a contract between an employer and employee that prevents the employee from working for a competing employer or starting a competing business after the worker’s employment ends. The restriction typically applies to a stated time period and geographical area.  On April 24, 2024, the Federal Trade Commission (FTC) voted to ban new non-compete agreements with all employees, including senior executives.  Existing non-compete agreements with senior executives remain enforceable but all other workers with non-compete agreements are unenforceable. Non-compete agreements have been designed historically to protect company secrets and intellectual property and encourage investment in employee training.  The new FTC rule affects workers employed by for-profit businesses; it does not apply to non-profit organizations. The rule goes into effect 120 days following publication in the Federal Register, however, it may be subject to court challenges.

Non-degree credentials include certificates, industry certifications, apprenticeships, educational certificates, occupational licenses, and digital badges.

For many decades, the traditional path to careers has been linear: go to school, earn a degree, get a job, and climb the career success ladder. The linear approach has generally been viewed as the safest path to professional achievement. Characteristics of linear careers include:

• Rigid structures: Often require dedication to a single path, including relocating for promotions, working inflexible hours, and/or postponing family plans given the demands of a career.
• Delayed fulfillment: Assumes career satisfaction is earned through decades of “climbing the ladder,” to be enjoyed upon retirement.
• Vulnerability in a dynamic economy: There is vulnerability when professional identity and sense of purpose are tied to a single career path. In a rapidly changing economy, job layoffs impact the stability often expected in linear careers.

Non-linear careers are a growing trend in career development. They enable professionals greater versatility in career development, especially the development of diverse skill sets. Characteristics of non-linear careers include:

• Recognition of longer work life: It is projected that many workers will have a 60-year work life versus the current 40-year work life. A longer work life is more amenable to versatility in career paths.
• Changes in the value of work: Increasingly, professionals are prioritizing meaning, work-life balance, and personal growth over traditional advancement.
• Rapidly changing workplaces and economy: With growing recognition that skills in demand today can become obsolete tomorrow and new roles will emerge, many employers are embracing skills-based hiring to create opportunities for career switchers seeking new paths better aligned with their evolving interests.

Non-credit education includes any course or program that did not go through the process to be for-credit at a community college or university. They typically include personal enrichment classes, customized training for employers, English as a second language classes, and adult basic education. Many higher education institutions develop noncredit to credit bridge pathways to enable learners to earn credit for learning acquired through noncredit courses and programs.

Refers to economic mobility for learners who start their education and career journeys in noncredit programs. Noncredit programs are typically shorter-term training opportunities that enable learners to gain specific workforce skills and qualifications; however, they usually do not count toward Associate or Bachelor’s degree requirements and this raises concern about the economic mobility learners experience who have completed noncredit programs. Factors often studied in determining noncredit mobility are (1) access to family-wage jobs and (2) access to further educational opportunities, especially noncredit to credit pathways.

Noncredit courses are designed for students who wish to advance their educational and career goals. There are a variety of bridge tools institutions can use to strengthen how noncredit courses translate to academic credits. Some schools will create formalized articulation agreements or internal equivalency agreements to illustrate how a noncredit course, industry certification, and credited course articulate. Another method is to cross-list courses within a learning management system and standardize learning outcomes, performance expectations, and faculty qualifications between credit and noncredit courses. Prior Learning Assessment (PLA) is also a common method for providing credit to students who can demonstrate competency based on work or noncredit course experience and education.

The standard used by Federal statistical agencies in classifying business establishments for the purpose of collecting, analyzing, and publishing statistical data related to the U.S. business economy.

A behavioral science-based approach that uses prompts, reminders, or cues—often delivered via digital platforms—to influence individual decision-making and encourage desired actions without mandating them. Nudges can take the form of text messages, app notifications, email prompts, or embedded digital cues within learning and workforce platforms. This term is increasingly embedded in both education and workforce innovation strategies:

• K-12 schools, colleges, universities, training providers, and edtech companies are using nudge technology (or “behavioral nudges”) to improve student success, enrollment, retention, financial aid compliance, and completion rates. Examples:
  • Text message nudges remind students of deadlines or class attendance.
  • Behavioral prompts encourage students to meet with advisors, register early, or apply for scholarships.
  • Platforms like Civitas Learning, EdSights, Mainstay (formerly AdmitHub), and Signal Vine are commercial providers applying nudge strategies.
• Employers, workforce boards, and workforce tech platforms are using nudge technology to support worker upskilling, job retention, wellbeing, and even productivity. Examples:
  • Prompt workers to engage in training programs or microlearning.
  • Reminders to complete certifications, renew credentials, or apply for promotions.
  • Nudges are embedded in Learning Experience Platforms (LXPs), HR systems, or coaching apps.
  • Use in behavior-based safety interventions, wellness programs, and use of employees’ benefits.
• Cross-cutting Applications
  • Nudge technology plays a role in career navigation tools, skills wallets, and Learning & Employment Records (LERs), by prompting individuals to update records, pursue new learning, or explore job opportunities.

O*NET Standard Occupation Codes (O*NET SOC) define the set of occupations across the world of work. Based on the Standard Occupational Classification, the taxonomy includes more than 900 occupations which currently have, or are scheduled to have, data collected from job incumbents or occupation experts. Information in the U.S. Department of Labor’s O*NET database includes information on skills, abilities, knowledge, work activities, and interests associated with occupations. This information can be used to facilitate career exploration, vocational counseling, and a variety of human resources functions (e.g., developing job orders, developing employment position descriptions, and aligning training with current workplace needs).

The O*NET Resource Center (information portal) draws from the O*NET Database and is the nation’s primary source of occupational information. It contains data and tools for workforce professionals and developers, including:

• Current O*NET data files
• Interest Profiler
• License agreements
• O*NET Content Model
• O*NET-SOC occupation taxonomy
• References
• Reports and documents
• Web Services

O*NET OnLine has detailed descriptions of the world of work for use by job seekers, workforce development and Human Resource (HR) professionals, students, developers, researchers, and others. Users can search across 900+ occupations based on their goals and needs. The site also offers comprehensive reports to learn about requirements, characteristics, and available opportunities for selected occupations. A customized OnLine Help feature is available throughout the site, or a Desk Aid. Options are available on the home page, or searchable at the site: Search the O*NET Resource Center

Occupational mobility (also known as occupational labor mobility) refers to the ability of workers to switch career fields to find gainful employment or to meet the needs of industry. Governments (federal, state) often play a role in providing occupational retraining to help workers acquire the necessary skills to expedite occupational mobility.

A related term is geographical labor mobility which refers to the level of flexibility and freedom that laborers have to physically move from one location to another to find gainful employment in their field.

Refers to the systemic overrepresentation or underrepresentation of particular demographic groups in an occupation or field of employment. Demographic groups include racial/ethnic groups, immigrants, individuals with prison records, and individuals with disabilities.

Racial occupational segregation is the degree to which members of different racial groups are distributed unequally across different types of jobs.  In the U.S., research shows that Black and Latinx workers are overrepresented in underpaying jobs, dangerous jobs, and jobs with fewer benefits.

Historically, occupational segregation is the result of policy and practice—by governments (federal and state), employer hiring, accessibility to education, and societal discrimination.

Related terms: Hiring bias, race-based earning disparities

Describes essential characteristics required for a given job, to include the level of education and training required; field of education and training required; and other requirements in terms of knowledge, skills, competence, occupational interests and work values.

See: Glossary – Skills Profiling/Skills Profile; Topic: Skills Profiling/Skills Profile 

Also known as job stress or work-related stress, occupational stress refers to the physical and emotional responses that occur when the requirements of a job do not match the capabilities, resources, or needs of an employee.  The causes of occupational stress include:

• Job demands — working long hours and shifts, high workloads, infrequent breaks, unnecessary routine tasks, ignoring workers’ skills, or feeling that the demands of their jobs exceed their capacity to cope
• Financial concerns — workers feeling they are not fairly compensated or  wages do not cover living expenses
• Frequent restructuring within an organization
• Risk of layoffs, job insecurity, or lack of advancement opportunities
• Poor work environment—poor lighting, lack of technology, uncomfortable office spaces, noise, lack of privacy, and toxic workplace culture
• Poor work-life balance—insufficient time for family, friends, hobbies, personal interests
• Low morale—workers often feel powerless when workers must respond to the demands and timelines of others with little control over events (too little authority, unfair labor practices, inadequate job descriptions)
• Management style—a workplace characterized by poor communication and workers not included in decision-making processes or not feeling supported by their coworkers and employers.
• Traumatic events—workers experiencing stressful situations and personal risk every day.

Stress can take a negative toll on workers’ health, leading to less productivity and job satisfaction. Over the short term, work stress causes strain; over the long term, it can negatively impact well-being.

OFCCP Compliance is the Internet Applicant final rule, issued by the Office of Federal Contract Compliance Programs. It addresses recordkeeping by Federal contractors and subcontractors about the Internet hiring process and the solicitation of race, gender and ethnicity of Internet applicants.

As defined by the Velocity Network Foundation, a credential before it has been accepted. An Offer is the communication to an individual that a credential is available to them. An Offer must occur before individuals can claim their credential, and individuals cannot claim a credential without first having a chance to view and actively accept an offer. In a Velocity-enabled ecosystem, individuals cannot be forced to claim a credential; individuals must have a choice, based on the offer presented, to receive that credential.

According to iCIMS (provider of talent acquisition software), offer management software eliminates manual, error-prone processes to get offer letters out to candidates faster. Branded templates and e-signatures are stored within the system for easily repeatable processes, while approvals are managed at scale during this critical stage of the recruitment lifecycle.

Offshoring refers to the relocation or assignment of business activities, tasks, or jobs from one country to another. This may involve moving entire positions or operational functions overseas; shifting specific tasks or components of work to foreign affiliates or contractors; or expanding employment in international locations in ways that substitute for, or reduce, domestic hiring. Offshoring can occur at the job level (visible relocation of positions) or at the task level, where discrete functions—such as software development, accounting services, customer support, or data processing—are performed abroad even if the core job remains domestically based.

International business expansion differs from offshoring. Companies may grow internationally by opening new markets, adding foreign operations, or increasing overseas employment—without reducing their domestic employment. In such cases, global growth complements rather than substitutes for domestic hiring.

Measurement note: Because modern work is increasingly organized around tasks rather than fixed job roles, offshoring is often difficult to measure directly. Policymakers typically rely on indirect indicators such as multinational employment trends, trade in services data, foreign affiliate activity, or industry-level employment patterns.

These terms should not be confused with:

• Outsourcing, which refers to contracting work to an external entity and may occur either domestically or internationally.
• Globalization or international expansion more broadly, which does not necessarily involve substituting foreign labor for domestic labor.

Generally refers to individuals who are aged 50 and above and still actively participating in the workforce. They may include individuals who are approaching retirement age, retirees who have chosen to re-enter the workforce, or those who have continued to work past the traditional retirement age for various reasons.

Dynamic shifts in the nation’s economy, shifting perceptions of retirement, increased workplace flexibility, aging of the “baby boom” generation, and an increasing number of centenarians are contributing to people working longer. By 2028, it is projected that workers aged 55 or older will represent more than 25% of the U.S. labor force, yet only 4% of firms have committed to programs that help integrate older workers into their talent pool.

In the rapidly changing learn-and-work ecosystem, postsecondary education is increasingly important for older job seekers’ reemployment. They often face challenges in accessing and completing education and training, sometimes due to their greater likelihood of having acquired age-related disabilities.

Alternate terms: 50+, 55+, experienced workers, returning workers (have retired and later return to the workforce), older jobseekers

See Topic: Older Workers in the Workforce | Learn & Work Ecosystem Library (learnworkecosystemlibrary.com)

According to iCIMS (provider of talent acquisition software), onboarding software streamlines the onboarding process by replacing handwritten forms with online documents, including Employment Eligibility Verification (E-Verify), paperless I-9, iForms and background checks. Onboarding software can save your company money by eliminating all shipping and storage fees associated with the handling of paper forms and by removing your HR staff from the data entry process.

Refers to models that make government services more user-friendly, coordinated, and equitable. Many states have established “one door” models for public supports (also known as “no wrong door” or “single point of entry” systems). These models streamline access to various government services and programs via a centralized entry point. The approach simplifies the process for individuals and families seeking assistance, particularly those who may face barriers due to complex administrative systems, limited resources, or lack of knowledge about available services.

Key Features:

1. Single Entry Point: Individuals enter the system through one location (whether physical office, website, or phone line) where they can access multiple services, reducing the need for them to apply to each program separately.
2. Comprehensive Assessment: Holistic assessment is usually conducted to determine eligibility for various supports—such as housing assistance, food benefits (SNAP), healthcare (Medicaid), unemployment, or childcare—without requiring multiple forms and processes.
3. Cross-Agency Collaboration: Agencies and departments work together to integrate data systems and service delivery, ensuring individuals can seamlessly connect to all the programs they qualify for, regardless of which agency is administering the benefits.
4. Case Management and Follow-Up: Often includes case management services to ensure individuals not only receive benefits but also get support in navigating the system. This can  include follow-up visits or calls to ensure services effectively meet client needs.
5. Technology Integration: Many systems use integrated technology platforms that allow different agencies to share data, making the process smoother for users and reducing redundancy in paperwork and applications.

Benefits:

• Simplification: Reduces the complexity of accessing public services by offering a streamlined process.
• Efficiency: Clients can receive multiple services more quickly without having to visit multiple offices or fill out numerous forms.
• Increased Accessibility: Vulnerable populations, including low-income individuals, immigrants, and people with disabilities, benefit from easier access to a variety of programs.
• Better Outcomes: Often leads to improved outcomes as individuals are more likely to receive the full range of supports they need, from financial assistance to healthcare and employment services.
• Reduced Bureaucracy: For agencies, this approach can reduce administrative costs and duplication of effort.

 Examples:

1. Colorado PEAK (Program Eligibility and Application Kit): Colorado offers an online “one door” system where residents can apply for food, cash, and medical benefits, as well as childcare assistance, through a single platform.
2. Michigan Bridges: Portal where residents can access multiple public assistance programs (SNAP, Medicaid, childcare, etc.) through one streamlined application and manage their benefits.
3. New York ACCESS HRA (Human Resources Administration): Platform allows residents to apply for food assistance, Medicaid, and other supports in one place.
4. Massachusetts “One-Stop Career Centers”: Provide access to various public support services, including unemployment insurance, job training, and related services.
5. California’s Health and Human Services No Wrong Door Program: Single entry point for health and social services to improve access to long-term services for older adults and people with disabilities.
6. Utah’s “one door” model for combined workforce and human services: Services are integrated under the Utah Department of Workforce Services (DWS), which includes programs such as health insurance, housing, refugee support and vocational rehabilitation. Utah emphasizes public benefits as part of a work-first approach where applicants work with a caseworker to navigate more than 50 federal and state-funded programs. The state operates locations in each county as part of single-state local area designation under the Workforce Innovation Opportunity Act. The model enables a unified statewide structure for service delivery which has created efficiencies in funding and improved customer service.

Online encyclopedias are digital, web-based reference platforms that organize and provide access to structured knowledge across a broad range of subjects. They evolved from traditional print encyclopedias into continuously updated, searchable, and hyperlink-rich knowledge systems. Online encyclopedias may be collaboratively edited, professionally curated, algorithmically structured, or institutionally managed. They serve as foundational knowledge infrastructures for education, research, artificial intelligence training datasets, and public information access.

Online encyclopedias represent one of the earliest large-scale digital knowledge aggregation models. They illustrate key dynamics in today’s information ecosystem, including authority versus openness, editorial governance, misinformation risk, citation practices, knowledge gaps, and the sustainability of public digital knowledge goods. They also increasingly feed downstream systems such as search engines, knowledge graphs, and AI-driven information services.

Increasingly, AI-powered conversational agents such as ChatGPT, Claude, Gemini, and other large language model–based systems serve as new interfaces to encyclopedic and reference knowledge. Rather than functioning as curated knowledge repositories themselves, these systems synthesize information drawn from multiple underlying knowledge sources, including online encyclopedias, academic databases, and structured knowledge graphs. Their emergence raises new questions about knowledge provenance, citation transparency, and trust in digitally mediated information environments.

Examples

The following examples illustrate the major online encyclopedias, ranging from expert-curated scholarly references to open community-edited platforms and machine-structured knowledge systems. Reliability and editorial governance vary across models. Reliability assessments are based on publicly documented editorial practices, contributor credentialing policies, citation requirements, and governance structures. They serve as qualitative indicators of typical content stability and verification practices rather than absolute judgments of accuracy.

Encyclopedia Britannica Online (Britannica.com)

• Launch: 1994
• Access Model: Subscription-based (limited free content)
• Focus: Comprehensive general knowledge across disciplines
• Strengths: Expert-authored; professionally edited; stable citations
• Limitations: Paywall; slower update cycle
• Reliability: Very high

Wikipedia

• Launch: 2001
• Access Model: Open Access
• Focus: Global general knowledge repository
• Strengths: Massive scale; rapid updating; multilingual; transparent revision histories
• Limitations: Variable article quality; uneven citation practices
• Reliability: Mixed but strong on well-monitored topics

Stanford Encyclopedia of Philosophy

• Launch: 1995
• Access Model: Open Access
• Focus: Philosophy and intellectual traditions
• Strengths: Scholarly peer-reviewed entries; continuously updated
• Limitations: Discipline-specific
• Reliability: Extremely high

Internet Encyclopedia of Philosophy

• Launch: 1995
• Access Model: Open Access
• Focus: Philosophy and intellectual history
• Strengths: Academic editorial oversight; accessible writing
• Limitations: Narrow disciplinary scope
• Reliability: High

Scholarpedia

• Launch: 2006
• Access Model: Open Access
• Focus: Expert-written scientific encyclopedia
• Strengths: Articles authored and curated by recognized experts
• Limitations: Limited disciplinary coverage
• Reliability: Very high

Wolfram MathWorld

• Launch: 1998
• Access Model: Open Access
• Focus: Mathematics reference
• Strengths: Deep technical coverage; professionally curated
• Limitations: Single-discipline scope
• Reliability: Very high

Oxford Reference

• Launch: 2002
• Access Model: Subscription-based
• Focus: Dictionaries and academic reference works
• Strengths: Trusted academic publisher
• Limitations: Paywall
• Reliability: Very high

Britannica ProCon

• Launch: 2004 (acquired by Britannica in 2018)
• Access Model: Open Access
• Focus: Structured presentations of controversial public issues
• Strengths: Balanced pro/con framing; editorial oversight
• Limitations: Narrow topical scope
• Reliability: Moderate to high

Encyclopedia.com

• Launch: 1999
• Access Model: Open Access
• Focus: Aggregated reference content
• Strengths: Free general reference access
• Limitations: Mixed source transparency; older editions common
• Reliability: Moderate

Citizendium

• Launch: 2007
• Access Model: Open Access
• Focus: Expert-moderated general encyclopedia
• Strengths: Real-name contributors; credentialed editors
• Limitations: Limited scale
• Reliability: Moderate to high

Fandom (formerly Wikia)

• Launch: 2004
• Access Model: Open Access / Ad-supported
• Focus: Entertainment and gaming encyclopedias
• Strengths: Deep niche coverage
• Limitations: Fan-edited; commercial influence
• Reliability: Low to moderate

Wikidata

• Launch: 2012
• Access Model: Open Access
• Focus: Structured machine-readable knowledge
• Strengths: Supports knowledge graphs and linked open data
• Limitations: Technical complexity; incomplete coverage
• Reliability: Moderate to high

Google Knowledge Graph

• Launch: 2012
• Access Model: Commercial / Proprietary
• Focus: Entity-based knowledge infrastructure powering search
• Strengths: Integrates multiple reference sources
• Limitations: Opaque sourcing and correction mechanisms
• Reliability: Variable

Internet Archive (Wayback Machine)

• Launch: 1996
• Access Model: Open Access
• Focus: Preservation of digital knowledge artifacts
• Strengths: Long-term web archiving
• Limitations: Archival rather than curated reference
• Reliability: High for preservation

Other Digital Knowledge Sources

Online encyclopedias exist within a broader ecosystem of digital knowledge sources that collectively shape how information is produced, curated, and accessed in the modern knowledge environment. Key related source types include:

• Academic Research Databases – Platforms such as JSTOR, ProQuest, and EBSCOhost provide access to peer-reviewed journal articles, dissertations, reports, and primary scholarly literature. They serve as authoritative research repositories rather than synthesized reference sources.
• Subject-Specific Community Wikis – Specialized wiki platforms such as Fanlore (fan studies), MicroWiki (retro computing), and other domain-focused wikis extend the participatory encyclopedia model into niche knowledge communities.
• Primary Expert Knowledge Sources – Books, government agency websites, policy reports, and research journals represent original knowledge production. Encyclopedias and AI knowledge systems frequently draw upon these sources for verified content.
• Professional and Archival Media Sources – Specialized magazines, documentary archives, investigative journalism collections, and news databases provide curated and time-sensitive knowledge dissemination, complementing reference and academic sources.

See Topic Brief: Online Encyclopedias & Emerging Knowledge Interfaces | Learn & Work Ecosystem Library

Refers to companies that help higher education institutions set up virtual programs in exchange for a large slice of the tuition revenue. A more specific definition: For-profit, third-party companies that enter into a contract with an institution of higher education to provide bundled products and services to develop, deliver, or provide managed programs, when the services provided include recruitment and marketing.

OPMs have increased over the last decade. Through contracts with higher education institutions, OPMs are often in charge of recruiting students into online programs and paid only if those students enroll. This factor has led to situations in which OPMs pressure students into signing up, sometimes using deceitful practices (some represent themselves as institution, not company officials). Students, therefore, are unaware that an outside party is recruiting them into what could be a potentially poor-quality program.

In response to growing concern about OPMs, beginning in the fall of 2023, the U.S. Department of Education expanded its interpretation of federal regulations to place OPMs under closer scrutiny. Under this guidance, many OPMs are considered “third-party servicers,” subjecting them to a new set of rules.

Some states are also considering barring such contracts through legislation. In May 2024, Minnesota became the first state to ban its public colleges and universities from tuition-share contracting with OPMs, effective January 2025. Many lessons learned from Minnesota’s policy work can inform other states wishing to pursue legislation that restricts tuition-sharing arrangements:

• Faculty unions which represent educators at the state’s public institutions must be engaged in these considerations.
• Barring OPM tuition-sharing raises related concerns such as barring OPMs from the rights to faculty members’ intellectual property and from having governance power at institutions.
• Legislation must address the role of institution governing boards in the review of OPM contracts.
• Legislative language must consider implications that could apply to other vendors/contractors that institutions work with, such as ed tech companies that offer online platforms where students can access course materials, submit assignments, and receive grades.

Refers to the relocation or expansion of business activities, jobs, or production processes within a company’s home country rather than abroad. It may involve bringing work that was previously performed overseas back to domestic locations, establishing new domestic production capacity instead of foreign operations, or shifting tasks and services from international suppliers to domestic providers.

Onshoring can occur at multiple levels of economic activity. Firms may return entire production facilities or supply chains to domestic locations, or they may relocate specific tasks—such as manufacturing components, software development, customer service, or logistics operations—previously performed in foreign markets. In some cases, onshoring reflects “reshoring,” where companies bring back activities that had earlier been offshored; in other cases, it represents new domestic investment rather than the reversal of earlier offshoring decisions.

Several factors have contributed to renewed interest in onshoring in the 2020s, including supply chain disruptions, geopolitical risks, rising international transportation costs, national industrial policies, and advances in automation that reduce labor cost differentials between countries. Governments have also encouraged onshoring through incentives designed to strengthen domestic manufacturing capacity, critical technologies, and workforce development.

Onshoring has implications for labor markets, regional economic development, and workforce training systems, particularly when new domestic industries require specialized technical skills or new apprenticeship and credential pathways.

These terms should not be confused with:

• Offshoring: relocating work or production from a company’s home country to another country.
• Outsourcing: contracting work to an external organization, which may occur domestically or internationally.
• Reshoring: a subset of onshoring referring specifically to bringing previously offshored work back to the home country.

Opacity (in artificial intelligence), often referred to as opaque AI, describes the degree to which an AI system’s internal processes and decision-making logic are difficult or impossible for humans to understand, interpret, or explain. An AI system is considered opaque when it produces outputs or recommendations without providing a clear, human-readable account of how those outcomes were generated. Opacity does not imply secrecy, error, or malicious intent. Rather, it reflects a structural limitation in interpretability, where the internal workings of the model are not easily transparent to users, decision-makers, or affected individuals.

AI opacity commonly arises in machine-learning and deep-learning systems that rely on large volumes of data, complex statistical relationships, and probabilistic modeling rather than explicit, rule-based instructions. In such systems, even developers may be unable to trace a specific output back to a sequence of logical steps.

In learning, credentialing, and workforce contexts, AI opacity raises important concerns related to trust, accountability, fairness, and governance, particularly when AI systems influence high-stakes decisions such as college admissions, employer hiring, assessment of learning, or credential recognition.

See Glossary Term:  AI Literacy vs. Adversarial Literacy | Learn & Work Ecosystem Library

See Glossary Term: Information Literacy | Learn & Work Ecosystem Library

See Topic Brief: Converging Terms:  Digital Literacy & Information Literacy | Learn & Work Ecosystem Library

Refers to an evaluation process that assesses individuals’ skills, knowledge, and/or performance using openly available modes that allow for scrutiny, feedback, and participation from various sources. The term is often used in educational contexts in which the assessments are designed to be fair, equitable, and conducive to learning. Open assessment methods may include open-book exams, peer assessments, self-assessments, project-based assessments, portfolios, and simulators (e.g., in aeronautical fields, the military, and health education) to assess skills, knowledge, and/or performance in occupational contexts.

Alternative terms: transparent assessment, inclusive assessment, participatory assessment, collaborative assessment

Refer to a digital credentials data standard that recognizes and verifies learning and achievements. Open Badges contain metadata that describe an achievement, the individual who achieved it, and the issuer of the credential.

Technical infrastructure supporting badge issuers and display sites to ensure interoperability across open badge systems, established by Mozilla Foundation in 2012 and currently maintained by 1EdTech Consortium (formerly IMS Global)

The standard describes a method for packaging information about accomplishments, embedding it into portable image files as digital badges, and includes resources for web-based validation and verification.

See: Open Badges | Learn & Work Ecosystem Library (learnworkecosystemlibrary.com)

See: 1EdTech Open Badges Standard | Learn & Work Ecosystem Library (learnworkecosystemlibrary.com)

Open Data is data that can be freely used and distributed by anyone to use, reuse, and redistribute. It requires data to be available, which means the data must be in the public domain or under license conditions that allow users to use the data without restriction.

Linked Data are datasets that make use of clear, unique identifiers that allow elements and relationships in different datasets to be identified as referring to the same thing. Linked data systems are built on a standard of World Wide Web (Web or Internet) technologies which enable linked data to become a global database. Linked Data enables links between datasets that are understandable to both humans and machines.

Open Data is not the same as Linked Data. Linked Data need not be Open Data, and Open Data need not be Linked Data.  Open Data is available to everyone without links to other data. At the same time, data can be linked without being freely available for reuse and distribution. Linked Data is enabled through a set of design principles for sharing machine-readable interlinked data on the Web. Linked Data enables the creation of a global network of data. The resulting network can automatically answer complex queries and analytics by searching the network of information and finding matches (known as Data Graph Traversal).

Linked Open Data is a blend of Linked Data and Open Data: it is both linked and uses open sources. The benefits of Linked Open Data: it breaks down information silos between various formats (often disparate sources and formats), facilitates the extension of data models, and allows for easy updates. The resulting data integration enables easier and more efficient searching through complex databases for information.

Main principles of the Linked Open Data:

• Uniform Resource Identifiers (URIs) are used to name and identify individual things. The URI is a single global identification system used to give unique names to anything (e.g., resources on a webpage; mail address; phone number; books; objects such as people and places; and concepts).
• All conceptual things have a name starting with HTTP (Hypertext Transfer Protocol)—an application (software) protocol in the Internet’s model of distributed, collaborative, hypermedia information systems. HTTP is the foundation of data communication for the Web, where hypertext documents include hyperlinks to other resources that users can easily access.
• Looking up an HTTP name returns useful data about the thing in question in a standard format. Anything else that that same thing has a relationship with through its data also has a name beginning with HTTP.
• When publishing data on the Web, other things are referred to using their HTTP URI-based names.
• URIs provide a means of locating and retrieving information resources on a network (either on the Internet or private network, such as a computer.

Refers to reusable agreements that make it easier for people and organizations to publish, access, share, and use better quality data. The standards are a set of specifications or requirements for how some sets of data should be made publicly available. The standards often incorporate specifications around cost (free/low cost), consistency, portability, open governance, interoperability, and structure. The data is typically made open on the web for the public to use. Examples: government data like anonymized census information, building permits, public facilities, real-time transit, road construction, service requests, zoning.

OER are publically accessible teaching, learning, and research resources created and licensed to be free for end users to own, share, and in some cases modify.  Modify could include re-mixing, improving, and redistributing under some licenses.

UNESCO’s 2019 definition: “Learning, teaching and research materials in any format and medium that reside in the public domain or are under copyright that have been released under an open license, that permit no-cost access, re-use, re-purpose, adaptation and redistribution by others. Stakeholders in the formal, non-formal and informal sectors (where appropriate) include: teachers, educators, learners, governmental bodies, parents, educational providers and institutions, education support personnel, teacher trainers, educational policy makers, cultural institutions (such as libraries, archives and museums) and their users, information and communications technology (ICT) infrastructure providers, researchers, research institutions, civil society organizations (including professional and student associations), publishers, the public and private sectors, intergovernmental organizations, copyright holders and authors, media and broadcasting groups and funding bodies.”

Refers to goals to share research findings broadly and encourage transparency in education research. Transparency can be supported by:

• All key personnel for federally funded grant projects obtaining a PID (Persistent Identifiers) such as ORCID iD (Open Researcher and Contributor Identification prior to a grant award. (Open science policies must be aligned with policies for federal funding agencies that protect national security.)
• Where appropriate, incorporate open science practices including pre-registration of projects, planning and budgeting to support curation of data and analysis codes to facilitate ease of data sharing, and budgeting for publication of findings in open access journals.
• For some federal grant programs, including a plan in grant proposal that describes how project data will be managed and how final research data set will be shared.

According to 1EdTech, refers to 5 principles for published specifications that are: 1) freely available to all with no implementation restrictions; 2) licensed for free with no restrictions on software implementations; 3) created, endorsed, governed, owned, and maintained by a substantial representation from across the community asked to abide by the standards;  4) governed via transparent neutral processes that provide for equitable participation by members of the community (not controlled by a limited set of market participants); and 5) transparent with respect to intellectual property claims that may hinder market adoption. These principles are useful to edtech suppliers in reducing the cost of many integrations they are required to support, because standards have a cost of implementation to suppliers even if distributed and licensed for free. Many leading suppliers choose to implement open standards because the cost is often returned to them many times over in savings in dealing with their partners.

Open universities are higher education institutions that offer degrees with low or no entry requirements. They are designed to offer equal opportunities to develop individuals’ abilities, improve their level of education, and retrain for a new career.  They are particularly useful for:

• Former students who have graduated from prior studies with low grades but seeking to continue their higher education.
• Learners who do not have a credential from secondary school.
• Learners unable to travel to a physical location to study. Open universities typically offer distance learning studies—courses taught fully online or with materials sent to the learner’s preferred location.
• Disabled learners seeking educational services.

The first European university to give students the option of open and online education was the Open University UK created in 1970. Many countries have since embraced this trend. Examples of open universities from which millions of students have graduated include:

• The Open University UK 
• Open University of the Netherlands
• Open University of Cyprus
• Hellenic Open University

Online learning universities are higher education institutions that have more admission requirements than open universities but are still flexible about learners’ background. Examples of online learning universities that offer degree diplomas:

• Royal Roads University
• Nottingham Trent University Online
• Walden University
• Kettering University
• The University of Law

Both open and online learning universities offer online degrees, but they have different degrees of openness to learners. Open universities generally accept any student, with no age limits and no prior education requirements. At some open universities, students cannot complete a degree but the credits they earn can be transferred towards an academic degree. Online learning universities have entry requirements similar to those of traditional universities and they guarantee that learners will earn an academic degree if they fulfil the course requirements.

The detailed specifications in a credential management system shows how a technical system meets the needs of users and stakeholders. These typically describe how the system should work, what features and functions it should provide, and how it should integrate with other systems and tools.

A higher education institution recognized under the Carnegie Classifications for Student Access and Earnings, developed in collaboration with the American Council on Education (ACE). Institutions earning this designation must meet two core criteria:

• High Student Access – Demonstrated success in enrolling and supporting students from underrepresented, low-income, first-generation, or historically marginalized backgrounds.
• High Graduate Earnings – Evidence that graduates achieve strong post-graduation income, indicative of economic mobility and return on investment.

Institutions earning the OCU label are thus designated for balancing equitable access with meaningful economic outcomes for students.

Examples of U.S. Institutions with the OCU designation:

• Lamar University (Beaumont, Texas)
• University of Texas System Institutions – Five UT System campuses are designated OCUs: UT Arlington; UT El Paso; UT Tyler; UT Rio Grande Valley (UT RGV); UT San Antonio
• Germanna Community College (Virginia)
• Morehouse College (Atlanta, GA)
• St. Thomas University (Miami Gardens, FL)
• Albion College (Albion, MI)
• Ball State University (Muncie, IN)
• Ferris State University (Big Rapids, MI)
• Western Governors University (Salt Lake City, UT)
• New Mexico Military Institute (Roswell, NM)
• Mercy College of Health Sciences (Des Moines, IA)
• Otis College of Art and Design (Los Angeles, CA)

Opportunity Colleges and Universities (OCUs) are U.S. postsecondary institutions identified through the Carnegie Student Access and Earnings Classification as delivering both broad access to students and strong economic outcomes for graduates. These institutions enroll significant numbers of students from underrepresented and low-income backgrounds and produce graduates who achieve competitive earnings—indicating meaningful upward economic mobility.

Introduced in 2025, the Student Access and Earnings Classification is among the most recent updates to the Carnegie Classifications and reflects a growing emphasis on student outcomes and economic mobility. It was launched alongside the Student Learning Environment Classification as part of a broader modernization of how higher education institutions are described and compared. Rather than focusing primarily on inputs (such as selectivity or resources), it evaluates who institutions serve and how their students fare after completion. OCUs are institutions that perform strongly on:

• Access: Enrolling and supporting students from historically underserved populations, including low-income, first-generation, and racially/ethnically underrepresented groups.
• Outcomes: Producing graduates with earnings that meet or exceed expectations relative to peers and regional labor markets.

As of 2025, 479 institutions have been designated as OCUs, collectively enrolling approximately 2.75 million undergraduate students across the U.S.

OCUs typically demonstrate:

• High Student Access – Commitment to enrolling and supporting students from a wide range of backgrounds, particularly those historically excluded from higher education.
• Strong Graduate Earnings – Evidence that students achieve competitive post-graduation earnings, signaling return on investment and long-term economic mobility.
• Diverse Institutional Types – OCUs span the full spectrum of U.S. higher education, including community colleges, Historically Black Colleges and Universities (HBCUs), tribal colleges and universities, regional public universities, and flagship and research-intensive institutions.
• Regional Anchor – Many OCUs serve as anchors in their regions, contributing to local workforce development and economic vitality by preparing graduates for in-demand careers.

Institutions do not apply for the OCU designation; it is assigned based on national data on student access and post-graduation earnings. The Carnegie Classifications are expected to be updated on a regular cycle (approximately every three years), meaning an institution’s designation may change over time as outcomes evolve.

An approach to workforce development founded in the recognition there are many ways to prepare individuals for social roles, jobs, and economic opportunities, not only the traditional path of K-12 education to college to job. An important component of opportunity pluralism is that opportunity comes from (1) useful knowledge and (2) social capital (strong personal networks). Programs focused on opportunity pluralism typically have four features:

• Academic curriculum linked with labor market needs, which leads to a credential and family-sustaining income
• Career exposure and work, including mentor-advisers
• Written civic compact between employers, trade associations, and community partners
• Supportive local, state, and federal policies that make these programs possible.

Program example:  STARS—Skilled Through Alternative Routes – Opportunity@Work

Opportunity populations refer to people in America who have had limited access to educational and professional opportunities and who face barriers to employment and career advancement. They may include: opportunity youth: young adults age 17–24 who are out of school or out of work; members of the LGBTQ community; members of the immigrant or refugee populations; formerly incarcerated individuals; members of Indigenous communities; people with disabilities (physical and/or cognitive); people without a high school diploma; people with limited English proficiency; people who are (or who have been) homeless. Not all members of these groups experience barriers to employment; individual circumstances including family background, race, geography, and other factors play a significant role in one’s access to opportunity.

Refers to young people between the ages of 16 – 24 who are not enrolled in school or participating in the labor market. There are 5-6  million, or one in nine members of this age group in the U.S. Despite many young people’s aspirations to advance and secure family wage jobs, make connections in civic engagement, and improve their communities, once they have experienced disconnection from school and work, research indicates it is unlikely they will be able to meet these aspirations, as only 1% of youth who have been disconnected will ever earn an associate’s degree or higher, compared to 36% of the general population.

Refers to young people, typically ages 16–24, who are neither enrolled in school nor participating in the workforce. The term emphasizes potential rather than deficit, framing these young people not as “dropouts” or “disconnected,” but as individuals with untapped talent who require pathways back into education, training, employment, and civic participation.

The term began gaining widespread use in the late 2000s and early 2010s. It emerged as a strengths-based alternative to earlier labels such as “disconnected youth,” “idle youth,” “status offenders,” or “at-risk youth.” While these earlier terms focused on deficit or risk, opportunity youth reflect a policy and practice shift toward reconnection, systems alignment, and long-term economic mobility.

Recent estimates suggest that approximately 5–6 million young people in the United States fall into this category at any given time. This population spans race, geography, and socioeconomic background, though rates of disconnection are often higher among young people from low-income communities, youth aging out of foster care, young parents, justice-involved youth, and those without access to stable transportation, childcare, or digital connectivity.

The condition of disconnection reflects absence from two primary launching institutions of adulthood: Education systems (secondary, postsecondary, or credentialing programs), and Employment systems (formal labor market participation). Because of this dual absence, opportunity youth sit at the intersection of the education-to-work pipeline, workforce development policy, and social services systems.

Efforts to reconnect opportunity youth often involve:

• Career and technical education (CTE) pathways
• Apprenticeships and work-based learning
• Short-term credential programs
• Wraparound support services (mentoring, childcare, transportation, mental health support)
• Reengagement centers and alternative education models
• Youth employment and summer job initiatives

Federal and state policies tied to workforce development (e.g., under the Workforce Innovation and Opportunity Act), education funding, and community-based partnerships frequently target this population.

A recent report by American Enterprise Institute titled “Reconnecting Opportunity Youth to Work and a Future,” argues that the scale and persistence of youth disconnection require more coordinated, systemic solutions rather than fragmented programmatic responses.

A related term is opportunity population, a broader strategic framing term  often used in workforce and  policy discussions to describe multiple groups with underutilized potential due to systemic barriers to education or employment.  This population may include multiple groups facing structural barriers to education or employment, such as:

• Opportunity youth
• Justice-involved individuals
• Veterans transitioning to civilian work
• Individuals with disabilities
• Long-term unemployed adults
• Displaced workers
• Returning caregivers
• Immigrants or refugees (depending on context)

The exact definition varies by organization; it is generally less standardized than “opportunity youth.”

The difference between opportunity youth and opportunity population is that opportunity youth refer to:

• A specific demographic subgroup.
• Typically refers to individuals ages 16–24.
• Defined by a status condition: not enrolled in education and not participating in the workforce.
• Widely used in youth policy, workforce development, and education reform.
• Has a fairly standardized meaning across research and policy communities.

Refers to interest-free loans to students to cover tuition and sometimes living expenses. Learners are required to pay back the loans only if they complete their program and hit a certain income threshold. These loans are more commonly used for short-term training programs that are offered by higher education institutions or alternative providers that are not eligible for federal financial aid.

An example is the State of New Jersey’s fund established to offer outcomes-based loans to learners in certificate and other non-degree programs in high-demand fields. The program is called a “Pay it Forward Fund,” which is a reference to the fact that graduates’ loan payments are recycled back into the fund and used to support the next round of learners.

The nonprofit Social Finance has used this form of lending as part of its mix of investments since launching in 2011. The organization’s investment approach is dependent on improving measurable outcomes in education, economic mobility, health, and housing.

See Interest-free Loans

Refers to structured sequences of learning, training, and work experiences that are organized around clearly defined and measurable knowledge, skill, competency, or performance outcomes rather than time-based participation or course completion. Progression through the pathway occurs when learners demonstrate mastery of targeted outcomes that are verified through assessment, workplace performance, or credential attainment.

Outcomes-based pathways occur in several settings:

• In K–12 education, outcomes-based pathways support mastery-based progression through the curriculum, standards-aligned expectations for high school graduation, and career-connected learning sequences.
• In higher education, they underpin competency-based programs, microcredential and certificate stacks, credit for prior learning, and guided pathways aligned to workforce outcomes.
• In employer and workforce training, they structure onboarding, upskilling, reskilling, and career advancement programs tied to job-role performance expectations.

A closely related term is Competency-Based Education, an instructional and credentialing model that frequently operates within broader outcomes-based training pathways.

Other related terms: Skills-Based Hiring; Microcredentials; Work-Based Learning; Apprenticeships; Career Pathways; Learning & Employment Records (LERs)

See also: Outcomes-Based Training Pathways in the Learn-and-Work Ecosystem | Learn & Work Ecosystem Library

Refers to services that are purchased from external providers, i.e., “what you pay someone else to do.” Examples include externally provided help desk, data center, or services provided by multicampus system or district offices; food services; cleaning services; a range of one-time project costs and professional services.

P20W refers to pre-kindergarten through college and into the workforce.

P20W data systems refer to the various state-level educational databases that collect student data across pre-kindergarten through college and into the workforce to help education leaders make policy decisions, the best use of resources, and support individuals throughout their life stages.

State Longitudinal Data Systems (SLDS) connect statewide information from early childhood through K–12 education, postsecondary education, and the workforce. These state-level data infrastructures in the U.S. securely bring together cross-agency data that enable leaders, practitioners, and community members to better understand the progress, predictors, and performance of learners throughout their educational and employment pathways. According to the Data Quality Campaign, SLDS:

• Reside in different places depending on the state context, but best practice is that the SLDS itself is not owned by any one contributing agency alone.
• Are Longitudinal (capture data from the same population over multiple years); Individual Level (include data that is specific to individual people but may contain identifiable information or be anonymous); and Statewide (bring together and connect data or records from multiple state agencies).
• Are supported in large part by a number of federal grants awarded to states to develop or improve their data systems in order to effectively measure the success of educational programs. Longitudinal data is needed for effective measurement.
• Are designed to help school districts, schools, and teachers make informed, data-driven decisions to improve student learning.
• Leverage stakeholders and partners of education, training, and employment programs to create a system which provides data to support the research and evaluation of programs to improve the outcomes of individuals provided service.

Data developments are fraught with challenges. Most state P20W and SLDS are custom built. They require technology and personnel investments to develop their systems from scratch—this can take years to materialize. Maintaining data systems over time creates financial strain and risks long-term durability of such a system. Examples of problems endemic to these developments:

1. They are unable to realize the promise of connecting full ecosystem of data sets across agencies.
2. They often have performance constraints due to an inability to keep up with the latest innovations in technology and data management.
3. They may have significant security vulnerabilities.
4. Data retooling of systems is expensive, and time-consuming. States rely on continuing infusion of federal funding, raising issues around funding reliability over time.

Refers to the invisible barrier that often faces workers without a bachelor’s degree. Opportunity@Work notes that the paper ceiling holds back more than 70 million workers who are STARs (Skilled Through Alternative Routes) rather than through a bachelor’ s degree.  They include 50% of the workforce that has developed valuable skills through on-the-job experience, military service, community college training programs, and partial college completion.

Research has found that as many as 90% of large companies use some form of automated applicant tracking system (ATS) to screen resumes. This filters out about half of all applications. This research also revealed that more than 60% of employers rejected otherwise qualified candidates because they did not have a bachelor’s degree. [Fuller, J., Raman, M., et al. (Oct. 2017). Dismissed By Degrees. Published by Accenture, Grads of Life, Harvard Business School]

The paper ceiling can be ameliorated by workers and companies uniting to create a new and more equitable future of work in which skills matter more than what is on paper, where workers and companies work together to tear down the paper ceiling.

Related terms: Degree screening/screens, stereotypes, misconceptions, biased hiring algorithms

ParentPLUS loans are offered by the federal government to make it more accessible and affordable for eligible parents to seek a college-level education.  To receive a loan, a parent borrower must (1) have a child who has filled out the Free Application for Federal Student Aid(FAFSA®; (2) be the biological or adoptive parent (or in some cases, the stepparent) of a dependent undergraduate student enrolled at least half-time at an eligible school; (3) not have an adverse credit history; (4) meet general eligibility requirements for federal student aid. Grandparents (unless they have legally adopted the dependent student) and legal guardians are not eligible to receive parent PLUS loans, even if they have had primary responsibility for raising the student.

See Topic: ParentPLUS Loans

Refers to a branded framework developed by the nonprofit America Succeeds, designed to cultivate and assess “durable skills”—also known as soft skills—for learners and workforce professionals. Pathsmith™ organizes 100 leading skills into 10 core competencies, each with sub-skills, often visualized via the “Durable Skills Wheel.” Examples of core competencies are critical thinking, adaptability, leadership, and communication—viewed as essential for economic mobility in a dynamic world.

There is a free Starter Edition of the Framework that offers definitions of approximately 82 durable skills and provides summary rubrics across performance domains. A paid/licensed Full Framework version contains detailed definitions for each skill at four performance levels—emerging, developing, applying, and exceeding—though access requires contacting the organization and may involve licensing or tailored pricing

The name, Pathsmith, was selected to evokes the image of a “blacksmith shaping tools to withstand the test of time.” In that sense, Pathsmith™ forges resilient, human-centric skills that endure even as job roles and technologies change

See: Pathsmith™ (Durable Skills Advantage Framework) – America Succeeds | Learn & Work Ecosystem Library