Address student confusion about what counts as misconduct and provide supportive, educational nudges instead of punitive alerts.
Combine real coursework context (Canvas data) with micro‑lessons and actionable reminders to reduce risky behavior before submission.
Make integrity coaching practical, private, and accessible across common student personas (freshman, working students, international students, students with disabilities).
What it does
Integrates with Canvas via user-provided API token to surface assignments, deadlines, grades, and rubrics locally. (Currently parses the webpage to extract information)
Real-time nudges: copy/paste warnings, pre-submission self-check, honor-pledge prompts, and collaboration vs. individual hints.
Educational modules: micro-lessons, situational quizzes, and reflection prompts tied to course context.
Dashboards: integrity risk snapshot, progress metrics, private logs; links to campus resources and support.
Chatbot: Provides AI conversation with a vast knowledge base custom to the URLs mentioned and institute involved.
AI: Support for webllm, gemini and regex. Also provides various tools like planner and feedback generator to ease the process.
How we built it
Browser extension that stores the Canvas access token locally after validation; uses Canvas REST API scopes for assignments, courses, calendar, and enrollments.
Modular front-end components for dashboard, nudges, and educational modules (reusable UI + state management).
Lightweight background service to refresh Canvas data on demand and fire context-aware prompts.
Security-first choices: local token storage, explicit consent flows, minimal scopes by default, and clear privacy UI.
Selected libraries and patterns for reliability and testability (modular services, well-scoped API wrappers, robust error handling).
Challenges we ran into
Canvas API variability across institutions and changing LMS behavior after updates. Another method (webpage parsing via contentscripts) was adopted temporarily to extract data.
Balancing helpful nudges with non-intrusive UX, and ensuring accessibility (keyboard navigation, dyslexia-friendly fonts).
Handling token validation, refresh, and secure storage while keeping the flow simple for students.
Planning based on user capabilities, i.e., user customization.
Understanding huge webpages via AI for creating knowledge base. Sent separate chunks iteratively and combined them all in end to achieve final results.
Accomplishments we're proud of
Working Canvas token flow and reliable data pulls across common course setups.
A functional pre-submission self-check and embedded draft feedback that students can use instantly.
Clear privacy model (FERPA-aware design) and accessible UI features.
Scalable architecture that separates LMS adapters, UI, and rules so we can expand quickly.
What we learned
Privacy-first, opt-in design increases trust and adoption.
Small, contextual interventions are more effective than broad monitoring.
Modular code + clear API wrappers reduces friction when supporting other LMSes.
Real-world testing with actual student workflows uncovers edge cases faster than synthetic tests.
What's next for AIM
Expand LMS support beyond Canvas and add institution-specific policy templates.
Improve detection and guidance with explainable models (research-backed, low false-positive thresholds).
More educator-facing opt-ins and aggregated, anonymized analytics for institutional use.
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