Constructing Modern Knowledge was borne of conversations between Gary Stager and Seymour Papert about the need to create a setting where educational computing and progressive education could dance together. Papert, the father of educational computing, helped Piaget understand how children construct mathematical knowledge, was a pioneer in artificial intelligence and a founding member of the MIT Media Lab, was the co-inventor of the Logo programming language, and authored three of the most important books about learning and computing.
Fundamentally, Papert framed two competing views of education as instructionism vs. constructionism. Instructionism posits that learning is the result of having been taught. It focuses on things like, instruction, curriculum, testing, and the intervention of teachers. Constructionism is built on the Piagetian notion that knowledge is a consequence of experience and the idea that people learn by constructing knowledge through the process of making something shareable. Simply stated, the learner learns.
The same paradigm may be applied to how AI is envisioned for schools. Instructionists tout the use of AI for lesson plan writing, marking student work, teacher record keeping, surveillance, and the production of instructional materials, including worksheets, quizzes, flashcards, and tests. Of course, those who harbor antipathy towards schools or ignorance about the nature of learning view AI as the latest attempt at automating education, raising test scores, degrading teachers, reducing costs, and disinvesting in children. I first wrote about this dystopian fantasy in 1992. This is not the fault of software. It is the result of adults profiting at the expense of children.
“Everyone needs a prosthetic!”
– Seymour papert
Constructionists are unconcerned with using technology to sustain questionable instructional practices, even when they are made more efficient. We (constructionists) view AI as a prosthetic and computation as an intellectual laboratory or vehicle for self-expression. In a learner-centered context, computation can supercharge learning and make what was impossible months ago, possible. When you make easy simple things easy to do, you make complexity possible. Generative AI, even in its crude form available today, is a copy-editor, art department, idea generator, software developer, and collaborator that gives our ideas flight and raises the expectations for human potential.
One should question how or why anyone would seek to “protect” children from such unprecedented empowerment. The wise educators of Reggio Emilia remind us that it is irresponsible to build pens around children. It is the responsibility of adults to create constructive contexts in which to engage with their world.
Since the 1960s, Seymour Papert and constructionists have offered a profoundly aspirational and learner-centered vision of artificial intelligence in education. Those seeking a humane learner-centered vision of AI would be wise to read Chapter 7 of Seymour Papert’s seminal book, Mindstorms: Children, Computers, and Powerful Ideas. That chapter is titled, “LOGO’s Roots: Piaget and AI”
Here are two particularly relevant passages.
In a recent podcast, MIT Professor Hal Abelson recalls how much the MIT Artificial Intelligence Laboratory in the late 1960s and early 1970s was influenced by Jean Piaget. This early AI R&D was based on the idea that if you understood how children think and learn, you might be able to teach that to a computer, and if a child could teach a computer to think and learn, they would learn a whole lot more. Abelson remembers that the unofficial motto of the AI Lab was, “Computers are for children,” and suggests a vision of “computational making.”
Ken Kahn was a student in the MIT Artificial Intelligence Laboratory around the same time as Professor Abelson.
An enduring vision of learning & computing along with the insights of 4 dozen experts from across the globe
His new book, The Learner’s Apprentice – AI and the Amplification of Human Creativity, shares the lessons he learned over the past fifty years at the intersection of children, computing, and powerful ideas and shares dozens of project ideas for learning by collaborating with chatbots.
Constructing Modern Knowledge was cool long before AI was cool.
Five Ways in Which CMK has its Roots in AI… …and AI has Roots in Powerful Ideas
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The roots of CMK are in Logo, the programming language and approach to learning designed for children, primarily by Seymour Papert and CMK Senior Fellow, Cynthia Solomon. Soon after its initial creation, Logo was nurtured in the MIT Artificial Intelligence Laboratory of the late 1960s and 1970s. The programming environments powering CMK projects are descendent of Logo, including: Scratch, SNAP!, Turtle Art, TurtleStitch, MakeCode, microBlocks, and Beetle Blocks.
Learn about Logo and explore a growing library of resources here.
Marvin Minsky, considered by many to be the “father of artificial intelligence” led a fireside chat at the first ten Constructing Modern Knowledge institutes. We were indeed spoiled to spend time in the company of one of the great minds of the past century and are grateful for Marvin’s generosity and willingness to engage educators in discussion.
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Dr. Stephen Wolfram, one of the world’s most significant scientists and creator of the tools powering the AI revolution was a guest speaker at Constructing Modern Knowledge twice! At The Language of Computation, his insanely powerful Wolfram Notebook Assistant + LLM Kit is now one of the tools available tor creating computational projects that were otherwise impossible just a few months ago.
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CMK Senior Fellow, Dr. Cynthia Solomon, assembled a fantastic volume of essays by AI pioneer, Marvin Minsky. Constructing Modern Knowledge Founder, Gary Stager, was honored to contribute one of the expert response essays in the book, Inventive Minds – Marvin Minsky on Education.
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Since its inception, projects at Constructing Modern Knowledge have included attempts to build “thinking” machines with sensors, feedback, and code.
Participants have built shoes that when clicked together call an Uber, stair climbing autonomous vacuum cleaners, and smart helmets…
Here’s a video of a robot fortune teller project. Who knows what will be possible with AI as our learner’s apprentice?
Educators, or anyone else interested in understanding what artificial intelligence may contribute to learning, education, and society, would be well-advised to read the books recommended in the document below. (click on the image)
