09/01/2026
Cognition itself is on the chopping block.
MIT has just released a 40-page report on AI in teaching and learning, and its main concern is much larger than cheating.
Students at MIT are already using AI widely. According to the report, some see it as a source of help, efficiency, and creative inspiration. Others feel anxious, pressured, and uncertain about what is allowed.
What caught my attention is how AI appears to be changing the social experience of learning.
The committee heard reports of fewer students attending office hours, participating in discussions, or meeting in study groups. When students turn immediately to a chatbot, they may miss the conversations through which they learn to explain an idea, disagree with someone, accept criticism, and work through confusion.
The report also takes assessment seriously. If AI can produce an essay, solve a problem, write code, or construct a proof, the finished product can no longer tell us enough about what a student understands.
MIT recommends oral exams, portfolios, project demonstrations, conversations about submitted work, drafts, version histories, and regular checkpoints. These approaches give teachers more opportunities to see how the student’s thinking developed.
One concept that appears throughout the report is productive struggle.
Students need to experience some difficulty. They need time to get stuck, try an approach, make a mistake, and revise their thinking. The report warns about “cognitive surrender”: the habit of turning to AI at the first sign of difficulty and allowing it to take over the thinking.
AI can still have an important place in learning. It can provide feedback, help students practise, make learning more accessible, and allow them to undertake projects that were previously too complex. The report describes this as augmentation. The student remains intellectually involved and responsible for the work.
MIT also calls for clear course-level AI policies. Students should know when AI is allowed, limited, required, or prohibitedand why. A single university-wide rule will not work equally well for a poetry seminar, a mathematics course, an architecture studio, and a software-engineering project.
The report advises caution with AI detectors because of false accusations and the distrust they can create. It also asks instructors to disclose when they use AI to create teaching materials, provide feedback, or evaluate student work.
I think this may be the most important part of the report: education is also a social and human practice.
Students learn through relationships, shared work, mentorship, discussion, disagreement, and the slow development of confidence and judgement. A chatbot may provide an answer, but it cannot give students the full experience of becoming members of an intellectual community.
AI-aware education will require much more than adding a paragraph to the syllabus. We need to reconsider what students should learn, how they should learn it, and what evidence will genuinely show that learning has taken place.
Link in the first comment!
Reference
MIT Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training. (2026, August 13). Report of MIT’s Ad Hoc Committee on AI use in teaching, learning, and research training. Massachusetts Institute of Technology.
