01/09/2026
Brown University has released a 51-page report on generative AI in teaching and learning, and one tension runs through the entire document.
Students are using AI regularly. They are also deeply worried about what it may be doing to their learning.
The committee collected feedback from nearly 700 members of the Brown community, reviewed policies from peer institutions, examined course syllabi, and screened 148 scholarly and professional publications.
Among the students who responded, 56% of undergraduates and 67% of graduate and medical students reported using generative AI daily or weekly.
Many were using it to learn. They asked AI to explain difficult problems, assess their understanding, revise written work, summarize readings, and debug code.
Yet 88% of undergraduate respondents and 73% of graduate and medical student respondents expressed concern that AI could reduce their long-term learning. Similar numbers worried about its effects on their cognitive abilities.
Even among undergraduates who used AI daily, 78% were concerned about reduced learning.
I find this tension more useful than the familiar division between people who support AI and people who oppose it.
Students can recognize the value of a tool while remaining uneasy about how it changes their intellectual habits. They may appreciate an immediate explanation and still wonder whether they could explain the same concept later without assistance.
The report connects this problem directly to AI literacy.
Brown argues that AI literacy should include technical knowledge, critical evaluation, ethical reasoning, and an understanding of AI’s environmental, social, cultural, and political effects.
It also adds something I consider especially important: metacognitive awareness.
Students need to distinguish their own cognitive contribution from the contribution made by AI. They need to recognize which uses lead to durable learning and which allow them to avoid the intellectual work through which learning develops.
The report also raises concerns about the social experience of learning.
When students repeatedly ask a chatbot for explanations, they may begin replacing support from classmates and instructors. They lose conversations in which they question an idea, explain their reasoning, receive criticism, and work through uncertainty with another person.
Unclear or highly punitive AI policies can make this worse. According to the report, they may create secrecy, shame, and distrust. Brown recommends clear expectations accompanied by explanations. If AI is required, students should understand how it supports learning. If its use is limited, they should understand what knowledge or ability the restriction is protecting.
The committee also examined AI-detection tools.
Among the peer institutions it reviewed, none endorsed their use. The report points to false positives, false negatives, and bias against multilingual writers. These systems also cannot reliably distinguish between limited grammatical editing and having AI structure and draft an entire assignment.
Link in the first comment!
Reference
Brown University, Generative AI in Teaching and Learning Committee. (2026, July). Generative AI in teaching and learning (GAITL) committee final report and recommendations.
