Brown says AI make student brain no work good, teacher should help use it better

Students and faculty at Brown University are worried generative AI could harm learning after an economics professor's take-home exam produced results he said pointed to widespread misuse of the technology. In a report [PDF] published by Brown's Generative AI in Teaching and Learning (GAITL) committee, teaching staff say they fear AI could weaken students' cognitive skills and encourage cheating. The committee's report comes days after Brown's Roberto Serrano warned that society "cannot choose to become idiots" - comments made after he all but proved his economics class was cheating on their midterms. Serrano allowed his students to complete their spring midterms outside class, while keeping it closed-book. After the December 2025 shootings at the university, which killed two and injured nine, the professor gave the nod to a home-based exam - the first in his almost 20 years at the university, he told Inside Higher Ed. For students, this meant effectively unlimited time to complete the test, and for Serrano, an opportunity to ramp up the difficulty. He said scores for these exams typically sit in the 65-80 percent range, but students on this test scored an average of 96 percent. Suspecting AI-assisted cheating had played a role, Serrano did not immediately call for a redo, in the hope that he simply had an especially talented cohort, but promised to void the results if students' scores on the final, which was taken under controlled conditions, did not meet similar standards. The average final score was 48.6 percent, a record low. Three students scored zero. Serrano told students that because of the discrepancy in scores, he was increasing the weighting of the final exam to 80 percent from 50 percent, and voiding the midterm results. "We cannot afford to have a society in which a significant fraction of our best young minds think that cheating is OK," he said. "That leads to a declining society, to a failed society... We cannot choose to become idiots." Brown's GAITL committee found that 56 percent of undergrads use AI tools daily or weekly, rising to 67 percent for graduate and medical students, and 85 percent for master's students. Students most often used AI for tasks that help build advanced cognition, such as explaining solutions to complex problems and debugging code. The committee also cited previous research on the topic in both US and UK higher education, finding that around 25 percent of students were submitting assignments completed with the help of AI tools, and the rate is increasing sharply each year. Students appear willing to adopt AI tools, despite many fearing the technology is making them dimmer. Eighty-eight percent of Brown students and 73 percent of graduate/medical students said they were concerned AI could have negative effects on their cognitive capacities. The same concerns were shared by teaching staff, 95 percent of whom feared for students' long-term learning, while four in five expected cognitive capabilities to decline. Pointing to wider research on AI tools' impact on student cognition, the GAITL committee noted a strong body of evidence to support the view that over-reliance could decrease higher-order thinking and metacognition. The research on the matter is still relatively sparse, but it supports the possibility of AI supplementing student cognition, provided it is used with specific guidance from teachers. Brown has since committed to enacting a range of the committee's recommendations, which cover near, medium, and long-term goals. The first steps will involve publishing university guidelines for using AI, and individual departments and faculties will establish their own standards. Further down the line, Brown will invest in improving AI literacy among teaching staff, which it hopes will improve the way in which AI is used - not abused - across the university, and place informed restrictions on how students can use it without compromising their development. (R)