Brown University Professor Says Majority of Students Used AI to Cheat

Inside Higher Ed reports that a Brown University professor suspects the majority of students in his class used AI to complete course assignments — describing subtle but consistent patterns across student submissions that detection tools flagged and human review confirmed. He stopped short of filing formal academic integrity complaints against all of them, a decision that reveals as much about the limits of existing enforcement mechanisms as it does about the scale of the problem.

The episode captures a bind instructors across higher education now face. Detection software produces too many false positives to serve as standalone evidence. Confronting an entire class creates an adversarial environment that undercuts learning. And the cost of doing nothing is a credential that no longer signals what it once did.

Universities have responded with a fragmented mix of policies — some banning AI outright, others asking students to disclose usage, others redesigning assessments around in-class oral defenses or iterative drafts that are harder to outsource. None has found a clean answer. The deeper problem is structural: a technology capable of passing graduate-level exams is now freely available to every undergraduate with a browser, and most academic integrity frameworks were written in 2005.

Read the full story at Inside Higher Ed