The article says a Brown professor teaching an economics course switched to take-home, closed-book exams and concluded that mass AI cheating followed. He points to a sharp gap between the midterm and an in-person final as evidence, and says the episode threatens academic integrity at elite universities. Commenters largely accepted the core premise that AI has made cheating easier, but they did not treat Brown as the interesting part. The stronger read was that a take-home closed-book exam was already an unstable format before large language models, and AI just turned a bad incentive structure into a rout.
The practical consensus was blunt. For foundational subjects, unsupervised take-home assessment is no longer credible if the goal is to certify what a student can do alone. The fallback is old but effective: in-person
proctored exams, often on paper, sometimes on institution-controlled computers, plus oral follow-ups for assignments that can be outsourced. Several professors said they now design courses as adversarial systems. They assume students will optimize for grade with minimum work, then build assessments so the easiest route to a good grade still requires real understanding. That means more frequent low-stakes quizzes,
viva-style checks on submitted work, and clearer alignment between what is graded and what students must personally demonstrate.
A second theme was that cheating is not mainly a morality story. Students are responding to incentives created by credential inflation, curved grading, weak entry-level job markets, and the use of degrees as hiring filters. In that world, many treat college as a gatekeeping transaction rather than a learning environment, and AI is simply the newest tool for gaming the signal. That framing led some people to a harder conclusion than the article did: universities are not just fighting a cheating wave, they are discovering how much of their assessment machinery depended on norms of trust that no longer hold at scale. A few commenters pushed the opposite direction and argued schools should permit AI and redesign work around AI-assisted output, but most thought that only makes sense after students have already mastered the fundamentals without help.