AI use leads UC Berkeley math professor to see 'no point' in grading homework

Commenters mostly treated the professor’s remark as an assessment-design problem, not proof that mathematics education has become pointless. The broad view is that take-home work can no longer carry meaningful credit when a model can generate polished solutions on demand. But many insisted homework still matters because struggling through problems exposes false understanding, builds habits, and gives instructors a chance to correct reasoning before a high-stakes exam. The proposed fix was blunt: make homework low- or zero-stakes, retain feedback and completion requirements, then certify mastery through supervised written or oral exams. Several readers warned that this shift will punish procrastinators who rely on graded assignments to force steady work, while others replied that college students should own that responsibility. There was also optimism that the same systems that make untrusted submissions easy can become unusually capable tutors: an independent learner can get immediate, detailed critique of a proof instead of waiting for scarce human help. That promise came with practical doubts about whether AI can reliably interpret messy handwritten mathematics and assess rigorous proofs. A smaller but sharper thread challenged the article’s larger fatalism: AI solving homework does not establish that studying math, or universities themselves, has lost its purpose.