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UC Berkeley Mathematics Professor Stops Grading Homework Amid Rise in AI‑Created Assignments

UC Berkeley Mathematics Professor Stops Grading Homework Amid Rise in AI‑Created Assignments

Richard Borcherds, a prominent mathematician at the University of California, Berkeley, said he will cease assigning and grading conventional homework in his classes, pointing to the increasing ubiquity of AI tools that can generate solutions indistinguishable from student‑written work.

Known for his demanding coursework and exacting grading, Borcherds noted that the swift advancement of large‑language models renders it practically impossible to confirm the genuineness of written work. "When a student submits a solution that might have been produced by an AI, I have no dependable method to distinguish it," he remarked, stressing that the assessment’s integrity is now at risk.

His move mirrors a wider unease among teachers regarding how readily generative AI can tackle problem sets, essays and coding tasks. Although numerous schools have trialed plagiarism‑detection tools, such software struggles against output from models capable of paraphrasing or generating original mathematical arguments. Consequently, Borcherds is moving his assessment approach to on‑site examinations, requiring students to prove their knowledge under supervised settings.

By switching to on‑campus exams, the department aims to safeguard the fundamental learning goals of its mathematics curriculum while deterring dependence on illicit help. This adjustment also fits a rising pattern across universities to revamp curricula, add oral defenses, and stress project‑based assignments that are more difficult for algorithms to outsource.

This shift prompts debate over the role of homework in teaching. Should AI reliably handle routine problems, instructors might have to redesign tasks to emphasize higher‑order reasoning, teamwork, or real‑world scenarios that demand contextual judgment beyond formulaic solutions. At present, Borcherds’s courses act as a pilot, with the university tracking results to see if comparable policies could spread to other departments.

Source: TechRadar
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