Berkeley Professor Stops Grading Homework Over AI—How Teachers Are Adapting

A University of California, Berkeley professor has stopped grading students' homework for accuracy over concerns that AI is making it harder for educators and teachers to use traditional written assignments to assess students' abilities.

Richard Borcherds, a mathematics professor at UC Berkeley who won the Fields Medal in 1998, now gives students credit for completing their homework on time, rather than assessing whether their answers are correct, according to reports by The Inc.

Borcherds believes there’s "no point" in marking papers that have been generated with AI, telling the California Post: "Grading for correctness just adds a huge incentive for them to use AI for homework instead of doing it themselves, and penalizes students for honesty."

His move comes as universities across the country grapple with the rapid advancement of AI and its impact on education, with some professors turning to oral examinations, in-person assessments and other methods designed to establish what students actually understand and can do.

Borcherds told the California Post students' use of AI for homework is now widespread.

He also said there was "no easy way to tell the difference between human and AI generated work," adding that improvements in the technology mean obvious AI mistakes are becoming rarer.

Borcherds has consequently moved to grading homework based on completion, meaning students receive credit for just handing in the work rather than for getting the answers right. While he’s acknowledged that it’s "not a very good system," he doesn’t currently have a better alternative.

The concerns extend well beyond Berkeley.

Research by Cornell University in May found that, among more than 95,000 students across 20 U.S. public research universities, 37 percent reported using generative AI at least monthly, while 9 percent reported using it to cheat.

"Assessment reform is necessary and urgent," study co-author Rene Kizilcec, an associate professor at Cornell, said. He argued that misuse of generative AI creates a problem for the validity of assessments and, in turn, "the credibility of university credentials."

How Is AI Impacting Mathematics?

Borcherds has said the rapid development of AI has changed his view of the future of mathematics as a profession. Five years ago, he believed mathematics was among the fields relatively protected from advances in AI. Now, he has said he doesn’t know what advice to give students considering a career in the subject.

"If AI continues to improve at the present rate," Borcherds told The Inc., "then, in a few years, there'll be nothing human mathematicians can do that AI can't do better."

His concerns come as AI systems become increasingly capable of tackling the kinds of assignments traditionally used to teach and assess mathematics.

A 2026 report from MIT's Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training found that AI was forcing the university to reconsider fundamental aspects of education, including how students are assessed. The committee called on instructors to change current teaching and assessment processes for an "AI-aware world," while emphasizing that AI should supplement human learning rather than automate it.

The issue is particularly relevant to mathematics and other technical subjects because AI can help students solve complex problems, making it harder for instructors to assess their real understanding and reasoning.

The challenge for educators, therefore, is not simply whether students should be allowed to use AI, but how to make sure they continue developing the underlying mathematical reasoning and problem-solving skills that traditional coursework is intended to teach.

Teachers Turn to Oral and In-Person Assessments

Some educators are responding not by abandoning assessments, but by changing their format.

Cornell University's Center for Teaching Innovation says oral assessments can be paired with written assignments, projects or problem sets to "discourage unauthorized or uncritical use of AI." Options include oral exams, question-and-answer sessions about a student's paper or project, presentations, role-plays, debates, and demonstrations.

At the University of Pennsylvania, Associate Professor Emily Hammer has paired oral examinations with written papers in her seminar classes. She has argued that the concern goes beyond preventing cheating, saying: "We're doing this because students are actually losing skills, losing cognitive capacity and creativity."

Penn has also highlighted the experience of mathematics professor Phil Gressman, who replaced a final written exam with individual oral exams in 2020 and has continued using the format. Gressman said the conversations allowed him to determine whether students genuinely understood concepts by asking follow-up questions, telling the university: "I felt like I got a better sense of what's going on."

Universities Rethink Assessment in the AI Era

The issue is prompting changes at an institutional level, too.

MIT President Sally Kornbluth said generative AI's opportunities and risks represented a "watershed moment" for MIT and higher education more broadly. She said practical changes would include "reevaluating how we assess what students have learned" and placing renewed emphasis on hands-on learning.

MIT has also recommended that every class clearly state its policy on generative AI, including what uses are permitted and why. The university has stopped short of imposing a single institution-wide classroom rule, instead providing instructors with a menu of approaches that can be adapted to individual courses.

Together, the approaches show how the arrival of increasingly sophisticated AI is shifting the question for educators from simply how to stop students using AI to how to design assessments that still demonstrate what students themselves know and can do.

Newsweek has contacted Borcherds, Cornell University and MIT for comment.