UC Berkeley Math Professor Richard Borcherds: Grading for Accuracy Is Meaningless After AI Can Solve Problems
Richard Borcherds, a mathematics professor at the University of California, Berkeley, argues that grading student assignments for accuracy has lost its meaning now that AI can solve complex mathematical problems, including the Navier-Stokes existence and smoothness problem. He currently grades only on whether assignments are submitted in full, while admitting uncertainty about whether he should still encourage young people to major in mathematics.
Richard Borcherds, who currently teaches mathematics courses at the University of California, Berkeley, has made a decision that has attracted attention in academia: he no longer grades students' math assignments for accuracy, but only for whether they are submitted in full. In an interview with The Daily Californian, he explained his logic: the use of AI has become inevitable, and since machines can solve problems, it is meaningless to pay teachers to check whether students' answers are correct. He admitted that this is not a particularly good system, but there is currently no alternative.
This judgment is based on two specific points. First, Borcherds has long observed that many students use AI to complete their assignments and often receive top marks. Second, OpenAI recently announced that its AI models had solved a series of mathematical problems, including the Navier-Stokes existence and smoothness problem. Based on these two points, Borcherds drew a broader inference: if AI continues to advance at its current pace, there will be nothing human mathematicians can do in a few years that AI cannot do better.
However, abandoning grading for accuracy does not mean the problem is solved. The UC Berkeley campus tested several AI detection tools, including Turnitin and Rumi, and ultimately concluded that they were not suitable for standardized use. A case from a university in Australia illustrates the unreliability of technical detection: the university's AI detector recorded nearly 6,000 suspected violations related to AI in 2024, but 90 percent of them were later proven to be false alarms. When detection tools themselves are unreliable, the dilemma facing teachers is not whether to use AI detection, but what else they can do besides abandoning grading.