Brown Students Suspected of AI Cheating

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In a recent Inside Higher Ed article, reporter Emma Whitford profiles Brown University economics professor Roberto Serrano, who suspected that many of his students had used AI to cheat on a take-home exam. With his dean’s approval, he changed the final to an in-person test, a decision that led more than a dozen students to drop the course and many more to fail.

Below are key highlights from the Emma’s article:

  • Why the exam was take-home. Whitford reports that Prof. Serrano gave a take-home midterm for the first time in nearly two decades of teaching Welfare Economics and Social Choice Theory. Many students had expressed anxiety about being in a classroom following a December mass shooting at Brown that killed two students and injured nine, and Prof. Serrano felt allowing at-home exams was appropriate. Enrollment jumped from a typical 30 students to 86 this spring, an increase he attributes to the promised take-home format.
  • How he spotted it. According to Whitford, the midterm average came in at 96 percent, far above the historic range of 65 to 80 percent, even though Serrano had written a harder exam. Sensing something “fishy,” Prof. Sorrano and his graders ran the test through ChatGPT. The AI produced answers that mirrored his students’ work, “kind of correct, but very off and with a very convoluted style.” On one question that called for a direct proof, ChatGPT and many students alike used a contrived “contradiction argument” that Prof. Serrano could tell was not written by a human.
  • The in-person final. Whitford reports that Prof. Serrano warned students he suspected AI use and gave them a chance to prove him wrong. “I am not declaring [the midterm] void for now. I am going to give the class a chance to prove me wrong,” he wrote. “That is, if the distribution of the final exam is roughly similar to the distribution of the midterm, I will count the midterm. Otherwise, which is of course what I expect to happen, I will declare the midterm void and reweigh the final accordingly.” The results, he said, proved his point: 18 students dropped the class, nine stayed but skipped the final, and the final’s average fell to 48.6 percent, a historic low. He voided the midterm, made the final worth 80 percent of the grade, and 19 students failed.
  • A “meek” institutional response. Whitford reports that Prof. Serrano submitted his data to Brown’s Standing Committee on the Academic Code in May and heard nothing. After he went public in late June, the committee asked him to file individual complaints against each suspected student, including copies of their exams, a request he called “ridiculous,” noting that AI-detection tools are known for false positives and negatives. Brown spokesperson Brian Clark told Inside Higher Ed that the university treats every integrity allegation seriously, and that academic leaders had been in touch with Serrano but that he had not yet provided the details needed to formally pursue the case.
  • The bigger picture. Whitford notes that adjudicating mass cheating is difficult for faculty, who, as UC San Diego’s Tricia Bertram Gallant explains, are neither rewarded nor compensated for pursuing large cases. Meanwhile, a Brown committee on generative AI released its first report during the episode, finding that three-quarters of surveyed faculty worry about AI-enabled cheating. The committee recommended updating the academic code while also urging faculty to “de-emphasize punishment,” since no method can detect AI use with total accuracy.
  • “We cannot choose to become idiots.” Whitford closes on Serrano’s broader warning about what is at stake. “We cannot afford to have a society in which a significant fraction of our best young minds think that cheating is OK,” he said. “That leads to a declining society, to a failed society … We cannot choose to become idiots.”

Whitford notes that this Brown episode has become a test case for how universities respond to AI-enabled cheating at scale, exposing a gap between the speed at which the technology is reshaping the classroom and the readiness of institutions to keep up.

Acknowledgement: This summary was generated using AI writing tools as part of my experimental learning process. For more information, check out the original article listed below.

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