Do in-person exams stop AI cheating? A Brown professor's grades say the take-home scores were the problem

The average score in Professor Roberto Serrano’s Brown University economics class fell from 96 out of 100 on a take-home midterm to 48.6 on an in-person final, a drop of roughly half, once students could no longer sit the exam wherever, and however, they chose.

Serrano has taught Welfare Economics and Social Choice Theory at Brown for nearly two decades. In December 2025, a gunman attacked the university’s campus and killed two people, one of whom had introduced herself to Serrano shortly before. Shaken by the attack, he decided his demanding spring 2026 section of ECON 1170 would allow take-home exams for both the midterm and the final, reasoning that students under that kind of strain deserved some flexibility.

Record scores, then a collapse

The course typically caps at around 30 students, and Serrano has taught sections with as few as eight. This time, 86 students signed up. The take-home midterm, sat on 5 March, produced an average score of 96 out of 100, with 40 students scoring a perfect 100. Historically, Serrano told Inside Higher Ed, the midterm average in this course has run between 65 and 80 per cent, on exams he considers easier than the one he set this year.

Suspicious of results that good, Serrano moved the final exam into a proctored room. Eighteen of the 86 enrolled students dropped the course before the final was sat, and a further nine did not turn up to it. Most of those 27 students had scored well on the take-home midterm, several of them a perfect 100. Among the students who did sit both exams, the average collapsed to 48.6 out of 100, and only a handful finished within 10 points of their midterm grade.

Serrano has not accused any individual student of cheating, and Brown has confirmed that no formal complaint had reached its Standing Committee on the Academic Code at the time of reporting. “Brown treats every allegation of academic integrity with the utmost seriousness,” a university spokesperson, Brian Clark, said. What Serrano has, instead, is a chart: a class that scored extraordinarily well under unsupervised conditions and far worse under supervised ones, at a scale too large to explain away as nerves or bad luck. That is circumstantial evidence, not proof against any one student, and the distinction matters. A grade collapse this size is a strong signal that something changed between the two exam rooms. It is not, on its own, a verdict on any individual sitting in either of them.

Why a blunt instrument beats a clever one

Serrano’s case sits alongside a wider pattern. A recent survey at Princeton found 29.9 per cent of students admitted to using AI on at least one exam or assignment. What makes Brown’s numbers unusual is the scale of the swing, and how directly it was produced: change the exam format, and watch the results move by roughly half. That is a much blunter instrument than an AI-detection score, and also a much less ambiguous one. It does not diagnose which student used which tool, or how. It simply removes the opportunity and reports what is left.

The blunt-instrument approach has a cost of its own, and it is worth naming rather than glossing over. Moving a whole course back into an exam hall makes life harder for students who rely on extended time, quiet rooms or other disability accommodations, and it reintroduces the old unfairness of a single high-stakes sitting for anyone who gets ill or has a bad morning. It is also not obviously scalable: Serrano’s course had 86 students in a proctored room, but a large introductory lecture might have several hundred, and finding invigilated space for all of them is a logistics problem long before it is a pedagogical one. None of that makes the take-home result any less striking. It does mean the fix cannot simply be “put every exam back in a hall” without reckoning with who that quietly disadvantages.

There is also a narrower lesson for how institutions treat suspicion versus proof. GPTZero and similar detection tools promise a score for an individual piece of writing, and that promise has not held up well under independent testing, with false positive rates in double figures on real student work. Serrano’s chart makes no such promise about any one student. It only shows what a class does, in aggregate, when the format changes, which is a weaker claim in one sense and a more honest one in another: it never pretends to know which student cheated, only that the group’s performance depended heavily on conditions that no longer applied.

Do in-person exams stop AI cheating? Not exactly. A proctored room removes the opportunity during the exam itself, but it does nothing about what a student did beforehand, and it revives its own old problems, from accessibility to the sheer cost of running exams at scale for hundreds of students at once. What Serrano’s numbers suggest is narrower, and more useful. Take-home assessment, in a course of this size, was probably no longer measuring what it was designed to measure. Whether Brown, or any other university watching this case, treats that as a reason to redesign assessment or simply to police it harder is the question the numbers cannot answer by themselves.

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