Summary: A university professor’s viral post shows evidence of widespread AI cheating at one of the nation’s best schools. A dramatic drop in test scores on a take home midterm vs an in-class final exam points to AI cheating. While going back to paper exams is an option, the issue is deeper, a semester with no reliable learning signals, no engagement visibility.

The Chart That Broke the Internet

Most all of Roberto Serrano's economics students at Brown had turned in a suspiciously strong take-home midterm, with several perfect scores and a large number of individuals sitting comfortably in the high 90s. 

When he moved the final exam to be in-person, the results told a very different story: students who had aced the midterm dropped to the 50s and 60s on the final, and a few fell below 20. Many students pulled the rip cord early and withdrew from the class entirely rather than sit for it. As Serrano put it to Business Insider, "the cost of cheating has basically gone down to zero." 

The chart plotting his students' paired scores went viral within days, passed around by Y Combinator's Paul Graham and picked apart across tech and education circles alike. One commenter singled out the class's most consistently middling scorer — a 55, then a 59 — with the caption "hire this person”. Serrano agreed with the sentiment. In a class full of near-perfect scores that collapsed the moment AI wasn't available to lean on, honest, unglamorous struggle had quietly become a standout performance. 

The Wrong Diagnosis

Serrano's response was the one most instructors reach for when a story like this breaks: eliminate the take-home exams. It's an understandable reflex. But it is short sighted. It treats the problem as an enforcement gap, as though students had simply found a loophole that now needs closing, when what the data actually shows is something more unsettling: an entire semester's worth of formative work produced almost no reliable signal about what these students had genuinely learned. 

That gap should worry institutions more than the scandal itself. Homework and take-home assessments exist for two reasons: to give students low-stakes room to practice, and to give instructors an early read on who is struggling before it's too late to help. When AI can quietly complete that work end-to-end, both functions disappear at once, and nobody notices until the stakes are much higher. 

The in-person final didn't just catch a pattern of cheating, it revealed that, for months, no one on the faculty side actually knew how these students were doing. Cutting take-home work might protect the final exam's integrity, but it doesn't deliver the practice, feedback, and early-warning signals that were missing along the way.

What Actually Needs to Wake Up

Serrano's fix was predictable, but it isn't an answer that scales across a curriculum, because the underlying problem was never really the assignment format he chose. The problem is the absence of any infrastructure that lets instructors see how students are engaging with the course materials, and how they are using AI, while the work is happening.

This is where observability built into the course delivery platform itself changes the game. Imagine Serrano's course running on a platform where daily student engagement (or a lack thereof) is visible. A place where engaging AI experiences are inside the coursework. Where students are so engaged and immersed in their learning journey that their dependence on consumer AI for shortcuts is broken. Imagine a place where they are comfortable being wrong. In a place like that they don’t need AI to be perfect. In a world like this a dependence on AI provides no benefit. That is a place where students can learn, comfortably, that allows curiosity driven learning, where shortcuts have no incentive. That place is ScholarStack.

Day by day and week by week the professor portal in ScholarStack showcases each student's learning journey as they work through the assigned course materials — asking questions, revealing their own gaps of knowledge, building toward understanding. A student whose engagement looked thin in week four would show up as a flag in week four, not as a mystery on final exam day. 

Serrano told Business Insider that this should be "a wake-up call to the professors," and he's right, though the learning isn't "eliminate take-home work." It's closer to "your assessment design currently has no way of knowing whether learning happened until it's far too late to do anything about it." Solving that is a harder and more structural problem than simply swapping exam formats.

Stay tuned for future posts about important topics on AI in education. Thanks for reading. 

– David Miller & Gladys Mercier