BROWN PROFESSOR EXPOSES AI CHEATING: GRADES PLUMMET FROM 96 TO 48%

The professor's 86 students faced a dramatic shift in testing conditions. The take-home exam showed minimal variation in performance, raising red flags about authenticity. When converted to a proctored in-person format, the grade disparity became undeniable. The findings align with recent research. Two major studies—one from China and another from UC Berkeley—documented similar patterns: students relying on AI for homework perform significantly worse on supervised exams. The gap suggests students may be using AI tools to complete assignments without genuinely learning material, then struggling when forced to demonstrate knowledge independently. The Brown case serves as a concrete data point in the ongoing debate about AI's role in education and academic integrity. Universities face mounting pressure to address AI cheating as tools become more sophisticated and accessible to students.