To stop a whole class from cheating with AI, a teacher only needs to randomly audit 2 to 4 students.
March 31, 2026
Original Paper
Random Audits as a Scalable Deterrent to Cheating: Using Game Theory to Design Fair and Effective Academic Integrity Systems for the AI Era
SSRN · 6378478
The Takeaway
Using game theory, researchers found that the threat of a heavy penalty combined with a tiny, random 'spot check' is enough to make cheating mathematically irrational for the entire group. This challenges the assumption that effective anti-cheating measures require constant, invasive proctoring.
From the abstract
Generative AI has broken the assumption that online assessment performance reflects student knowledge, and the traditional countermeasures-proctoring software, AI detectors, lockdown browsers-are expensive, adversarial, and increasingly ineffective. This paper proposes a different paradigm: random post-assessment audits with credible penalties, modeled on the sampling-based enforcement strategies used in tax auditing, financial regulation, anti-doping, and nuclear nonproliferation. A game-theore