Despite the transformative effects large language models are having on education, the current economic models of student cheating do not factor in the impact of a highly capable artificial intelligence (AI). We attempt to fill this gap by constructing an economic model of student effort, course difficulty and cheating using AI on a per-course requirement basis. We find that to maximize student knowledge, there are two viable approaches to instructor selection of course difficulty. The ‘carrot’ approach is characterized by low course difficulty to incentivize effort with a high grade, while the ‘stick’ approach is characterized by high course difficulty to motivate enough effort to pass the course. We also find that student effort falls as the capability of AI increases. As a result, AI can eliminate the strategy of using high course difficulty to motivate students as a viable pedagogical option beyond a certain threshold level of AI capability.
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