AI RESEARCH
Can LLMs Model Incorrect Student Reasoning? A Case Study on Distractor Generation
arXiv CS.AI
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ArXi:2603.15547v1 Announce Type: cross Modeling plausible student misconceptions is critical for AI in education. In this work, we examine how large language models (LLMs) reason about misconceptions when generating multiple-choice distractors, a task that requires modeling incorrect yet plausible answers by coordinating solution knowledge, simulating student misconceptions, and evaluating plausibility. We