arXiv (OAI Expanded)
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Can LLMs Model Incorrect Student Reasoning? A Case Study on Distractor Generation
Modeling student misconceptions in a realistic manner is critical for AI in education. In this work, we examine how large language models (LLMs) reason about misconceptions when generating distractor answers for multiple-choice questions (MCQs), a task that requires producing answers that are incorr
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