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Andreas Opedal

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Can LLMs Model Incorrect Student Reasoning? A Case Study on Distractor Generation

This work examines how large language models reason about misconceptions when generating distractor answers for multiple-choice questions (MCQs) and introduces a taxonomy over reasoning strategies for distractor generation that is grounded in learning-science literature and empirical observation.

Yanick Zengaffinen, Andreas Opedal, Donya Rooein et al. · 2 citations

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