2 papers
cs.CL2026
Can LLMs Model Incorrect Student Reasoning? A Case Study on Distractor Generation
Yanick Zengaffinen, Andreas Opedal, Donya Rooein +3
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 ge…
cs.CL2025
Are Language Models Efficient Reasoners? A Perspective from Logic Programming
Andreas Opedal, Yanick Zengaffinen, Haruki Shirakami +5
Modern language models (LMs) exhibit strong deductive reasoning capabilities, yet standard evaluations emphasize correctness while overlooking a key aspect of reasoning: efficiency…