2 papers
cs.CL2026
Can LLMs Model Incorrect Student Reasoning? A Case Study on Distractor Generation
Yanick Zengaffinen, Andreas Opedal, Donya Rooein +3
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 mul…
cs.CL2026
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…