AI RESEARCH
Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues
arXiv CS.CL
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ArXi:2605.01097v1 Announce Type: new Recent advances in large language models (LLMs) have led to the development of AI-powered tutoring systems that provide interactive via dialogue. To enable these tutoring systems to provide personalized, it is essential to assess student performance at each turn, motivating knowledge tracing (KT) in dialogue settings. However, existing dialogue-based KT approaches often ignore question difficulty modeling and rely on opaque latent representations from LLMs, hindering accurate and interpretable prediction.