The paper defines the task of generating reasoning trajectories for Socratic debugging of student code, releases an annotated dataset, and shows LLMs can produce up to 91% correct trajectories and 98.7% valid conversation turns per LLM-as-judge evaluation.
arXiv preprint arXiv:2502.18940
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Reasoning Trajectories for Socratic Debugging of Student Code: From Misconceptions to Contradictions and Updated Beliefs
The paper defines the task of generating reasoning trajectories for Socratic debugging of student code, releases an annotated dataset, and shows LLMs can produce up to 91% correct trajectories and 98.7% valid conversation turns per LLM-as-judge evaluation.
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Interpretable Difficulty-Aware Knowledge Tracing in Tutor-Student Dialogues
A difficulty-aware conversational knowledge tracing framework that combines LLMs with Item Response Theory to produce interpretable student performance predictions in tutor dialogues.
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