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.
InProceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1, ICER ’22, page 27–43, New York, NY , USA
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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.