A Math Tutoring Dataset Based on Student Comprehension States for Socratic Question Generation

Q: What does this research mean for the field?

Training AI models on a math tutoring dataset that diagnoses student comprehension states to generate tailored Socratic questions consistently improves tutoring success rates and utterance quality. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Jiwoo Song; Juoh Sun; Geon Park; Harksoo Kim

doi:10.5626/jok.2026.53.8.788

August 24, 2026Journal of KIISE

A Math Tutoring Dataset Based on Student Comprehension States for Socratic Question Generation

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Key Points

To develop a Korean math tutoring dataset that diagnoses student problem and concept comprehension states to generate tailored Socratic questions rather than directly providing answers.
Designed a tutor-verifier-student tri-agent framework to diagnose comprehension states and generate adaptive Socratic dialogue.
Constructed a dataset comprising N=3,084 Korean math tutoring conversations.
Benchmarked and fine-tuned open-source domestic and international large language models on the dataset to assess tutoring efficacy.
Language models trained on student comprehension states achieved consistent performance improvements in tutoring success rates.
Comprehension-focused training resulted in qualitative enhancements across conversational utterance quality evaluations.

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Implication

Experimental evaluation demonstrates improved tutoring success and dialogue quality using a comprehension-state dataset for Socratic tutoring, highlighting enhanced AI-driven learning.

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Song et al. (2026) studied this question.

synapsesocial.com/papers/6a8e9af2451774b83f3b3620 https://doi.org/10.5626/jok.2026.53.8.788

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A Math Tutoring Dataset Based on Student Comprehension States for Socratic Question Generation

Key Points

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Implication

Abstract

Discussion

Cite This Study

A Math Tutoring Dataset Based on Student Comprehension States for Socratic Question Generation

Key Points

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Implication

Abstract

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