SCIENCE@home

Pedagogy-R1: Pedagogical Large Reasoning Model and Well-balanced Educational Benchmark

Unggi Lee,Jaeyong Lee,Jiyeong Bae,Yeil Jeong,Junbo Koh,G. Lee,Gunho Lee,Taekyung Ahn,Hyeoncheol Kim

Published 2025 in International Conference on Information and Knowledge Management

ABSTRACT

Recent advances in large reasoning models (LRMs) have demonstrated impressive capabilities in highly structured domains such as mathematics and programming. However, their application to education-where effective reasoning must be pedagogically meaningful, context-sensitive, and responsive to real student needs-remains relatively unexplored. Existing large language models (LLMs) often struggle to deliver instructional coherence, formative feedback, or simulate sophisticated teacher decision-making, limiting their practical utility in educational settings. To fill this gap, we present Pedagogy-R1, a comprehensive pedagogical reasoning framework designed to adapt LLMs for authentic classroom tasks. Our approach features three key innovations: (1) a distillation-based training pipeline that uses pedagogically filtered outputs for instruction tuning, (2) the Well-balanced Educational Benchmark (WBEB), which systematically evaluates models across five dimensions-subject knowledge, pedagogical knowledge, knowledge tracing, essay scoring, and real-world teacher decision-making-and (3) the Chain-of-Pedagogy (CoP) prompting strategy, employed both to generate pedagogically enriched training data and to elicit teacher-like reasoning during inference. We conduct a mixed-methods evaluation, combining fine-grained quantitative analyses of model performance with qualitative insights into the model's pedagogical reasoning patterns.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    International Conference on Information and Knowledge Management

  • Publication date

    2025-11-10

  • Fields of study

    Computer Science, Education

  • Identifiers
    DOI 10.1145/3746252.3761133
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-42 of 42 references · Page 1 of 1

CITED BY