Pedagogy-R1: Pedagogical Large Reasoning Model and Well-balanced Educational Benchmark
Unggi Lee, Jaeyong Lee, Jiyeong Bae, Yeil Jeong, Junbo Koh, Gyeong-Geon Lee, Gunho Lee, Taekyung Ahn, Hyeoncheol Kim · 2025
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.