让大模型像老师一样教人，更懂教学逻辑。
Pedagogy-R1: Pedagogically-Aligned Reasoning Model with Balanced Educational Benchmark
- 用教学逻辑优化模型输出，提升可教性
- 构建多维度教育评估基准，覆盖知识与教学行为
- 通过教师式推理提示，生成自然教学流程
近期大型推理模型在数学、编程等结构化领域表现优异，但缺乏教学连贯性和真实教学行为。为此，我们提出Pedagogy-R1框架，包含三项创新：(1) 基于知识蒸馏的流水线，过滤并优化模型输出用于指令微调；(2) 全面教育评估基准（WBEB），从学科知识、教学知识、解题追踪、作文评分到教师决策等多维度评估；(3) 教学链（CoP）提示策略，生成和激发教师风格的推理过程。混合方法评估结合定量指标与定性分析，首次系统评估了大型推理模型在教学能力上的优劣。
原文摘要 · Abstract (English)
Recent advances in large reasoning models (LRMs) show strong performance in structured domains such as mathematics and programming; however, they often lack pedagogical coherence and realistic teaching behaviors. To bridge this gap, we introduce Pedagogy-R1, a framework that adapts LRMs for classroom use through three innovations: (1) a distillation-based pipeline that filters and refines model outputs for instruction-tuning, (2) the Well-balanced Educational Benchmark (WBEB), which evaluates performance across subject knowledge, pedagogical knowledge, tracing, essay scoring, and teacher decision-making, and (3) a Chain-of-Pedagogy (CoP) prompting strategy for generating and eliciting teacher-style reasoning. Our mixed-method evaluation combines quantitative metrics with qualitative analysis, providing the first systematic assessment of LRMs' pedagogical strengths and limitations.
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