Pedagogy-R1：教学对齐的推理模型与均衡教育基准
Pedagogy-R1: Pedagogically-Aligned Reasoning Model with Balanced Educational Benchmark
AI总结：
提出Pedagogy-R1框架，通过蒸馏流水线、均衡教育基准和链式教学法提示策略，将大型推理模型适配于教学，并首次系统评估其教学能力。
摘要：
近期大型推理模型（LRMs）在数学和编程等结构化领域表现出强大性能；然而，它们往往缺乏教学连贯性和真实的教学行为。为弥合这一差距，我们提出了Pedagogy-R1，一个通过三项创新将LRMs适配于课堂使用的框架：（1）基于蒸馏的流水线，用于过滤和精炼模型输出以进行指令微调，（2）均衡教育基准（WBEB），用于评估学科知识、教学知识、追踪、作文评分和教师决策等方面的性能，以及（3）链式教学法（CoP）提示策略，用于生成和激发教师风格的推理。我们的混合方法评估结合了定量指标与定性分析，提供了对LRMs教学优势与局限的首次系统性评估。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
