用合成数据训练的智能助教，帮教师实时解决教学难题
TeachingCoach: A Fine-Tuned Scaffolding Chatbot for Instructional Guidance to Instructors
- 基于教育资料提取教学规则，用合成对话微调专用模型
- 专家评估显示其指导更清晰、反思更深，优于GPT-4o mini
- 适合教师发展支持系统设计者，兼顾深度与交互效率
高等教育教师常缺乏及时且符合教学法的支持，现有工具或依赖通用聊天机器人建议，或依赖非可扩展的教学中心人工咨询。我们提出 TeachingCoach，一个基于教学法的对话式助教，通过实时对话为教师专业发展提供指导。该系统采用数据驱动流程，从教育资料中提取教学法则，并利用合成对话生成技术微调专用语言模型，帮助教师识别问题、诊断成因并制定策略。专家评估表明，TeachingCoach 的指导比 GPT-4o mini 更清晰、更具反思性且响应更恰当；对高校教师的用户研究揭示了对话深度与交互效率之间的权衡。结果表明，基于教学法和合成数据的聊天机器人可提升教学支持效果，为未来教学聊天机器人系统提供可扩展的设计范式。
原文摘要 · Abstract (English)
Higher education instructors often lack timely and pedagogically grounded support, as scalable instructional guidance remains limited and existing tools rely on generic chatbot advice or non-scalable teaching center human-human consultations. We present TeachingCoach, a pedagogically grounded chatbot designed to support instructor professional development through real-time, conversational guidance. TeachingCoach is built on a data-centric pipeline that extracts pedagogical rules from educational resources and uses synthetic dialogue generation to fine-tune a specialized language model that guides instructors through problem identification, diagnosis, and strategy development. Expert evaluations show TeachingCoach produces clearer, more reflective, and more responsive guidance than a GPT-4o mini baseline, while a user study with higher education instructors highlights trade-offs between conversational depth and interaction efficiency. Together, these results demonstrate that pedagogically grounded, synthetic data driven chatbots can improve instructional support and offer a scalable design approach for future instructional chatbot systems.
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