用AI自动生成中学数学辅导培训课，提升新手导师教学能力
Automatic Large Language Models Creation of Interactive Learning Lessons
- 分步提示工程分解任务，生成结构化教学课程
- 人工评估显示分步生成的课程评分更高，内容更清晰
- 适合教育科技公司快速构建辅导培训体系
我们探索了利用自动化的互动式情境化课程来训练在线教授初中数学的新手导师。通过采用基于检索增强生成的提示工程方法，结合GPT-4o，开发出一个能够生成结构化导师培训课程的系统。研究针对三个核心主题——鼓励学生独立性、鼓励求助行为、开启摄像头——生成英文课程，采用任务分解提示策略将课程生成拆分为子任务。课程由两名人类评估者依据基于课程设计研究的综合评分量表进行定量与定性评估。结果表明，任务分解策略生成的课程在评分上显著优于单步生成。评估者指出，模型生成的课程具有结构良好、节省时间等优点，但也存在反馈泛化、部分教学环节表述不清等局限。研究证实了人机协作方式在生成有效导师培训课程方面的潜力。
原文摘要 · Abstract (English)
We explore the automatic generation of interactive, scenario-based lessons designed to train novice human tutors who teach middle school mathematics online. Employing prompt engineering through a Retrieval-Augmented Generation approach with GPT-4o, we developed a system capable of creating structured tutor training lessons. Our study generated lessons in English for three key topics: Encouraging Students' Independence, Encouraging Help-Seeking Behavior, and Turning on Cameras, using a task decomposition prompting strategy that breaks lesson generation into sub-tasks. The generated lessons were evaluated by two human evaluators, who provided both quantitative and qualitative evaluations using a comprehensive rubric informed by lesson design research. Results demonstrate that the task decomposition strategy led to higher-rated lessons compared to single-step generation. Human evaluators identified several strengths in the LLM-generated lessons, including well-structured content and time-saving potential, while also noting limitations such as generic feedback and a lack of clarity in some instructional sections. These findings underscore the potential of hybrid human-AI approaches for generating effective lessons in tutor training.
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