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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. Our code is available on GitHub: https://github.com/GEMLab-HKU/ECTEL_Course_Gen_APP .

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Automatic Large Language Models Creation of Interactive Learning Lessons

  • 会议论文

  • 发表日期：

    2026年

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  • Jionghao Lin,
  • Jiarui Rao,
  • Sandy Yiyang Zhao,
  • Yuting Wang,
  • Ashish Gurung,
  • Amanda Barany,
  • Jaclyn Ocumpaugh,
  • Ryan S. Baker,
  • Kenneth R. Koedinger

摘要

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. Our code is available on GitHub: https://github.com/GEMLab-HKU/ECTEL_Course_Gen_APP .