Generative Teaching via Code

Yuheng Wang, Runde Yang, Lin Wu, Jie Zhang, Jingru Fan +6 more
12/7/2025
cs.CYcs.AIcs.CL

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Abstract

The scalability of high-quality online education is hindered by the high costs and slow cycles of labor-intensive manual content creation. Despite advancements in video generation, current approaches often fail to ensure pedagogical structure and precise control due to their pixel-level, black-box nature. In this paper, we propose Generative Teaching, a novel paradigm that transitions educators from manual creators to high-level directors, allowing them to focus on pedagogical intent while autonomous agents handle the execution. To realize this vision, we introduce TeachMaster, a multi-agent framework that leverages code as an intermediate semantic medium. Unlike traditional video generation methods, TeachMaster orchestrates a collaborative team of agents--spanning planning, design, and rendering--to automate the production of interpretable, editable, and curriculum-ready educational videos. Experiments validate that TeachMaster significantly boosts production efficiency without compromising structural coherence or visual fidelity, providing a robust solution for scalable education.

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Cite this paper

@article{wang2025generative,
  title  = {Generative Teaching via Code},
  author = {Yuheng Wang and Runde Yang and Lin Wu and Jie Zhang and Jingru Fan and Ruoyu Fu and Tianle Zhou and Huatao Li and Siheng Chen and Weinan E and Chen Qian},
  year   = {2025},
  eprint = {2601.04204},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.04204v1}
}

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