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TeachMaster: Generative Teaching via Code

Paper · 2026 · Yuheng Wang, Runde Yang, Lin Wu et al.

Summary

Producing online course videos by hand is costly and slow; typical courses cost around $30,000. Pixel-level video generators such as Sora 2 are expensive to run, hard to edit, and weak on pedagogical structure. The authors propose "Generative Teaching," in which educators act as directors who state teaching goals while agents do the production work. They implement this idea in TeachMaster, a multi-agent system that uses executable code as the intermediate representation. Starting from a lecture outline, the pipeline runs in three stages:

  • Content planning: drafting the script and splitting it into pages.
  • Presentation generation: writing Manim animation code, with optional image insertion, plus narration and TTS.
  • Quality validation: debugging code, syncing audio and visuals, and fixing layouts.

Because every video is code, it stays interpretable and editable. Evaluated against Sora 2 and professional human-made videos, TeachMaster greatly improves efficiency while keeping quality close to human-made content. It has been deployed at scale across many disciplines in both Chinese and English. The work shows that structured, programmatic generation can make curriculum-ready video affordable.

Key points

  • A 45-hour semester course costs about $83.70 to produce, roughly 0.3% of traditional online-course production costs.
  • Visuals are rendered with the Manim engine. The coding agent can use either Gemini-3 through its API or a locally deployed Qwen3-32B tuned for Manim code, and Minimax handles TTS.
  • Scoring was done by GPT-5.2 on a 1–10 scale. Three human experts checked 300 sampled videos and agreed with the model's judgments 81.71% of the time.
  • In deployment, 75.2% of pages needed no manual edits. The remaining pages were finished in an average of 1.88 interaction rounds.
  • The system has produced over 30,000 minutes of content across more than 40 disciplines, with deployment involving about 50 universities.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksComputer Science Applications · 102 works

Cite

Cite as Yuheng Wang, Runde Yang, Lin Wu, Jie Zhang, Jingru Fan, Tianle Zhou, Ruoyu Fu, Huatao Li, Ruijie Shi, Siheng Chen, Weinan E and Chen Qian. 2026. TeachMaster: Generative Teaching via Code. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-industry.7

@inproceedings{wang2026teachmaster,
  title = {{TeachMaster: Generative Teaching via Code}},
  author = {Yuheng Wang, Runde Yang, Lin Wu, Jie Zhang, Jingru Fan, Tianle Zhou, Ruoyu Fu, Huatao Li, Ruijie Shi, Siheng Chen, Weinan E and Chen Qian},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-industry.7},
  url = {https://doi.org/10.18653/v1/2026.acl-industry.7}
}

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