@inproceedings{wang-etal-2026-teachmaster,
title = "{T}each{M}aster: Generative Teaching via Code",
author = "Wang, Yuheng and
Yang, Runde and
Wu, Lin and
Zhang, Jie and
Fan, Jingru and
Zhou, Tianle and
Fu, Ruoyu and
Li, Huatao and
Shi, Ruijie and
Chen, Siheng and
E, Weinan and
Qian, Chen",
editor = "Li, Yunyao and
Rehm, Georg and
Tu, Mei",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-industry.7/",
pages = "88--98",
ISBN = "979-8-89176-394-4",
abstract = "The scalability of high-quality online education is hindered by the high costs and slow cycles of 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 shifting educators from manual creators to high-level directors who focus on pedagogical intents while 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, slashing production costs to only 0.3{\%} of traditional online course videos and providing a robust solution for scalable education."
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%0 Conference Proceedings
%T TeachMaster: Generative Teaching via Code
%A Wang, Yuheng
%A Yang, Runde
%A Wu, Lin
%A Zhang, Jie
%A Fan, Jingru
%A Zhou, Tianle
%A Fu, Ruoyu
%A Li, Huatao
%A Shi, Ruijie
%A Chen, Siheng
%A E, Weinan
%A Qian, Chen
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F wang-etal-2026-teachmaster
%X The scalability of high-quality online education is hindered by the high costs and slow cycles of 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 shifting educators from manual creators to high-level directors who focus on pedagogical intents while 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, slashing production costs to only 0.3% of traditional online course videos and providing a robust solution for scalable education.
%U https://aclanthology.org/2026.acl-industry.7/
%P 88-98
Markdown (Informal)
[TeachMaster: Generative Teaching via Code](https://aclanthology.org/2026.acl-industry.7/) (Wang et al., ACL 2026)
ACL
- 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. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 88–98, San Diego, California, USA. Association for Computational Linguistics.