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From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent

Paper · 2026 · Yucheng Wang, Shen Yang, Jifan Yu et al.

Summary

LLMs know a great deal about many subjects, but they struggle to turn that knowledge into good lessons. Their generated lessons look tidy yet misuse sources, skip foundational steps, and quiz learners on material never presented. The authors frame this as a knowing-teaching gap, drawing on Shulman's notion of Pedagogical Content Knowledge. They isolate three decisions that expert teachers make: selection, sequencing, and synthesis.

Guided by interviews with K-12 teachers, they built TeachCraft, a three-agent pipeline for multi-document lesson synthesis:

  • Explorer labels each source chunk with a pedagogical intent from a six-category taxonomy.
  • Planner orders learning objectives along Bloom's taxonomy.
  • Generator produces slides, scripts and quizzes through a shared schema that keeps them mutually consistent.

For evaluation, the authors release LessonBench: 40 expert-designed lessons, each built from between two and five source documents of varied types. In pairwise human and LLM judging, TeachCraft beats eight baselines, and ablations show that each decision helps on its own. The work suggests that teaching skill can be split into explicit, engineerable steps.

Key points

  • Win rates: TeachCraft wins 67.8% of human pairwise judgments (24 raters, 920 judgments) and 79.6% of LLM-judge comparisons. The eight baselines include direct prompting, general agents and educational systems such as NotebookLM.
  • LessonBench: 40 lessons across four subject areas, built by retrieval-augmented curation from MIT OpenCourseWare and Khan Academy.
  • Ablations: removing synthesis or sequencing hurts overall lesson quality the most.
  • SlidesBench: visual quality is competitive with existing slide generators, with 100% execution reliability.
  • Limitations: the multi-agent design needs many LLM calls, which raises latency and cost compared with direct prompting.

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

 Institutions

🎓Tsinghua UniversityBeijing · China333 works · 87,019 citations

 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 worksInformation Systems · 1,437 works

Cite

Cite as Yucheng Wang, Shen Yang, Jifan Yu, Haoxuan Li, Joy Jia Yin Lim, Daniel Zhang-Li, Huiqin Liu, Lei Hou, Juanzi Li and Bin Xu. 2026. From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1328

@inproceedings{wang2026knowing,
  title = {{From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent}},
  author = {Yucheng Wang, Shen Yang, Jifan Yu, Haoxuan Li, Joy Jia Yin Lim, Daniel Zhang-Li, Huiqin Liu, Lei Hou, Juanzi Li and Bin Xu},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1328},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1328}
}

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