@inproceedings{wang-etal-2026-knowing,
title = "From Knowing to Teaching: Scaffolding Pedagogical Decisions for {LLM} Agent",
author = "Wang, Yucheng and
Yang, Shen and
Yu, Jifan and
Li, Haoxuan and
Lim, Joy Jia Yin and
Zhang-Li, Daniel and
Liu, Huiqin and
Hou, Lei and
Li, Juanzi and
Xu, Bin",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1328/",
pages = "28778--28801",
ISBN = "979-8-89176-390-6",
abstract = "Knowing and teaching differ fundamentally: effective instruction requires transforming knowledge into forms learners can grasp. Large language models, when asked to generate lessons (a concrete form of teaching), produce content lacking pedagogical depth. We trace this failure to three decisions that expert teachers make: \textit{selecting} content by recognizing each source{'}s instructional role, \textit{sequencing} topics so foundations precede applications, and \textit{synthesizing} components into a unified whole. To scaffold these decisions, we introduce \textbf{TeachCraft}, a framework with three agents: Explorer classifies sources by pedagogical intent to guide selection; Planner orders objectives from foundational to advanced; Generator produces lesson materials through a schema that ensures consistency across components. To evaluate this approach, we construct LessonBench, 40 expert-designed lessons paired with two to five heterogeneous source documents, on which TeachCraft achieves 67.8{\%} win rate in human evaluation and 79.6{\%} in LLM-based evaluation against eight baselines, with ablations confirming that each decision contributes independently to overall lesson quality.[Source code is available at {\ensuremath{<}}https://anonymous.4open.science/r/TeachCraft-1672{\ensuremath{>}}]"
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<abstract>Knowing and teaching differ fundamentally: effective instruction requires transforming knowledge into forms learners can grasp. Large language models, when asked to generate lessons (a concrete form of teaching), produce content lacking pedagogical depth. We trace this failure to three decisions that expert teachers make: selecting content by recognizing each source’s instructional role, sequencing topics so foundations precede applications, and synthesizing components into a unified whole. To scaffold these decisions, we introduce TeachCraft, a framework with three agents: Explorer classifies sources by pedagogical intent to guide selection; Planner orders objectives from foundational to advanced; Generator produces lesson materials through a schema that ensures consistency across components. To evaluate this approach, we construct LessonBench, 40 expert-designed lessons paired with two to five heterogeneous source documents, on which TeachCraft achieves 67.8% win rate in human evaluation and 79.6% in LLM-based evaluation against eight baselines, with ablations confirming that each decision contributes independently to overall lesson quality.[Source code is available at \ensuremath<https://anonymous.4open.science/r/TeachCraft-1672\ensuremath>]</abstract>
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%0 Conference Proceedings
%T From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent
%A Wang, Yucheng
%A Yang, Shen
%A Yu, Jifan
%A Li, Haoxuan
%A Lim, Joy Jia Yin
%A Zhang-Li, Daniel
%A Liu, Huiqin
%A Hou, Lei
%A Li, Juanzi
%A Xu, Bin
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F wang-etal-2026-knowing
%X Knowing and teaching differ fundamentally: effective instruction requires transforming knowledge into forms learners can grasp. Large language models, when asked to generate lessons (a concrete form of teaching), produce content lacking pedagogical depth. We trace this failure to three decisions that expert teachers make: selecting content by recognizing each source’s instructional role, sequencing topics so foundations precede applications, and synthesizing components into a unified whole. To scaffold these decisions, we introduce TeachCraft, a framework with three agents: Explorer classifies sources by pedagogical intent to guide selection; Planner orders objectives from foundational to advanced; Generator produces lesson materials through a schema that ensures consistency across components. To evaluate this approach, we construct LessonBench, 40 expert-designed lessons paired with two to five heterogeneous source documents, on which TeachCraft achieves 67.8% win rate in human evaluation and 79.6% in LLM-based evaluation against eight baselines, with ablations confirming that each decision contributes independently to overall lesson quality.[Source code is available at \ensuremath<https://anonymous.4open.science/r/TeachCraft-1672\ensuremath>]
%U https://aclanthology.org/2026.acl-long.1328/
%P 28778-28801
Markdown (Informal)
[From Knowing to Teaching: Scaffolding Pedagogical Decisions for LLM Agent](https://aclanthology.org/2026.acl-long.1328/) (Wang et al., ACL 2026)
ACL
- 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. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 28778–28801, San Diego, California, United States. Association for Computational Linguistics.