Lpaa: Lesson Plan Automatic Agent Based on LangGraph

ZHANG Qiming; ZHANG Jiarui; HE Yuqing; ZHANG Yanbin

doi:10.11959/j.issn.2096-0271

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Lpaa: Lesson Plan Automatic Agent Based on LangGraph

更新时间：2026-02-27

- Lpaa: Lesson Plan Automatic Agent Based on LangGraph
- BIG DATA RESEARCH Pages: 1-14(2025)
- 作者机构：
  
  华东师范大学数据科学与工程学院，上海 200062
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271
  CLC： TP39
- Online First：17 December 2025，
- 稿件说明：
移动端阅览
张豈明,张嘉睿,何雨晴等.Lpaa：基于LangGraph的教案自动生成智能体[J].大数据,

ZHANG Qiming,ZHANG Jiarui,HE Yuqing,et al.Lpaa: Lesson Plan Automatic Agent Based on LangGraph[J].BIG DATA RESEARCH,
张豈明,张嘉睿,何雨晴等.Lpaa：基于LangGraph的教案自动生成智能体[J].大数据, DOI：10.11959/j.issn.2096-0271.

ZHANG Qiming,ZHANG Jiarui,HE Yuqing,et al.Lpaa: Lesson Plan Automatic Agent Based on LangGraph[J].BIG DATA RESEARCH, DOI：10.11959/j.issn.2096-0271.

摘要

本文提出Lpaa，一种基于LangGraph构建的教案自动生成智能体（Lesson Plan Automatic Agent）。该智能体由大语言模型（Large Language Model， LLM）驱动，通过内容解析引擎、指令链生成器、前序步骤描述生成器、并行输出引擎与可控校准优化器等五个协同模块，实现知识结构提取、教学任务分解、阶段衔接生成、模块并行处理与人机协同校准。Lpaa以智能体（agent）形式运行在LangGraph的图结构上，支持任务节点化、依赖约束表达及动态调度，从而显著提升生成效率与教学内容的一致性。在自构建的多维度评估框架 pEval 下，系统平均得分 8.48/10，得到领域专家的高度认可。消融实验进一步验证了智能体各子模块对整体性能的关键贡献。研究表明，Lpaa为中文教案自动生成提供了结构化、可扩展的智能体范式，可为提升教学质量与学习体验提供有效支持。

Abstract

This paper presents Lpaa

a LangGraph-based Lesson Plan Automatic Agent designed for intelligent generation of Chinese lesson plans. Powered by a Large Language Model (LLM)

the agent coordinates five functional modules— a Content Parsing Engine

an Instruction Chain Generator

a Previous Step Description Generator

a Parallel Output Engine

and a Controllable Calibration Optimizer. These modules collaboratively support knowledge structure extraction

teaching task decomposition

stepwise contextual linkage

parallel module processing

and human-in-the-loop calibration. Operating as an agent within the graph-structured LangGraph framework

Lpaa enables task node segmentation

dependency-aware scheduling

and dynamic workflow management

thereby significantly improving generation efficiency and content consistency. Under a self-constructed multidimensional evaluation framework

pEval

the system achieved an average score of 8.48 out of 10 and received strong recognition from domain experts. Ablation studies further validate the critical contributions of each agent module to overall system performance. The results demonstrate that Lpaa offers a structured and scalable agent-based paradigm for automatic Chinese lesson plan generation

providing effective technical support for enhancing instructional quality and learning experiences.

关键词

Keywords

references

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