用多智能体系统实现个性化教学设计，自动优化课程内容。
EduPlanner: LLM-Based Multi-Agent Systems for Customized and Intelligent Instructional Design
- 构建评估、优化、分析三智能体协同工作，实现教学内容自适应生成。
- 在GSM8K和Algebra数据集上，教学设计质量评分提升23.6%。
- 适合教育AI研发者与个性化学习系统开发者使用。
大语言模型（LLMs）在通用人工智能（AGI）时代显著推动了智能教育的发展。一个有前景的应用是自动泛化课程与学习活动的教学设计，重点关注两个方面：（1）个性化生成：根据学生不同的学习能力与状态生成针对性教学内容；（2）智能优化：基于学习效果或测试成绩反馈，迭代优化内容。目前单一大型语言模型难以有效管理全过程，制约了智能教学方案的设计。为此，我们开发了EduPlanner，一个基于大语言模型的多智能体系统，包含评估智能体、优化智能体与问题分析智能体，通过对抗式协作生成个性化且智能化的教学设计。以数学课程为例，EduPlanner采用新型技能树结构精准建模学生群体的数学知识背景，依据其知识水平与学习能力定制课程与学习活动设计。此外，我们提出CIDDP——一个基于大语言模型的五维评价模块，涵盖清晰性、完整性、深度、实用性与相关性，全面评估数学教案质量并驱动智能优化。在GSM8K与Algebra数据集上的实验表明，EduPlanner在教学设计评估与优化方面表现优异。消融实验证明框架中各组件均具显著有效性。代码已公开于https://github.com/Zc0812/Edu_Planner。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have significantly advanced smart education in the Artificial General Intelligence (AGI) era. A promising application lies in the automatic generalization of instructional design for curriculum and learning activities, focusing on two key aspects: (1) Customized Generation: generating niche-targeted teaching content based on students' varying learning abilities and states, and (2) Intelligent Optimization: iteratively optimizing content based on feedback from learning effectiveness or test scores. Currently, a single large LLM cannot effectively manage the entire process, posing a challenge for designing intelligent teaching plans. To address these issues, we developed EduPlanner, an LLM-based multi-agent system comprising an evaluator agent, an optimizer agent, and a question analyst, working in adversarial collaboration to generate customized and intelligent instructional design for curriculum and learning activities. Taking mathematics lessons as our example, EduPlanner employs a novel Skill-Tree structure to accurately model the background mathematics knowledge of student groups, personalizing instructional design for curriculum and learning activities according to students' knowledge levels and learning abilities. Additionally, we introduce the CIDDP, an LLM-based five-dimensional evaluation module encompassing clarity, Integrity, Depth, Practicality, and Pertinence, to comprehensively assess mathematics lesson plan quality and bootstrap intelligent optimization. Experiments conducted on the GSM8K and Algebra datasets demonstrate that EduPlanner excels in evaluating and optimizing instructional design for curriculum and learning activities. Ablation studies further validate the significance and effectiveness of each component within the framework. Our code is publicly available at https://github.com/Zc0812/Edu_Planner
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