用多智能体自动生成课程材料，减轻教师备课负担。
Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design
- 模拟角色协作的多智能体框架，统一生成讲义、课件、脚本和试题。
- 在5门大学课程中验证，显著缩短准备时间且材料获教师认可。
- 支持灵活人机协同，适合资源有限院校推广高质量教学。
制作高质量教学材料仍是一项耗时的工作，常需教师、教学设计师和助教间大量协调。本文提出Instructional Agents，一个基于大语言模型的多智能体框架，可端到端自动化生成课程内容，包括课程大纲、基于LaTeX的幻灯片、讲课稿和评估题。与以往仅聚焦单一任务的工具不同，该系统通过模拟角色化协作确保教学逻辑一致性。系统提供四种模式：自主模式、目录引导模式、反馈引导模式和完整协作者模式，灵活控制人类参与程度。我们在五门大学课程中评估该系统，结果显示其生成的内容经教师评审后可直接使用，显著降低课程准备时间。对于缺乏教学设计能力的机构，该框架为实现高质量教育普及提供了可扩展、低成本的解决方案，尤其适用于资源匮乏地区。项目官网及源码见https://darl-genai.github.io/instructional_agents_homepage/
原文摘要 · Abstract (English)
Preparing high-quality instructional materials remains a labor-intensive process that often requires extensive coordination among teaching faculty, instructional designers, and teaching assistants. In this work, we present Instructional Agents, a multi-agent large language model framework designed to automate end-to-end course material generation, including syllabi creation, LaTeX-based slides, lecture scripts, and assessments. Unlike prior tools focused on isolated tasks, Instructional Agents simulates role-based collaboration to ensure pedagogical coherence. The system operates in four modes: Autonomous, Catalog-Guided, Feedback-Guided, and Full Co-Pilot mode, enabling flexible control over the degree of human involvement. We evaluate Instructional Agents across five university-level courses and show that it produces high-quality instructional materials that are reviewed and refined by teaching faculty prior to use, while significantly reducing the time required to prepare classroom-ready content. By supporting institutions with limited instructional design capacity, Instructional Agents provides a scalable and cost-effective framework to democratize access to high-quality education, particularly in underserved or resource-constrained settings. The project website, including source code, is available at https://darl-genai.github. io/instructional_agents_homepage/
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