Instructional Agents: LLM Agents on Automated Course Material Generation for Teaching Faculties

2025-08-27

AI Summary

Teaching material development heavily relies on manual collaboration, resulting in low efficiency and high costs. This paper proposes the first multi-agent large language model framework tailored for course generation, wherein role-specialized agents—such as curriculum designers, lecturers, and assessment experts—collaboratively emulate pedagogical team workflows to generate syllabi, lecture scripts, LaTeX-based slides, and assessments end-to-end. We innovatively introduce four human-AI collaboration paradigms—autonomous, outline-guided, feedback-guided, and full co-piloting—integrating prompt engineering, automated LaTeX typesetting, feedback-driven iterative refinement, and alignment with course-specific knowledge graphs. Evaluated across five undergraduate computer science courses, our framework significantly reduces development time and human effort while producing pedagogically consistent, production-ready materials—particularly beneficial in resource-constrained educational settings.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Human-AI Collaboration / Human-AI Teaming

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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 (LLM) framework designed to automate end-to-end course material generation, including syllabus creation, lecture scripts, LaTeX-based slides, and assessments. Unlike existing AI-assisted educational tools that focus on isolated tasks, Instructional Agents simulates role-based collaboration among educational agents to produce cohesive and pedagogically aligned content. 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 computer science courses and show that it produces high-quality instructional materials while significantly reducing development time and human workload. 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.

Problem

• Automating end-to-end course material generation process
• Reducing labor-intensive instructional development workload
• Supporting scalable high-quality education democratization

Innovation

• Multi-agent LLM framework automates course generation
• Role-based collaboration produces pedagogically aligned content
• Four operational modes enable flexible human involvement