Instructional Agents: Reducing Teaching Faculty Workload through Multi-Agent Instructional Design

TL;DR

Instructional Agents is a multi-agent LLM framework that automates course material creation, reducing faculty workload and making high-quality education more accessible, especially in resource-limited settings.

Contribution

This work introduces a multi-agent LLM system for end-to-end instructional material generation with role-based collaboration and flexible operation modes.

Findings

Produces high-quality materials reviewed by faculty

Significantly reduces preparation time

Effective across diverse university courses

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 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/