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

• URL: http://arxiv.org/abs/2508.19611v2
• Date: Mon, 01 Sep 2025 01:38:20 GMT
• Title: Instructional Agents: LLM Agents on Automated Course Material Generation for Teaching Faculties
• Authors: Huaiyuan Yao, Wanpeng Xu, Justin Turnau, Nadia Kellam, Hua Wei,
• Abstract summary: We present Instructional Agents, a framework designed to automate end-to-end course material generation.<n>The framework simulates role-based collaboration among educational agents to produce cohesive and pedagogically aligned content.<n>It produces high-quality instructional materials while significantly reducing development time and human workload.
• Score: 3.045939700894802
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models [78.73992315826035]
  We introduce Youtu-LLM, a lightweight language model that harmonizes high computational efficiency with native agentic intelligence.<n>Youtu-LLM is pre-trained from scratch to systematically cultivate reasoning and planning capabilities.
  arXiv  Detail & Related papers  (2025-12-31T04:25:11Z)
• Generative Teaching via Code [43.45801837806965]
  TeachMaster orchestrates a collaborative team of agents--spanning planning, design, and rendering--to automate the production of interpretable, editable, and curriculum-ready educational videos.<n>Experiments validate that TeachMaster significantly boosts production efficiency without compromising structural coherence or visual fidelity.
  arXiv  Detail & Related papers  (2025-12-07T12:52:24Z)
• Agentic Workflow for Education: Concepts and Applications [7.875055566698523]
  This study introduces the Agentic for Education (AWE), a four-component model comprising self-reflection, tool invocation, task planning, and multi-agent collaboration.<n>AWE offers a promising path toward reducing teacher workload, enhancing instructional quality, and enabling broader educational innovation.
  arXiv  Detail & Related papers  (2025-09-01T14:39:48Z)
• Enabling Multi-Agent Systems as Learning Designers: Applying Learning Sciences to AI Instructional Design [6.080614844688028]
  This study shifts pedagogical expertise from the user's prompt to the LLM's internal architecture.<n>We tested three systems for generating secondary Math and Science learning activities.
  arXiv  Detail & Related papers  (2025-08-20T14:44:00Z)
• Self-Challenging Language Model Agents [98.62637336505242]
  We propose the Self-Challenging framework for training an agent on high-quality tasks that are generated by itself.<n>The framework achieves over a two-fold improvement in Llama-3.1-8B-Instruct, despite using only self-generated training data.
  arXiv  Detail & Related papers  (2025-06-02T14:23:33Z)
• Design of AI-Powered Tool for Self-Regulation Support in Programming Education [4.171227316909729]
  Large Language Model (LLM) tools have demonstrated their potential to deliver high-quality assistance.<n>However, many of these tools operate independently from institutional Learning Management Systems.<n>This isolation limits the ability to leverage learning materials and exercise context for generating tailored, context-aware feedback.
  arXiv  Detail & Related papers  (2025-04-03T22:47:33Z)
• LLM Agents for Education: Advances and Applications [49.3663528354802]
  Large Language Model (LLM) agents have demonstrated remarkable capabilities in automating tasks and driving innovation across diverse educational applications.<n>This survey aims to provide a comprehensive technological overview of LLM agents for education, fostering further research and collaboration to enhance their impact for the greater good of learners and educators alike.
  arXiv  Detail & Related papers  (2025-03-14T11:53:44Z)
• ARCHED: A Human-Centered Framework for Transparent, Responsible, and Collaborative AI-Assisted Instructional Design [10.99360129432492]
  ARCHED is a framework that ensures human educators remain central in the design process while leveraging AI capabilities.<n>The framework integrates specialized AI agents - one generating diverse pedagogical options and another evaluating alignment with learning objectives.<n> Empirical evaluations demonstrate that ARCHED enhances instructional design quality while preserving educator oversight, marking a step forward in responsible AI integration in education.
  arXiv  Detail & Related papers  (2025-03-11T22:19:46Z)
• LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System [54.71619734800526]
  GenMentor is a multi-agent framework designed to deliver goal-oriented, personalized learning within ITS.<n>It maps learners' goals to required skills using a fine-tuned LLM trained on a custom goal-to-skill dataset.<n>GenMentor tailors learning content with an exploration-drafting-integration mechanism to align with individual learner needs.
  arXiv  Detail & Related papers  (2025-01-27T03:29:44Z)
• AI Agent for Education: von Neumann Multi-Agent System Framework [5.454637151538793]
  This paper centers on the multi-Agent system in education and proposes the von Neumann multi-Agent system framework.<n>It breaks down each AI Agent into four modules: control unit, logic unit, storage unit, and input-output devices.<n>It defines four types of operations: task deconstruction, self-reflection, memory processing, and tool invocation.
  arXiv  Detail & Related papers  (2024-12-30T16:58:17Z)
• Experiential Co-Learning of Software-Developing Agents [83.34027623428096]
  Large language models (LLMs) have brought significant changes to various domains, especially in software development. We introduce Experiential Co-Learning, a novel LLM-agent learning framework. Experiments demonstrate that the framework enables agents to tackle unseen software-developing tasks more effectively.
  arXiv  Detail & Related papers  (2023-12-28T13:50:42Z)
• Empowering Private Tutoring by Chaining Large Language Models [87.76985829144834]
  This work explores the development of a full-fledged intelligent tutoring system powered by state-of-the-art large language models (LLMs) The system is into three inter-connected core processes-interaction, reflection, and reaction. Each process is implemented by chaining LLM-powered tools along with dynamically updated memory modules.
  arXiv  Detail & Related papers  (2023-09-15T02:42:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.