This book provides a systematic exploration of large language model based multi-agent systems in education, offering researchers, educators, and technology developers actionable methodologies to advance personalized learning. Moving beyond the architectural limitations and hallucination risks of single language models, the text details how collaborative multi-agent frameworks can be effectively embedded into intelligent tutoring, adaptive recommendations, and simulated classroom environments. Grounded in foundational pedagogical theories including constructivism and the community of inquiry, the volume presents robust empirical cases across mathematics and science disciplines. It examines innovative paradigms such as multi-agent debate mechanisms, automated teaching optimization, and integrated cognitive and emotional support systems. Through detailed analyses of practical educational frameworks like SimClass and TeachTune, the authors demonstrate how inter-agent collaboration enhances student engagement, fosters divergent thinking, and improves overall academic performance. By bridging artificial intelligence architecture design with authentic instructional scenarios, this book delivers a comprehensive technical pathway for creating scalable and inclusive educational ecosystems. It serves as an essential resource for stakeholders seeking to optimize teaching quality, promote educational equity, and drive evidence-based technological innovation in modern learning environments.
Table of Contents:
Chapter 1. Conceptual Foundations and Educational Relevance.- Chapter 2. Fundamentals of LLM and Multi-Agent Systems.- Chapter 3. Application Scenarios of LLM-Powered Multi-Agent in Education.- Chapter 4. Case Studies and Effectiveness Evaluations.- Chapter 5. Technical Solutions and System Architecture in Education.- Chapter 6. Future Directions and Conclusion.
About the Author :
Zhi Liu is a Professor and Ph.D. Supervisor within the Laboratory for Artificial Intelligence and New Forms of Education and the Faculty of Artificial Intelligence in Education at Central China Normal University (CCNU). He has also maintained a long-term appointment as a Guest Researcher at Humboldt University of Berlin since 2017 and is currently a Visiting Scholar at the German Research Center for Artificial Intelligence (DFKI) for the 2025-2026 term. Specializing in educational LLMs, learning analytics, and intelligent tutoring systems, Prof. Liu pioneered multi-space integrated learning context awareness and personalized tutoring methods. Notably, this innovation has improved students' knowledge transfer levels in open scenarios by 0.96 standard deviations. He has published over 60 papers in top-tier journals, including Computers & Education, Internet and Higher Education, and IEEE Transactions on Learning Technologies, featuring seven ESI Highly Cited Papers (Top 1%). Furthermore, his scholarly impact is underscored by his recognition as a Top 1% Highly Cited Scholar by CNKI for two consecutive years (2024-2025). As a leading researcher, he serves as the Principal Investigator for the National Key R&D Program of China (2030 Major Projects) and several National Natural Science Foundation of China grants. He also holds key academic leadership roles, including Director of the Editorial Department for the national journal Educational Intelligence and Chair of the Organizing Committee for ICET. His distinguished contributions have earned him prestigious accolades, most notably First Prizes in the Hubei Social Science Outstanding Achievement Award (2025), the Science and Technology Progress Award of Hubei Province (2024), and the Hubei Province Teaching Achievement Award (2022).
