Large Language Model-based Multi-Agent Systems in Education

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.