Large Language Model-based Multi-Agent Systems in Education
Frameworks, Applications, and Future Directions
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Beschreibung
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.
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.
Integrates LLM and multi-agent technologies with educational scenarios Discusses underexplored topics such as emotional support agents in learning and student data privacy protection Provides directions and guidance for long-term academic exploration and technical innovation
Autor*in
Zhi Liu
Themen in »Large Language Model-based Multi-Agent Systems in Education«
LLM-powered multi-agent systems in education Intelligent tutoring systems based on large language models Multi-agent collaboration for educational technology innovation LLM generalization ability in diverse educational scenarios AI-supported student smart communities Multi-agent systems for differentiated teaching Educational AI innovation in Asian contexts LLM applications in mathematics classroom teaching Emotional support agents for student learning Adaptive learning platforms with LLM integration