The International Conference on Educational Large Language Models and Multi‑Agent Systems (ELLM-MAS 2027) will be held in Musashino University Ariake Campus, Tokyo, Japan from March 19 to 21, 2027, under the central theme of Innovation and Application of LLM and Multi-Agent Systems in Digital Education.
Amid the global digital transformation of education, educational large language models and multi-agent collaborative intelligence have emerged as pivotal technologies driving the modernization and intelligent advancement of educational systems worldwide. ELLM-MAS 2027 provides a premier international academic forum that brings together leading scholars, researchers, educators, and industry practitioners to disseminate groundbreaking theoretical discoveries, cutting-edge technological innovations, and interdisciplinary practical applications in the fields of intelligent learning, AI-enhanced pedagogy, and educational digitalization.
Featuring distinguished keynote addresses, peer-reviewed paper presentations, and focused academic workshops, the conference upholds rigorous academic standards, fosters cross-disciplinary and international collaboration, addresses critical technical and theoretical challenges in smart education, and cultivates global research partnerships to promote the high-quality and sustainable development of future-ready educational intelligence ecosystems.
Following peer review, accepted papers with completed registration and presentation will be included in the Conference Proceedings, indexed by Ei Compendex and Scopus.
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Topics of interest include, but are not limited to:
Vector database construction for textbooks and course resources
Document chunking and reranking optimization for accurate RAG retrieval
Knowledge point retrieval and intelligent Q&A system development
Intelligent generation of experimental procedures and exercise analysis
Multi-role agent interaction framework for teachers, students and assistants
Collaborative learning grouping mechanism based on multi-agent scheduling
Dynamic adaptive task allocation for agent clusters
Engineering implementation of human-AI collaborative teaching systems
LLM-driven adaptive learning pathways and dynamic knowledge mapping
Cognitive diagnosis and precision intervention for learning difficulties
Multi-dimensional learner profiling and individualized instruction design
Affective computing and emotional state-aware adaptive tutoring
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