- 期刊
大模型驱动的教育多智能体系统应用研究－技术架构、发展现状、实践路径与未来展望
Research on Educational Multi-Agent Systems Empowered by Large Language Models-Technical Architecture, Current Status, Practical Pathways, and Future Prospects
摘要
随着生成式大模型技术的快速发展，大模型驱动的单智能体在教育领域已获得广泛应用。然而，在处理需要多角色协作的教育任务时，如教育资源开发、模拟协作学习等复杂场景，单智能体仍面临认知视角单一和角色定位固定等局限，难以满足教育领域深层次的发展需求。本研究系统探讨了大模型驱动的教育多智能体系统的应用现状与发展前景。首先，构建了教育多智能体的多层次应用技术架构体系，为相关研究奠定理论基础；其次，从教学辅助、学习支持、教育评价和教育研究四个维度，深入分析了教育多智能体系统的应用现状与典型案例；再次，从应用机制、技术实现和教育效果三个层面，系统阐释了多智能体系统赋能教育的内在机理，并提出“机制设计－系统建模－效果评估”的闭环实践路径；最后，探讨了教育多智能体系统的未来发展趋势、面临的挑战及其应对策略。研究成果对推动多智能体技术与教育深度融合、促进智能教育创新发展具有重要的理论指导意义与实践价值。
並列摘要
The rapid advancement of generative large language models (LLMs) has facilitated the widespread application of LLM-powered single-agent systems in educational contexts. However, when addressing educational tasks that require multi-role collaboration-such as the development of educational resources and simulated collaborative learning in complex environments-single-agent systems face significant limitations, Including narrow cognitive perspectives and fixed role definitions. These limitations impede their ability to meet the evolving demands of education. This study systematically examines the current applications and future prospects of LLM-powered educational multi-agent systems. First, it establishes a multi-layered technical architecture for educational multi-agent systems, providing a theoretical foundation for related research. Second, the study conducts an in-depth analysis of the current state and typical case studies of educational multi-agent systems across four dimensions: teaching assistance, learning support, educational assessment, and educational research. Third, it systematically explores the internal mechanisms through which multi-agent systems empower education, addressing three key aspects: application mechanisms, technical implementation, and educational effectiveness. The study proposes a closed-loop practical framework that encompasses "mechanism design-system modeling-effectiveness evaluation." Finally, the research discusses the future development trends, challenges, and strategies for addressing these challenges in the context of educational multi-agent systems. The findings offer valuable theoretical insights and practical guidance for promoting the deep integration of multi-agent technology with education and advancing innovative developments in intelligent education.