大语言模型支持的多智能体:技术路径、教育应用与未来展望

Multi-Agent Systems Supported by Large Language Models:Technical Pathways,Educational Applications,and Future Prospects

面对复杂多变的教育环境,多智能体系统以其协作、分布式和自适应的优势成为解决教育难题的新途径。本研究分析了多智能体系统的基本原理和演化历程,聚焦其在教育领域的源点,并从领域、结构和场景三个角度,探讨多智能体系统教育应用的形态演化。在技术层面,本研究探讨了生成式人工智能技术如何赋能多智能体系统,构建了基于大模型的智能体“眼—脑—手”三维能力框架,并提出了多智能体系统智能性提升的内外双循环框架。在教育应用方面,本研究阐述了多智能体系统教育应用的多重角色,包括促进知识管理的百科全书型智能体、促进协作交互的智能学伴型智能体、促进学习规划的教学助手型智能体和促进学科教学的专业教师型智能体等,探讨了多智能体系统在教育应用与跨文化体系适应中的潜力。多智能体系统的教育应用也面临诸多挑战,包括如何确保系统稳定性和安全性、如何避免负面影响、如何实现与传统教育方法优势互补等。针对这些挑战,本研究提出融合教学要素、对接数字基座、变革教育范式、加强安全伦理隐私保护等对策,以期重塑多智能体教育生态,引领其稳健前行。本研究可为促进智能化教育技术的发展提供新思路,为推动教育数字化和教育高质量发展贡献新力量。

The rapid advancement of technology,particularly generative artificial intelligence(GenAI),is facilitating the education transformation.In response to the increasingly complex and dynamic educational landscape,Multi-Agent Systems(MAS)have emerged as a promising solution to address educational challenges due to their collaborative,distributed,and adaptive capabilities.This study begins by analyzing the core principles and evolutionary trajectory of MASs,focusing on their early applications in education.It explores the evolution of these systems from three critical perspectives:Domain,structure,and application scenarios.On the technical front,the study delves into how GenAI enhances MASs by developing an"eye-brain-hand"capability framework using Large Language Models(LLM).Additionally,it introduces a dual-cycle framework to boost the intelligence of these systems.Regarding applications,the study provides an in-depth analysis of the diverse roles of MASs in education,including an encyclopedia-type Agent that facilitates knowledge management,an intelligent learning companion that fosters collaboration,a teaching assistant Agent that aids in learning planning,and a specialized teacher agent that supports subject-specific instruction.The study also highlights the potential of MASs for various educational contexts and cross-cultural environments.However,the implementation of MASs in education faces several challenges,such as ensuring system stability and security,mitigating potential negative impacts,and integrating the strengths of traditional educational methods.To address these issues,the study proposes a range of strategies,including integrating educational elements,aligning with digital infrastructure,transforming educational paradigms,and enhancing security,ethics,and privacy safeguards.These measures reshape the educational ecosystem empowered by MASs and ensure its sustainable development.The study offers meaningful insights into intelligent educational technologies and contributes to the digitalization and high-quality development of education.