构建教育用智能体框架，让AI像人一样思考与协作。
AI Agent for Education: von Neumann Multi-Agent System Framework
- 将AI智能体拆分为控制、逻辑、存储和输入输出四模块，实现分步决策。
- 通过自我反思与多智能体辩论，提升学习效果与知识构建能力。
- 适合教育AI研发者与智能教学系统设计者参考使用。
大型语言模型的发展为教育带来了新范式。本文聚焦教育领域的多智能体系统，提出冯·诺伊曼多智能体系统框架。该框架将每个AI智能体分解为控制单元、逻辑单元、存储单元和输入输出设备四个模块，并定义了任务分解、自我反思、记忆处理和工具调用四种操作类型。同时引入链式思维（Chain-of-Thought）、Reson+Act及多智能体辩论等关联技术。论文还探讨了教育多智能体系统的能力建设循环，包括人类学习者参与的外部循环以促进知识建构，以及基于大模型智能体的内部循环以增强群体智能。通过协作与反思，该系统可更好地支持学习者学习，并在此过程中提升其教学能力。
原文摘要 · Abstract (English)
The development of large language models has ushered in new paradigms for education. This paper centers on the multi-Agent system in education and proposes the von Neumann multi-Agent system framework. It breaks down each AI Agent into four modules: control unit, logic unit, storage unit, and input-output devices, defining four types of operations: task deconstruction, self-reflection, memory processing, and tool invocation. Furthermore, it introduces related technologies such as Chain-of-Thought, Reson+Act, and Multi-Agent Debate associated with these four types of operations. The paper also discusses the ability enhancement cycle of a multi-Agent system for education, including the outer circulation for human learners to promote knowledge construction and the inner circulation for LLM-based-Agents to enhance swarm intelligence. Through collaboration and reflection, the multi-Agent system can better facilitate human learners' learning and enhance their teaching abilities in this process.
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