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.
@article{arxiv.2501.00083,
title = {AI Agent for Education: von Neumann Multi-Agent System Framework},
author = {Yuan-Hao Jiang and Ruijia Li and Yizhou Zhou and Changyong Qi and Hanglei Hu and Yuang Wei and Bo Jiang and Yonghe Wu},
journal= {arXiv preprint arXiv:2501.00083},
year = {2025}
}
Comments
Conference Proceedings of the 28th Global Chinese Conference on Computers in Education, GCCCE 2024