# AI Agent for Education: von Neumann Multi-Agent System Framework

arXiv: https://arxiv.org/abs/2501.00083 · HTML: https://mlsanity.com/paper/2501.00083/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use.
- Builds on 21 of its 35 references that are in the map.
- First posted on arXiv 30 Dec 2024.

## Placed in

- LLM agents and tool use / [Educational agents and math problem solving](https://mlsanity.com/agents/educational-agents-and-math-problem-solving/)

## Builds on (most cited first, 21 of 21)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [Graph of Thoughts: Solving Elaborate Problems with Large Language Models](https://mlsanity.com/paper/2308.09687/) (2023)
- [Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2307.15217/) (2023)
- [LLM+P: Empowering Large Language Models with Optimal Planning Proficiency](https://mlsanity.com/paper/2304.11477/) (2023)
- [TALM: Tool Augmented Language Models](https://mlsanity.com/paper/2205.12255/) (2022)
- [Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems?](https://mlsanity.com/paper/2309.15943/) (2023)
- [EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education](https://mlsanity.com/paper/2308.02773/) (2023)
- [Generative Adversarial User Model for Reinforcement Learning Based Recommendation System](https://mlsanity.com/paper/1812.10613/) (2018)
- [CGMI: Configurable General Multi-Agent Interaction Framework](https://mlsanity.com/paper/2308.12503/) (2023)
- [InterAct: Exploring the Potentials of ChatGPT as a Cooperative Agent](https://mlsanity.com/paper/2308.01552/) (2023)
- [Towards Goal-oriented Intelligent Tutoring Systems in Online Education](https://mlsanity.com/paper/2312.10053/) (2023)

## Built on by (most cited first, 12 of 12)

- [A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models](https://mlsanity.com/paper/2503.23350/) (2025)
- [When Your AI Agent Succumbs to Peer-Pressure: Studying Opinion-Change Dynamics of LLMs](https://mlsanity.com/paper/2510.19107/) (2025)
- [A Survey on Agent Workflow -- Status and Future](https://mlsanity.com/paper/2508.01186/) (2025)
- [A Comprehensive Review of AI Agents: Transforming Possibilities in Technology and Beyond](https://mlsanity.com/paper/2508.11957/) (2025)
- [MAS-KCL: Knowledge component graph structure learning with large language model-based agentic workflow](https://mlsanity.com/paper/2505.14126/) (2025)
- [Agentic Workflow for Education: Concepts and Applications](https://mlsanity.com/paper/2509.01517/) (2025)
- [A cybersecurity AI agent selection and decision support framework](https://mlsanity.com/paper/2510.01751/) (2025)
- [ExpertAgent: Enhancing Personalized Education through Dynamic Planning and Retrieval-Augmented Long-Chain Reasoning](https://mlsanity.com/paper/2510.07456/) (2025)
- [Single-agent vs. Multi-agents for Automated Video Analysis of On-Screen Collaborative Learning Behaviors](https://mlsanity.com/paper/2604.03631/) (2026)
- [Thinking in Graphs with CoMAP: A Shared Visual Workspace for Designing Project-Based Learning](https://mlsanity.com/paper/2604.06200/) (2026)
- [Persona-Based Requirements Engineering for Explainable Multi-Agent Educational Systems: A Scenario Simulator for Clinical Reasoning Training](https://mlsanity.com/paper/2604.17186/) (2026)
- [Agentic AI Ecosystems in Higher Education: A Perspective on AI Agents to Emerging Inclusive, Agentic Multi-Agent AI Framework for Learning, Teaching and Institutional Intelligence](https://mlsanity.com/paper/2605.14266/) (2026)

## Abstract (arXiv)

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.