用双记忆机制让AI教师学生在模拟学校中自我进化，更真实还原教育互动。
Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics
- 基于双记忆架构的持续反思优化循环，实现智能体自主演化。
- 在多种校园场景中模拟出贴近真实的师生互动与学习过程。
- 适合教育仿真、智能教学系统研究者使用。
基于大语言模型的智能体在模拟复杂人类系统与互动中日益重要。本文提出AI-Agent School（AAS）系统，通过自演化机制模拟复杂的教育动态。针对教学过程建模碎片化及智能体在多样化教育参与者模拟中的局限性，AAS构建了零经验策略，采用持续的“体验-反思-优化”循环，依托包含经验与知识库的双记忆体系，并融合短期与长期记忆组件。在此机制下，智能体通过在多样化的模拟校园场景中进行情境化交互实现自主演化，更准确地建模真实学校中多维度、细微的师生互动与学习过程。实验表明，AAS能有效模拟复杂教育动态，促进智能体高级认知能力的发展，为从‘经验时代’迈向‘仿真时代’提供了基础支撑，生成高保真的行为与交互数据。
原文摘要 · Abstract (English)
Large language models (LLMs) based Agents are increasingly pivotal in simulating and understanding complex human systems and interactions. We propose the AI-Agent School (AAS) system, built around a self-evolving mechanism that leverages agents for simulating complex educational dynamics. Addressing the fragmented issues in teaching process modeling and the limitations of agents performance in simulating diverse educational participants, AAS constructs the Zero-Exp strategy, employs a continuous "experience-reflection-optimization" cycle, grounded in a dual memory base comprising experience and knowledge bases and incorporating short-term and long-term memory components. Through this mechanism, agents autonomously evolve via situated interactions within diverse simulated school scenarios. This evolution enables agents to more accurately model the nuanced, multi-faceted teacher-student engagements and underlying learning processes found in physical schools. Experiment confirms that AAS can effectively simulate intricate educational dynamics and is effective in fostering advanced agent cognitive abilities, providing a foundational stepping stone from the "Era of Experience" to the "Era of Simulation" by generating high-fidelity behavioral and interaction data.
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