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    <abstract>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.</abstract>
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%0 Conference Proceedings
%T Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics
%A Jin, Sheng
%A Wang, Haoming
%A Gao, Zhiqi
%A Yang, Yongbo
%A Chunjia, Bao
%A Wang, Chengliang
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F jin-etal-2025-evolution
%X 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.
%R 10.18653/v1/2025.findings-emnlp.312
%U https://aclanthology.org/2025.findings-emnlp.312/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.312
%P 5843-5857

Markdown (Informal)

[Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics](https://aclanthology.org/2025.findings-emnlp.312/) (Jin et al., Findings 2025)

• Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics (Jin et al., Findings 2025)

ACL

• Sheng Jin, Haoming Wang, Zhiqi Gao, Yongbo Yang, Bao Chunjia, and Chengliang Wang. 2025. Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 5843–5857, Suzhou, China. Association for Computational Linguistics.