Evolution in Simulation: AI -Agent School with Dual Memory for High-Fidelity Educational Dynamics (2025)

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.

## introduction large language models (llms) have demonstrated exceptional performance across a variety of tasks @xcite , such as code instruction, information retrieval, and complex problem-solving. as the capabilities of llmbased agents continue to advance, researchers have begun exploring the simulation of human behaviors to construct complex systems based on realworld scenarios @xcite . such simulations are instrumental in understanding human decision-making processes, developing novel human-computer interaction systems, and driving societal model transformations @xcite . among these domains, education is particularly eager to leverage agents to achieve adaptive learning and optimize teaching models @xcite . existing research has already developed educational agents for tasks such as mathematical formula conversion @xcite and classroom interaction simulation @xcite . however, some scholars have pointed out the limitations of current agents in the education field: first, there is a lack of systematic modeling of the teaching process, and second, llm agents struggle to accurately simulate the behaviors and interactions of diverse participants in educational settings. in view of this, our goal is to enhance the realism and research value of agent simulations within educational settings by facilitating complex, multi-participant interactions. we propose the ai-agent school (aas), a multi-agent system capable of simulating multidimensional dynamic educational scenarios. central to aas is our proposed zero-exp strategy, which establishes a dual memory base for storing experience and knowledge. this strategy effectively divides both the experience and knowledge repositories into short-term and long-term parts. within the aas environment, multi-role agents iteratively update these memory bases through preset behaviors and interaction data, achieving autonomous evolution via the core "experiencereflection-optimization" mechanism. experimental results demonstrate that aas successfully simulates multidimensional dynamic learning scenarios, the autonomous evolution of multi-agents within aas enables the high-fidelity simulation of the complex performance and interactions of diverse roles in realistic educational scenarios. our frame-work provides a verifiable technical model and theoretical pathway for the development of educational digital twins and the production of valuable educational interaction data. the contributions of our work are as follows: first, we proposed aas, a multi-agent educational scenario simulation system. it is capable of capturing teacher-student relationships, peer interactions, and environmental influences, enabling the simulation of real teaching processes. compared to existing methods, aas demonstrates advantages in handling multiple roles, multi-variable dynamics, and temporal evolution. second, we designed the zero-exp mechanism to address the challenges of data scarcity and role behavior consistency in educational simulations. this mechanism guides agents to evolve from a zero-experience state to expert-level behavior using a small set of initialization parameters. experiments show that zero-exp enables agents to generate interaction patterns consistent with real educational scenarios. third, this research pioneers a new paradigm of "computational education science", deeply integrating traditional educational research with ai technologies. it lays the theoretical and technical foundation for next-generation educational systems, teacher training platforms, and educational policy simulation tools, propelling the education field from the "era of experience" to the "era of simulation". ## related work the concept of educational intelligent agents originates from @xcite principle of programmed instruction and the programmed teaching machines he designed. the intelligent tutoring systems developed in the 1970s were early iterations of this idea. according to @xcite , a pioneer in the field, an educational agent can be defined as a virtual tutoring role within a learning system that is responsible for dynamically sensing the learning environment, analyzing and inferring learner information, and actively or passively performing assistive actions based on needs. although early educational intelligent agents did not gain widespread attention due to technological limitations, by the early 21st century, scholars began to develop educational intelligent agents with practical value @xcite . empirical studies have shown that educational agents can promote deep learning @xcite and enhance motivation @xcite . however, at that time, agent systems struggled to achieve natural, human-like interaction @xcite , with a primary focus on promoting cognitive processes. with the development of ai technologies, research on educational intelligent agents has entered a new phase. ai agents have given educational intelligent agents "living souls," enabling them to participate more vividly in educational practices @xcite . researchers have also shown great interest in defining the teaching roles of agents, exploring various roles such as tutors, assistants, learning partners, collaborators, competitors, and even troublemakers @xcite @xcite . through largescale data training, ai agents have become more embodied and capable of performing interactive roles such as collaboration, encouragement, and guidance in complex teaching activities @xcite . at the same time, ai agents have developed a certain level of emotional empathy, even alleviating the marginalization experiences of some learners in traditional classrooms, providing a more engaging learning experience @xcite @xcite . in recent years, the rapid development of llm has further advanced research on educational ai agents @xcite . empirical cases have already demonstrated the enormous potential of llm-supported educational ai agents. for example, lan and chen constructed a teaching ai agent based on llm and applied it to teaching sequence words (or ordered words), achieving promising results @xcite . other scholars have introduced llm-supported ai agents in areas such as programming education and foundational ai knowledge teaching @xcite , creating new learning logic and models in the classroom. however, the interaction of a single ai agent is limited, making it difficult to fully realize the potential of llm. the ai agent town developed by the stanford team has validated this point @xcite . in light of this, this study will build upon the logic of the ai agent town and agent hospital @xcite to construct aas (agent-based learning simulation) in order to simulate and predict various teaching and learning processes in schools, creating a corresponding knowledge base of teaching experience. this not only has numerous benefits for teacher training and iteration in the education field, but it could also have a profound impact on other industries that require rapid accumulation of experience. ## conclusion we introduces the aas, a multi-agent simulation environment designed to model and accelerate the evolution of educational cognitive processes through situated interactions. addressing lack of systematic teaching process modeling and challenges in simulating diverse participant behaviors, we proposed the zero-exp mechanism. central to zero-exp is a dual memory base, distinguishing between episodic experience and structured knowledge, organized hierarchically into shortterm and long-term components. this architecture facilitates a continuous "experience-reflectionoptimization" cycle, enabling agents to evolve autonomously based on their interactions within the simulated school environment. our comprehensive experimental evaluation, involving nine different memory configurations, validates the effectiveness of the zero-exp mechanism. both automated rouge-l scores and expert human evaluation demonstrate that the full memory model (id 1) significantly outperforms baselines lacking the dual structure or the st/lt hierarchy. the results indicate that the proposed memory system is crucial for enabling agents to generate more realistic appropriate behaviors, progressively aligning with expert-curated ground truth data over time. the aas environment and zero-exp mechanism represent a significant step towards creating highfidelity digital twins of educational settings and generating valuable behavioral data @xcite . this work provides a verifiable technical model and theoretical pathway for future research in educational ai, agent-based simulation, and the broader pursuit of experience-driven artificial intelligence, contributing foundational elements towards realizing the potential of the "era of experience" in educational and potentially other complex human-centric domains.