Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics
This work addresses the challenge of high-fidelity simulation of educational processes for researchers and developers in AI and education, though it appears incremental as it builds on existing agent-based simulation methods.
The paper tackles the problem of simulating complex educational dynamics by proposing the AI-Agent School system, which uses a self-evolving mechanism with dual memory to model teacher-student interactions, and experiments confirm it effectively simulates these dynamics and fosters advanced agent cognitive abilities.
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.