mlsanityData to 7 Oct 2026

EduMirror: Modeling Educational Social Dynamics with Value-driven Multi-agent Simulation

arXiv 2606.07948 ↗First posted 6 Jun 2026cs.MA cs.CYcited by 0

On the map: LLM agents and tool use → Educational agents and math problem solving

Abstract (arXiv)

Understanding how educational social dynamics evolve is critical for informing effective educational policies and counterfactual interventions. However, traditional methods face a fundamental dilemma: observational studies often lack causal power, while controlled experiments are frequently constrained by ethical concerns. Although LLM-based multi-agent simulations offer a scalable in silico alternative, existing approaches remain limited by weak psychological grounding and insufficient measurement of latent psychological states. To address this, we introduce EduMirror, a multi-agent simulator for the scientific study of educational social dynamics. We provide configurable education-oriented agent forms, including value-driven agents grounded in psychological needs and social value orientation, together with a dual-track measurement protocol for quantifying observable behaviors and latent psychological states. We validate the realism and usability of EduMirror through case studies on school bullying and group cooperation, as well as broader evaluations across diverse educational scenarios. The results show that EduMirror generates educational social dynamics that are realistic, theory-consistent, and measurable by empirical criteria. These properties enable structured in silico educational research, providing a computational tool for hypothesis testing and counterfactual intervention analysis in educational science. Project page: https://edumirror.net.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Educational agents and math problem solving, one of 16 directions in LLM agents and tool use. Builds on 4 of its 49 references that are in the map. First posted on arXiv 6 Jun 2026.

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4 of 49 references.

References · 4

  1. 2024LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individualscited by 235
  2. 2025AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Societycited by 180
  3. 2023Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordiacited by 119
  4. 2025YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Modelscited by 11

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  5. 2024EduAgent: Generative Student Agents in Learning
  6. 2024Sense and Sensitivity: Evaluating the simulation of social dynamics via Large Language Models
  7. 2025Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics
  8. 2026Edu-Theater: A Data-Efficient Agent Framework for Scalable Learner Behavior Simulation through Staging Roll-Call