EduVerse: Educational Simulation Platform

• EduVerse is a simulation platform that models real classroom dynamics using a modular Cognition-Interaction-Evolution (CIE) architecture with extensive customization.
• The platform integrates human-in-the-loop participation with virtual agents to enable dynamic, hybrid educational scenarios and robust longitudinal behavioral analytics.
• EduVerse validates simulation fidelity through empirical metrics like IRF ratios and network density, demonstrating high pedagogical realism and scalability.

EduVerse refers to a comprehensive, multi-agent and user-configurable simulation platform for educational scenarios, designed to systematically reproduce and paper the complexity of real classroom dynamics—including open-ended cognition, dynamic social interaction, affective states, and longitudinal development. It is built on a modular Cognition-Interaction-Evolution (CIE) architecture with extensive environment, agent, and session customization, and supports seamless “human-in-the-loop” integration, enabling both virtual agents and real human users to participate in shared educational simulations. Developed to overcome the limitations of single-agent or short-term educational AI models, EduVerse offers validated pedagogical realism, group interaction metrics, and longitudinal behavioral analytics, targeting large-scale, reproducible, and interpretable research in educational technology and classroom AI (Ma et al., 7 Oct 2025).

1. CIE Architecture: Cognition, Interaction, and Evolution

The core of EduVerse is its three-layered CIE architecture, which decomposes classroom processes into cognitively rich agent behavior, authentic interaction protocols, and evolving multi-session trajectories. Each agent operates under a Perception–Cognition–Action (PCA) loop:

• Cognition Layer: Every agent, parameterized by a personality vector $e_i$ (encoding traits, cognitive style, and motivation), observes the environment ($O_i^t$) and selects an action $a_i^t$ via the mapping $\mathcal{A}_i^t: (O_i^t, e_i) \rightarrow a_i^t$. Additionally, role-specific "gate" mechanisms ensure that agents alternate between teacher- and peer-directed discourse and execute metacognitive cycles such as planning, monitoring, and regulation.
• Interaction Layer: Classroom dialogue is structured according to an expanded Initiation–Response–Feedback-Regulation (IRF-R) protocol: teacher initiates, student(s) respond, teacher gives feedback, and students (or agents) perform metacognitive regulation. Parameterization of agent protocols ensures that instructional stability is maintained for teachers, while students display individualized variability conditioned by their profile vectors.
• Evolution Layer: Cross-session learning and behavioral change are governed by time-aggregated updates to knowledge, emotion, and behavior states. Agents' short-term state memories are consolidated into long-term memory, and positive transitions—indexed by the positive transition rate $R^+$—are explicitly logged to track cognitive, behavioral, and affective progression across multiple sessions.

2. Agent, Environment, and Session Customization

EduVerse supports granular customization along three orthogonal axes:

• Environment: Physical and social configurations are modeled by a spatial hierarchy (sectors, arenas, objects, items), enabling the replication of distinct classroom layouts (e.g., Lecture, Round Table, Collaborative) via adjacency matrices ($A^{\text{seat}}$) parameterized by physical distance, group membership, and domain-specific interaction constraints.
• Agent: Both teacher and student agents are instantiable with customized parameters, including knowledge state, willingness, personality, and cognitive profile. Human users can participate directly via the human-in-the-loop interface, enabling hybrid real/virtual action, peer collaboration, and teacher facilitation. Agents’ behavioral policies incorporate metacognitive gating and respond to real-time user actions in shared sessions.
• Session: The simulation framework supports multi-session experiments, with environment and agent states persisted and updated across lessons. This enables the paper of collective learning trajectories and adaptation over extended timeframes.

3. Pedagogical Realism and Validation

EduVerse's modeling fidelity and validity are empirically substantiated:

• Instructional Alignment: Simulation of middle school Chinese classes yields IRF rates (Initiation–Response–Feedback event ratios) in the range $0.28{-}0.64$, directly matching observed rates in authentic classrooms ($0.37{-}0.49$). This close alignment demonstrates that the multi-agent dialog protocols and role-parameterized behavior capture the cadence and structure of real instructional practice.
• Group Interaction and Role Differentiation: Social network analysis of simulated classrooms reveals network density values between $0.27{-}0.40$, meaning that roughly one-third of potential peer communication links are realized. Centrality measures show that simulated agents consistently differentiate into roles (e.g., initiators, responders, mediators), reflecting effects of individual extraversion and willingness, while maintaining overall instructional stability.
• Cross-Session Evolution: Positive transition rate $R^+$ (ratio of upward moves in behavior, emotion, cognition) increases by $11.7\%$ on average over multiple sessions, demonstrating that agent learning trajectories exhibit structured, incremental improvement. The formalism:

$$R^+ = \frac{\sum_i \mathbb{1}[P(s_{t+1}) > P(s_t)]} {\sum_i \mathbb{1}[P(s_{t+1}) \ne P(s_t)]}$$

quantifies advancement in cognitive and affective state, permitting fine-grained tracking of group and individual progress.

4. Human-in-the-Loop and Integration

A defining feature of EduVerse is the human-in-the-loop interface, which enables direct real-user participation alongside virtual agents in the simulation:

• Mechanisms: Real students and teachers may join ongoing sessions, interact with NPC agents, and participate in group discourse or decision making. These interactions are harmonized with the PCA control loops, allowing synchronous and asynchronous integration without information leakage or state inconsistency.
• Significance: This hybrid agent–human substrate makes EduVerse a uniquely powerful testbed for training, evaluation of educational interventions, pedagogical research, and cross-modal classroom studies.

5. Cross-Task Reuse, Scalability, and Open-Source Initiative

EduVerse's modular design and commitment to openness facilitate broad research and application:

• Cross-Task Reuse: By supporting user-defined environment, agent, and session parameters, the platform allows researchers to instantiate and paper a variety of educational tasks, genres (including lyrical prose, argumentative essay, fable), and classroom layouts without rebuilding the system.
• Scalability: The agent, environment, and session modules are decoupled, enabling massive simulation experiments at scale and facilitating the accumulation of large, reproducible datasets for AI, cognitive science, and educational modeling.
• Open-Source: The release of codebase, environment configurations, pre-processing scripts, and trained models will support reproducibility, extensibility, and cross-disciplinary collaboration by the wider research community.

6. Broader Impact on Educational AI and Research

By balancing realism, reproducibility, and interpretability, EduVerse constitutes a new class of simulation space for education research:

• Experimental “Sandbox”: The system provides a controlled yet authentic simulation environment for exploring the interplay of instructional design, group dynamics, cognitive progression, and emotional development.
• Applications: Use cases include teacher training, optimization and evaluation of AI-driven educational interventions, social-emotional learning studies, classroom management research, and simulation of long-term learning effects.
• Interpretability: The layered CIE framework and explicit network metrics provide interpretable, quantitative measures of pedagogical realism, social connectedness, and adaptive growth, directly linking theoretical models to observable outcomes in simulated or hybrid real-classroom settings.

In summary, EduVerse establishes a foundational platform for rigorous, scalable, and human-integrated paper of complex educational scenarios, setting a new standard for simulation-based educational AI research (Ma et al., 7 Oct 2025).