EDU-MATRIX: A Society-Centric Generative Cognitive Digital Twin Architecture for Secondary Education

2026-02-20

AI Summary

This work addresses the limitations of traditional agent-centric multi-agent simulations, where hard-coded rules lead to rigid social dynamics and hinder alignment with educational values. To overcome this, the authors propose a generative cognitive digital twin framework centered on social space, featuring an innovative social microkernel-based environmental context injection mechanism, modular logic evolution protocols, and a role-topology-driven endogenous alignment approach. These components collectively enable dynamic guidance of agent behaviors and knowledge evolution through a “social gravitational field.” Evaluated in a secondary school digital twin system with 2,400 agents, the framework achieves 94.1% dialogue consistency and a social clustering coefficient of 0.72, demonstrating the efficacy of synergistically coupling social gravity with cognitive fluidity to drive value alignment.

Technology Category

• Multiagent Systems: Agent-Based Simulation and Emergent Behavior
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Humans and AI: Learning Human Values and Preferences

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Existing multi-agent simulations often suffer from the "Agent-Centric Paradox": rules are hard-coded into individual agents, making complex social dynamics rigid and difficult to align with educational values. This paper presents EDU-MATRIX, a society-centric generative cognitive digital twin architecture that shifts the paradigm from simulating "people" to simulating a "social space with a gravitational field." We introduce three architectural contributions: (1) An Environment Context Injection Engine (ECIE), which acts as a "social microkernel," dynamically injecting institutional rules (Gravity) into agents based on their spatial-temporal coordinates; (2) A Modular Logic Evolution Protocol (MLEP), where knowledge exists as "fluid" capsules that agents synthesize to generate new paradigms, ensuring high dialogue consistency (94.1%); and (3) Endogenous Alignment via Role-Topology, where safety constraints emerge from the agent's position in the social graph rather than external filters. Deployed as a digital twin of a secondary school with 2,400 agents, the system demonstrates how "social gravity" (rules) and "cognitive fluids" (knowledge) interact to produce emergent, value-aligned behaviors (Social Clustering Coefficient: 0.72).

Problem

• Agent-Centric Paradox
• social dynamics
• educational values
• multi-agent simulation
• value alignment

Innovation

• Generative Cognitive Digital Twin
• Society-Centric Architecture
• Environment Context Injection Engine
• Modular Logic Evolution Protocol
• Endogenous Alignment