Federated Explainable Multi-Agent Governance Framework for Adaptive Personalized Learning Equity in Higher Education

Abstract

The rapid deployment of artificial intelligence (AI) in higher education raises questions of equity, transparency, accountability, privacy, and governance in adaptive personalized learning. While AI-enabled personalization can increase educational responsiveness, top-down personalized learning architectures may reinforce inequality through decentralized institutional data representation, esoteric decision-making, and insufficient stakeholder agency. Prior work has advanced federated learning, multi-agent systems, explainable AI (XAI), and algorithmic fairness, yet such components have not been fully integrated into a governance architecture for equitable higher education. This paper proposes an adaptive personalized learning federated explainable multi-agent governance architecture for equitable higher education. A theory-building research methodology leveraging critical realism and abductive inference combines concepts in distributed machine learning, multi-agent coordination, XAI, educational equity, and AI governance. The architecture consists of four interacting layers: federated learning for distributed institutional data; multi-agent governance comprising student, instructor, policy, and optimizer agents; explainability for decision traceability and interpretation; and equity evaluation to inform ongoing bias mitigation. Theoretical triangulation, construct-to-function mapping, cross-domain consistency testing, and boundary-condition analysis serve as the analytical validation process. Privacy, distributed governance, XAI, and equity are systemically positioned as mutually constraining properties. The architecture can inform design principles and empirical evaluation for agency-aware, privacy-preserving, equitable institutional AI governance.