Designing a Multi-Agent Personalized Learning Support System Grounded in Mastery Learning Theory

Abstract

Generative artificial intelligence offers new ways to support personalized learning, yet many existing applications emphasize content generation without fully connecting learning evidence, instructional intervention, and subsequent evaluation. Adopting a design and development research approach, this study designs a multi-agent personalized learning support system with mastery learning theory as its instructional framework. Within the bounded context of a learning space, specialized agents share data and coordinate tasks to diagnose mastery from formative assessment evidence, generate corrective or enrichment support, and prepare a second parallel formative assessment after teacher review. This process creates an interpretable and traceable closed loop of personalized learning support. The study further derives five design principles and develops a learning-evidence model, a multi-agent collaboration architecture, personalized learning packages, and a staged evaluation framework. Its principal contribution is the translation of mastery learning into an executable and auditable multi-agent workflow, offering design guidance for personalized learning support systems. The proposed design and prototype establish feasibility claims only; their educational effectiveness remains to be examined empirically.