Designing GenAI Tools for Personalized Learning Implementation: Theoretical Analysis and Prototype of a Multi-Agent System

Educator preparation, personalized learning (PL) implementation, and applications of Generative AI converge as three interrelated systems that, when carefully designed, can help achieve the long-sought goal of providing inclusive education for all learners. However, realizing this potential comes with challenges resulting from theoretical complexities and technological constraints. This article provides a theoretical analysis of the complex interconnectedness among these systems guided by the Cultural-Historical Activity Theory (CHAT). Building on the analysis, we introduce CoPL, a multi-agent system consisting of multiple agents with distinct functions that facilitate the complex PL design and engage pre-service teachers (PSTs) in dynamic conversations while prompting them to reflect on the inclusivity of agent-generated instructional suggestions. We describe the affordances and limitations of the system as a professional learning tool for PSTs to develop competencies for designing inclusive PL to meet diverse learning needs of all learners. Finally, we discuss future research on refining CoPL and its practical applications.