Toward Collaborative and Adaptive Learning: A Survey of Multi-Agent Reinforcement Learning in Education

In recent years, researchers have leveraged single-agent reinforcement learning to boost educational outcomes and deliver personalized interventions; yet this paradigm provides no capacity for inter-agent interaction. Multi-agent reinforcement learning (MARL) overcomes this limitation by allowing several agents to learn simultaneously within a shared environment, each choosing actions that maximize its own or the group's rewards. By explicitly modeling and exploiting agent-to-agent dynamics, MARL can align those interactions with pedagogical goals such as peer tutoring, collaborative problem-solving, or gamified competition, thus opening richer avenues for adaptive and socially informed learning experiences. This survey investigates the impact of MARL on educational outcomes by examining evidence of its effectiveness in enhancing learner performance, engagement, equity, and reducing teacher workload compared to single agent or traditional approaches. It explores the educational domains and pedagogical problems addressed by MARL, identifies the algorithmic families used, and analyzes their influence on learning. The review also assesses experimental settings and evaluation metrics to determine ecological validity, and outlines current challenges and future research directions in applying MARL to education.