Exploring the Role of Multi-Agent Reinforcement Learning in Generative AI-Based Educational Systems

Abstract

In recent years, Generative Artificial Intelligence has experienced rapid advancements, particularly following the emergence of ChatGPT, which has significantly impacted various domains, including education. As Generative AI tools become more integrated into learning environments, there is a growing need to ensure that their outputs demonstrate pedagogical alignment, contextual relevance, and quality control. This paper proposes a multi-agent framework that enhances the educational quality of Generative AI outputs through collaborative evaluation and adaptive response mechanisms. By incorporating a layered decision-making process, the framework aims to improve the clarity, relevance, and instructional value of AI-generated answers. We outline a conceptual design and discuss how this approach contributes to the development of more reliable, personalized, and pedagogically effective AI-powered learning systems.

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