Self-Evolving Generative AI Tutors: Reinforcement Learning-Augmented ITS for Personalized, Proactive, and Context-Aware Student Engagement

This study presents a reinforcement learning-driven multi-agent AI tutor that advances beyond traditional intelligent tutoring systems (ITS) by integrating adaptive intervention selection, real-time engagement tracking, and multi-agent feedback refinement. Unlike rule-based ITS and static LLM-generated responses, our system dynamically adjusts instructional strategies based on student behavior, incorporating reinforcement learning (RL) for intervention optimization, neural knowledge tracing (NKT) for misconception prediction, and an engagement prediction model (EPM) to sustain student participation. Additionally, a multi-agent debate mechanism refines AI-generated explanations, enhancing clarity and pedagogical alignment. Experimental validation demonstrates that our approach improves intervention adaptability by 28.6%, reduces recurring student errors by 31.2%, and lowers dropout rates by 24.8%, surpassing existing ITS and static AI tutors. These findings indicate that reinforcement learning and multi-agent collaboration enable AI tutors to provide more responsive, personalized, and effective learning support. Beyond technical improvements, this work contributes to scalable, real-world AI tutoring solutions that align with established learning science principles. Future research should explore deployment in diverse educational settings and further refinements in balancing personalization with instructional equity.