Adaptive AI Tutoring in Education: Leveraging Large Language Models and Reinforcement Learning to Transform Personalized Learning at Scale

Abstract: Educational institutions face mounting pressure to deliver personalized learning experiences that sustain student engagement while accommodating diverse learning speeds and backgrounds. While generative AI chatbots have attracted considerable attention as tutoring tools, emerging evidence suggests that reactive question-answering alone may be insufficient to optimize learning outcomes. This article examines how tightly integrating large language model (LLM)-guided reinforcement learning with AI tutoring platforms can substantially improve educational outcomes. Drawing on a five-month randomized controlled trial involving 770 high school students across ten schools in Taipei, we demonstrate that adaptive problem sequencing—informed by rich behavioral signals from student-chatbot interactions and code-editing patterns—increased final exam performance by 0.15 standard deviations compared to fixed sequencing. Mediation analysis revealed that these gains operated primarily through sustained student engagement rather than increased practice volume or uniformly harder content. The findings suggest that organizations implementing AI-assisted learning systems should prioritize proactive guidance mechanisms alongside conversational interfaces, with particular attention to extracting actionable intelligence from learner-system interactions. This evidence-based approach offers a scalable framework for workforce development, digital literacy initiatives, and educational equity efforts.

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