This research examines the evolution of adaptive AI tutoring, moving beyond simple reactive chatbots to systems that proactively sequence learning activities. By integrating large language models with reinforcement learning, these platforms can analyze complex behavioral signals—such as code-editing patterns and dialogue quality—to provide personalized instruction at scale. A five-month study demonstrated that this approach significantly boosts student engagement and academic performance, particularly for those starting with weaker foundational skills. The research emphasizes that maintaining a "productive struggle" through appropriately calibrated difficulty is essential for long-term educational success and equity. Ultimately, the research advocates for an integrated system architecture that combines algorithmic decision-making with pedagogical scaffolding to transform digital learning.

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