AI-Enabled Personalized Online Learning Using Reinforcement Learning

The quick development of online learning platforms has fundamentally altered the way that education is provided, making it more adaptable and available to a larger audience. Many of these platforms, however, continue to rely on rigid, rule-based systems that are unable to adjust to the unique behavior of each learner or their evolving learning requirements. There is still a lack of a cohesive, learner-focused approach, despite recent research demonstrating that reinforcement learning (RL) can enhance particular areas like recommendation systems, adaptive learning paths, and overall learning quality.By enabling systems to continuously learn and get better through interaction and feedback, reinforcement learning offers a potent solutionIn order to improve personalization, adaptability, learning analytics, and overall quality of experience (QoE), this paper investigates the application of reinforcement learning (RL) in online learning environments. The study identifies important issues like scalability, real-time implementation, data privacy, and sustaining learner engagement by analyzing and integrating findings from current research.Lastly, the paper discusses how a useful RL-based adaptive learning framework can be applied as a practical project for contemporary online learning platforms.