Personalized Learning Systems Using AI for Adaptive Educational Content Delivery Based on Reinforcement Learning Algorithms

Individualized control of instructions has received increased attention as an approach to improving the achievement of learning goals since it Individualises the delivery of these goals to cater for the need of each learner. In this study, the author looks at the creation of an adaptive education content delivery framework with the usage of RL. In contrast to the conventional approach of using systems that provide learners with static and structured training courses, the present work adapts a RL, which allows for real time content changes based on learner's progress, choice and performance. The system applies deep reinforcement learning in order to make the interaction process and the learning path interesting and effective for each user. Some of them are ability to use predictive analysis to suggest areas where students need help, built-in feedback system and modularity to combine different forms of educational material. From the experimental outcomes it can be confirmed that the knowledge retention and skill acquisition rates of the proposed system have enhanced manifold than conventional training methods. In addition to that, the authors highlight that the framework is designed for scalability of learning; now, it can be applied to all different types of settings for learning including the formal schooling, workplace learning and training, as well as lifelong learning settings. Thus, being devoted to promote the synthesis of the principles of education with the most advanced AI techniques, this research sets its goal to fill the gap between individual learning and mass learning. Rather, the studies suggest that RL-based personalized learning models may revolutionize learning processes and engage learners and educators.