Dynamic programming algorithm for personalized English learning path based on artificial intelligence

The aim of English personalized learning is to create a learning process tailored to each learner’s needs for better efficiency and better engaged. Designing an adaptive learning path that adapts to the learner as he learns is a complex problem. The current solutions are based on a rule-based model or on a fixed sequence, offering no way of capturing the variability, engagement, or skill deficit of the learner at any given moment. To solve those problems, we suggest a Reinforcement Learning-Driven Dynamic Path Optimization (RL-DPO) framework, which can realize the generation of personalized English learning paths following the principles of dynamic programming and AI. The model is continuously updated based on the learners’ performance data, allowing the system to dynamically suggest the next module, which is the best fit based on the anticipated learning gain, and the time spent. DPO’s superior performance, achieving Learning Accuracy 96.8%, Progression Speed 1.42 units/session, Cumulative Reward 270, Path Efficiency 94.2%, Retention Score 92.8%, Learning Path Optimality 0.87, Cognitive Load 0.21, and Knowledge Depth Gain 91.6%, validates its effectiveness for adaptive language learning.