Dynamic Optimization of Personalized English Learning Ability Enhancement Methods Driven by Reinforcement Learning

Intelligent and adaptive English language learning utilizes artificial intelligence to personalize the learning experience. Adapting English learning content in real-time, depending on individual student behavior and progress, is not possible with existing systems due to a lack of dynamic optimization. This proposed method is used to develop a dynamic, reinforcement learning-driven framework that adapts to each learner’s cognitive and emotional states, thereby enhancing individualized English language acquisition. This method uses Meta-Cognitive Reinforcement Learning (MCRL) to describe each learner as an intelligent agent with a dynamic state vector of proficiency, attentiveness, and tiredness. Based on reinforcement signals from learner performance and motivation, a meta-policy controller chooses optimal learning strategies and tasks. The results reveal that MCRL outperforms baseline systems in vocabulary, grammar, comprehension, learner engagement, and retention. Real-time adaptability is demonstrated by a 22% increase in learning efficiency and a reduction in task repetition. Results show that MCRL-based learners improved their average competency scores by 18% over four weeks while maintaining better engagement. Autonomously optimizing the learning path reduced cognitive fatigue and increased motivation. Ultimately, reinforcement learning enhances learning outcomes and the learner experience in individualized language instruction.