Adaptive English vocabulary recommendation systems: a computational intelligence approach using deep reinforcement learning

The increasing demand for personalized vocabulary learning solutions has highlighted the limitations of traditional methods, such as rote memorization and static recommendation systems, which fail to adapt to individual learning trajectories and engagement levels. This study introduces an innovative adaptive English vocabulary recommendation system powered by deep reinforcement learning (DRL). The proposed framework models vocabulary learning as a sequential decision-making process, where recommendations are dynamically adjusted based on user performance and contextual factors. Leveraging a deep Q-network, the system integrates multi-dimensional features, including proficiency metrics, behavioral data, and contextual information, to optimize both learning outcomes and user satisfaction. Unlike conventional approaches, the system incorporates advanced reward mechanisms that balance immediate learning gains with long-term retention objectives. By addressing scalability and real-time adaptability, the proposed system offers a novel, robust solution for personalized learning, laying the groundwork for future advancements in adaptive educational technologies.