An Intelligent Framework for English Teaching through Deep Learning and Reinforcement Learning with Interactive Mobile Technology

As globalization deepens, the significance of English teaching in the educational landscape has become more prominent. Traditional teaching methods are increasingly inadequate for providing personalized and efficient learning experiences. This gap is being addressed by the rapid advancements in artificial intelligence, especially through deep and reinforcement learning. These technologies provide a framework for intelligent English teaching systems by mimicking human learning processes to customize personalized learning experiences, optimize learning paths, and enhance efficiency. However, challenges remain in fine-tuning teaching strategies to meet the varying needs of individual learners and dynamically adapting to their evolving interests in the short term. This study introduces a novel framework for an intelligent English teaching system that leverages the potential of interactive mobile technology alongside a deep Q-network (DQN) algorithm to dynamically adjust English teaching strategies. This approach enables real-time personalization of teaching strategies to create optimal learning paths for individual learners. Moreover, it incorporates a model based on neural collaborative filtering to capture and adapt to learners’ short-term dynamic interests, thereby recommending relevant learning content in real-time. This framework enhances learning efficiency and personalizes content delivery, demonstrating considerable theoretical and practical value for the future of educational technology.