An adaptive learning path recommendation framework based on deep learning for AI-driven vocational English education

The rapid growth of artificial intelligence (AI) has significantly reshaped the landscape of education, particularly in vocational English training, which demands personalized and adaptive learning approaches. As a core component of Smart Education, AI-driven frameworks offer opportunities to transform traditional teaching methods into intelligent, data-driven, and learner-centered systems. This study proposes an innovative adaptive learning path recommendation framework based on deep learning, aiming to address challenges such as learner diversity, engagement, and efficiency. This study proposes an innovative adaptive learning path recommendation framework based on deep learning, aiming to address challenges such as learner diversity, engagement, and efficiency. By integrating attention mechanisms and collaborative filtering, the framework dynamically captures contextual dependencies and user-specific behaviors to generate personalized learning paths. The methodology involves preprocessing learner interaction data, embedding it into a high-dimensional space, and utilizing a deep neural network enhanced with attention mechanisms to predict resource relevance. Furthermore, the integration of optical technologies, such as eye-tracking and virtual reality (VR), provides a novel approach to capturing real-time learner engagement and interaction patterns. The framework was validated through experiments on a real-world vocational English dataset, involving over 5,000 learners. Results demonstrate that the proposed model outperforms traditional collaborative filtering and non-attention-based deep learning models, achieving superior precision, recall, and F1-Score. Additionally, learners following the recommended paths exhibited a 25% faster course completion rate and a 30% higher retention rate compared to static, non-adaptive approaches. These findings highlight the framework's potential to enhance learning outcomes and engagement in vocational English education. This work bridges the gap between AI-driven recommender systems, optical technologies, and practical educational applications, offering a scalable, dynamic, and effective solution for personalized learning in diverse educational contexts.