Research on Personalized Learning Resource Recommendation Method for Students Supported by Artificial Intelligence

With the rapid development of artificial intelligence technology, the education sector is undergoing unprecedented changes.Personalized learning has become a key method to enhance teaching quality and learning outcomes.This paper aims to explore the application of artificial intelligence technology in personalized learning resource recommendation for students, by constructing user profiles, multidimensional models, and personalized recommendation algorithms, in order to provide precise learning resource recommendations for students.This paper proposes a personalized learning resource recommendation algorithm based on a one-dimensional convolutional neural network (1D-CNN).The algorithm first extracts local features of the sequence through convolutional operations, then uses pooling operations to extract long-term features of the sequence, and combines the two features through weighted addition to obtain the user feature information, which allows for the comprehensive extraction of both local and long-term features.Subsequently, the user feature information is multiplied by the linearly transformed sequence information to introduce temporal information.Additionally, student learning records, class performance, and incorrect question records are collected and integrated as user feature information.These user features are passed through a feedforward network to achieve nonlinear transformation and cross-dimensional interaction enhancement.Finally, the user feature vector and item feature vector are computed to obtain their relevance, which is then used for recommendations.Experimental evaluations validate the effectiveness and feasibility of the proposed method, with the aim of providing valuable insights for educational reform and development.