AI-driven personalized learning path optimization and adaptive resource recommendation for foreign language education

Based on the theory of personalized learning, learning style and AI algorithm, the research constructs a model framework that includes four core modules: learner characteristics analysis, learning content recommendation, learning strategy adjustment and feedback and evaluation. Through multi-dimensional learning behavior data analysis, combined with machine learning and deep learning technology, the model can provide tailor-made learning paths and resource recommendations for learners. The empirical study adopts an eight-week experimental design, and compares the learning effects of the experimental group (using AI recommendation model) and the control group (traditional linear learning). The results show that the experimental group is significantly better than the control group in learning efficiency, cognitive load reduction and long-term learning effect. The improvement rate of learning effect in the experimental group reached 28.8%, which was significantly higher than that in the control group (15.7%, P < 0.001). The matching degree between the learning path recommended by AI and the expert path reaches 82.7%, which meets the research hypothesis. The cognitive load in the experimental group was significantly lower than that in the control group (p<0.01). In addition, users' satisfaction with the AI learning system is high, with an overall score of 4.3 (out of 5), reflecting the good user experience of the system. The research results prove the effectiveness of the AI-based personalized foreign language learning path optimization model, and provide new ideas and methods for the personalized development of foreign language education.