AI驱动的个性化学习路径推荐研究（2024）

Abstract:This study explores the design and application of an AI-driven personalized learning path recommendation system. In modern education，personalized learning has become a key factor in enhancing learning outcomes and student satisfaction. With the advancement of artificial intelligence technologies，especially large models，machine learning，and deep learning algorithms，personalized learning path recommendation systems can automatically generate the most suitable learning paths based on students’ learning behavior，performance，and personal interest data. This research constructs a learner model based on digital twin technology，integrating various modern AI technologies，such as the language model GPT-3.5 and its subsequent versions，as well as deep learning algorithms，to achieve personalized recommendations. The experimental results show that the system performs excellently in improving learning outcomes，user satisfaction，and in terms of system response time and resource consumption. However，the study also highlights challenges related to data privacy，security，and model interpretability. Future work will focus on optimizing algorithms to improve system interpretability and multimodal data integration capabilities.

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