Design and Implementation of Personalized Learning System Based on Generative Artificial Intelligence

This paper proposes a personalized learning system based on generative artificial intelligence, which aims to improve students' learning efficiency and interest through adaptive learning paths and content recommendations. The system integrates advanced artificial intelligence technologies such as GAN and VAE to achieve deep customization and dynamic adjustment of the learning process. First, the GAN model is used to generate personalized learning resources, including courseware, exercises and learning paths, which are dynamically adjusted according to students' knowledge mastery and learning styles. VAE is used to construct a latent space representation of students' learning status and further optimize the personalized recommendation of learning content. To verify the effectiveness of the system, this paper conducted many simulation experiments. By analyzing the learning behavior data of different student groups, the system's performance in personalized recommendation and learning progress adaptation was evaluated. The experimental results show that the personalized learning system based on generative artificial intelligence has significant advantages in improving students' learning effects, shortening learning time, and increasing learning participation. Specific data show that the recommendation accuracy of the system reaches 92%, the learning efficiency is improved by 15%, and the student satisfaction is 20% higher than the traditional learning model.