Improvement of AI-Driven Deep Knowledge Tracing Algorithms

This research aims to assess the application and effectiveness of AI-driven Deep Knowledge Tracing (DKT) technology in vocational education. By integrating LSTM, Bayesian Knowledge Tracing (BKT), and Reinforcement Learning (RL), we developed a hybrid model designed to optimize personalized learning paths. According to the experimental data, this hybrid model considerably improves student learning outcomes compared to the classic BKT and the independent LSTM models. The model demonstrates superior performance in prediction accuracy, knowledge mastery, and error control, highlighting its substantial potential in the vocational education sector. Future research should focus on optimizing this model and its application in a broader range of educational contexts.