Application of AI and deep learning technology for IPE education under dual track cultivation model

This work intends to explore the effectiveness of a dual-track cultivation model for ideological and political literacy in vocational colleges driven by artificial intelligence deep learning models. This work compares the performance of different models on several key indicators of ideological and political education. Through analysis of experimental results across varying data volumes, the optimized model demonstrates significant advantages in four areas: mastery of ideological and political knowledge, ideological and political consciousness, ideological and political practical ability, and student satisfaction. The highest score for political belief reaches 4.8, while the scores for theoretical knowledge mastery, social practice participation, and activity satisfaction all reach 4.7, far surpassing traditional models. This indicates that the optimized model can more effectively help students understand and retain course content. Additionally, the optimized model significantly enhances students' recognition and trust in the national political system and core values. It also improves students' ability to apply ideological and political knowledge to real-world problems. Lastly, in terms of student satisfaction, the optimized model performs exceptionally well in both course and activity satisfaction. Therefore, this work contributes to the field of ideological and political education in vocational colleges.