Model Construction and Application of Artificial Intelligence in Improving the Efficiency of Ideological and Political Education

In recent years, with the vigorous rise of cutting-edge technologies such as big data analysis, machine learning (ML), and cloud computing, artificial intelligence (AI) technology is penetrating into various fields of society at an unprecedented speed, among which higher education has undergone profound and extensive changes. This change is not only reflected in the innovation of teaching methods, but also deeply touches on the renewal of educational concepts and the improvement of educational quality. This article innovatively proposes an AI based efficiency improvement model for ideological and political education (IPE), which fully utilizes big data analysis capabilities to deeply mine and insight into students' learning behavior, interest preferences, and ideological dynamics, providing personalized and differentiated teaching content recommendations for IPE. At the same time, combined with ML algorithms, real-time monitoring and warning of students' ideological dynamics can be achieved, and potential ideological deviations can be detected and intervened in a timely manner. The experimental results show that the model significantly improves students' learning interest and participation, and students have achieved significant results in mastering IPE theoretical knowledge, improving their ideological and moral qualities, and cultivating their sense of social responsibility.