Construction and Effect Evaluation of AI-Empowered Personalized Teaching Model for Ideological and Political Education in Colleges and Universities

Abstract

With the rapid development of artificial intelligence technology, digital transformation in education is accelerating, and ideological and political education in colleges and universities faces the dual tasks of teaching-model innovation and effectiveness improvement. Traditional models are characterized by homogeneous content, single teaching methods, and one-sided evaluation, making it difficult to meet the personalized growth needs of college students. Based on constructivist learning theory, precision teaching theory, and intelligent education theory, this paper proposes an AI-empowered personalized teaching model for ideological and political education and designs its operational mechanism from four dimensions: intelligent adaptation of teaching resources, dynamic regulation of teaching processes, real-time generation of teaching feedback, and multidimensional quantitative teaching evaluation. An empirical study is conducted with 1,200 students taking ideological and political courses at three universities. Controlled experiments and statistical analysis are used to evaluate the model. The results show that the personalized teaching model significantly improves students’ course participation, theoretical cognitive level, and value identity, providing a practical framework for intelligent teaching and data-driven educational evaluation in higher education.