The deepening of the integration of artificial intelligence technology and the field of education has injected new vitality into the optimization of the ideological and political education model. This paper focuses on the optimization of the ideological and political teaching mode and the construction of its personalized learning path, and analyzes three important problems encountered in the construction of the personalized learning path for ideological and political education. Subsequently, it describes the representation of seven learner characteristic parameters as a reference for the representation of the learner characteristic data set in the personalized learning path. After completing the preparation and input of learner characteristic data, the improved genetic algorithm is applied to construct the personalized learning path model under the Civic Education Mode. With the assistance of the designed personalized learning path model, students’ knowledge mastery rate can be increased by up to 35.18%. With the support of artificial intelligence, the personalized learning path model of Civic and Political Education can effectively recommend learning resources and provide learning assistance according to the individual situation of different learners.
