Research on Intelligent Generation and Adaptation System of Civic Education Content Based on Machine Learning

This paper deeply analyzes the innovative application and intelligent upgrading steps of Artificial Intelligence Generated Content (AIGC) in Civic and Political Education.Based on metadata, we construct an automated generation model of Civics education resources, divide the meta-properties of education knowledge resources, set up a knowledge tracking model DT-BKT to obtain students' mastery of Civics knowledge, adopt personalized recommendation model to realize the high adaptability of education resources based on students' Civics learning, and combine the functions of each model to build a Civics education content intelligent generation and adaptability system.Knowledge tracking experiments show that the AUC and R2 indexes of the DT-BKT model in this paper are better than those of other comparative models, and it can better simulate the response of learners on the dataset.Facing different groups of learners is able to recommend Civics courses that meet the learners' abilities.For active learners and potential learners, the average difficulty of the recommended client layer is higher by 0.08~0.15and 0.06~0.085respectively, while the overall difficulty difference for inactive learners is between -0.01~0.015,and the recommended difficulty is in line with the characteristics of the learner groups.