Integrating Artificial Intelligence (AI) into education, particularly civic education, represents a transformative shift. This study explores the innovative fusion of AI with teaching methodologies, aiming to enhance educational outcomes and foster comprehensive student development. We construct a multidimensional civic education framework by employing theoretical and empirical approaches, examining the dynamics between educators, students, content, and pedagogical strategies. We assess student academic performance and behavior by utilizing the Multi-Task Classroom Behavior Recognition Network (MCBRN) and multivariate analysis of variance (ANOVA). Our findings reveal that the AI-enhanced teaching model significantly boosts student engagement and learning achievements in the experimental group, with behavior recognition accuracy reaching 96.9%. Moreover, these students demonstrated superior examination scores and overall competency levels compared to the control group (P<0.05), highlighting the effectiveness of this novel approach in elevating the quality of civic education through personalized and efficient learning experiences.