Digital technology is profoundly transforming various aspects of life, thus highlighting the need to enhance digital literacy on a national scale. In primary and secondary schools, artificial intelligence (AI) education plays a pivotal role in fostering digital literacy. To comprehensively investigate the variables influencing AI education in primary and secondary schools, a dynamic system model was constructed based on the CIPP (Context, Input, Process, and Product) education evaluation model. We employed a mixed-method research approach, combining both qualitative and quantitative methods, to thoroughly investigate the influencing factors and perform predictive and simulation experiments. Firstly, we construct a system framework for AI education in primary and secondary schools based on CIPP model, utilizing qualitative methods such as literature review and expert interviews to identify influencing factors and define the system's boundaries. Secondly, we collect 814 questionnaire responses from 12 primary and secondary schools and employ Structural Equation Modeling (SEM) for quantitative analysis to explore the relationships between these influencing factors. Finally, these relationships are utilized to construct a System Dynamics model, allowing for an in-depth exploration of the development trends in AI education in primary and secondary schools through predictive analysis and control simulation. Controlled simulations allow us to predict and validate factors that influence the level of AI education development, aiding in the identification of high-leverage elements. Our findings underscore that AI education in Chinese primary and secondary schools is still in its initial stage with insufficient developmental momentum. Through controlled simulation results, it is found that the development of AI education in primary and secondary schools is influenced by both external systems and internal student systems, with national policy serving as the fundamental driving force. Among these influencing factors, societal factors play a predominant role in the external environment, followed by school factors, while the students' level of learning engagement is identified as the most crucial factor within the internal system. Based on the results of prediction and simulation studies, recommendations for improvement are proposed, including policy reforms and collaboration between schools and enterprises.