The teaching of higher mathematics is fundamentally challenged by its abstract and logical nature, while traditional instructional methods show significant limitations in visualizing and dynamizing core concepts. The rise of artificial intelligence has brought revolutionary breakthroughs to the visualization teaching of higher mathematics, largely due to its powerful capabilities in image generation, natural language interaction, and dynamic simulation. This paper focuses on the application of AI technology in this field and systematically constructs three teaching models, “intelligent diagram generation” “interactive experimental exploration”, and “dynamic process simulation”. These models are supported by in-depth analysis of typical cases such as saddle surfaces, vector fields, and integrals. Furthermore, the paper discusses the theoretical foundations and practical pathways of AI visualization teaching, aiming to provide theoretical guidance for promoting the paradigm shift in higher mathematics education from static abstraction to dynamic intuition.
Zhao et al. (Thu,) studied this question.