The Integration of Artificial Intelligence (AI) Into the Teaching of Functional Polymers

Abstract

This paper focuses on the research on the integrated application of artificial intelligence technology in the teaching of functional polymers. Addressing core issues in traditional teaching in this field, such as abstract concepts, high barriers to practical implementation, and disconnection from cutting-edge research and development, the study systematically elaborates on the inherent necessity of an AI-driven paradigm shift in teaching. It constructs a comprehensive framework for teaching reform across five dimensions: intelligent curriculum restructuring, virtual simulation platforms, data-driven inquiry, adaptive assessment, and industrial case library development. The research indicates that the deep integration of artificial intelligence can not only visualize abstract knowledge and enable personalized learning through methods like knowledge graphs and molecular simulations but also fundamentally promote the transformation of teaching models from "knowledge transmission" to "data-driven innovative practice." This cultivates interdisciplinary talent that meets the demands of national strategies for new materials. Finally, the paper envisions future trends in the deep integration of AI and education, pointing out that its development will rely on the evolution of technologies such as vertical domain-specific large language models and generative AI, ultimately building a new intelligent educational ecosystem characterized by human-machine collaboration and the integration of teaching, research, and industry.