Research on Generative AI-Empowered Smart Education in Automotive Structure Course

With the rapid development of automotive technologies, the knowledge system of Automotive Structure course is updating quickly, involves large volumes of information, and exhibits significant interdisciplinary characteristics, making traditional classroom teaching insufficient to meet students' learning needs. This paper proposes a smart education model driven by generative AI, integrating AI-generated dynamic knowledge presentation, problem-based reasoning, and exploration of cutting-edge technologies to construct personalized, layered learning paths for students. Combined with the problem-based learning (PBL) approach, platforms such as ChatGPT and DeepSeek are employed to assist students in analyzing structural principles, exploring design solutions, and engaging in inquiry-based learning. Empirical analysis shows that this model significantly improves students' knowledge mastery, engineering problem-solving skills, and innovation abilities, while enhancing learning initiative and immersion. This study provides a systematic framework for reforming Automotive Structure course teaching and offers a replicable paradigm for AI-empowered education in engineering courses.