Against the background of the rapid rise of generative artificial intelligence (GAI) represented by DeepSeek, ChatGPT, ERNIE Bot, Tongyi Qianwen, Doubao and Kimi, the teaching of programming courses of new engineering majors is facing profound challenges: the role of programmers is undergoing a fundamental transformation, the traditional “programming-centered” teaching content can hardly meet the industrial demand of the GAI era, and engineering practice is disconnected from the real production process of enterprises. To address these problems, this paper investigates the influence mechanism of GAI on the teaching of programming courses and proposes systematic improvement measures. First, starting from the feasibility and inevitability of GAI undertaking basic programming tasks, the coupling mechanism between GAI and programming teaching is analyzed, and the potential possibility of programming role transformation and the inevitable trend of human-machine collaborative development of complex functions are revealed. Second, the concepts of teaching and learning are reconstructed, and a “broad-specialized-integrated” programming curriculum system integrating the “design-centered” concept is constructed. Third, combined with the CDIO engineering education concept, a GAI-driven “design-centered” engineering practice model is explored, and an engineering case base highlighting disciplinary intersection and facing real industry scenarios is built. The research provides a theoretical reference and a practical path for the construction of programming courses of new engineering majors in application-oriented universities in the GAI era.

Lili Zhang, Wei Wei, Jing Li, Wentao Wu, Ge Wang. Generative AI-Driven Reconstruction of the Programming Curriculum System for New Engineering Majors. Frontiers in Educational Research (2026), Vol. 9, Issue 9: 33-38. https://doi.org/10.25236/FER.2026.090905.

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