Impact and Countermeasures of Generative AI on the Curriculum System of Computer Science in Universities

Dan-Yang Liu （ Daqing Open University, Daqing, Heilongjiang, 163311, China ）

Abstract

Generative AI (GenAI), represented by Large Language Models (LLMs) and diffusion models, is rapidly reshaping the global technological landscape, with higher education—particularly computer science (CS) majors—at the forefront of this transformation. This paper systematically analyzes the multi-dimensional and deep-seated impact of GenAI on the curriculum system of university CS courses, spanning knowledge structures, competency cultivation, pedagogical paradigms, and ethical values. It first elucidates the technical characteristics of GenAI and its deconstructive effect on core course content such as traditional programming. Subsequently, it discusses the potential threat GenAI poses to students' foundational coding abilities while simultaneously enhancing their creativity. The paper then analyzes the necessitated shift in teaching modes from "knowledge transmission" to "competency guidance." Finally, a strategic framework for response is proposed, integrating "preservation of fundamentals" with "innovation." By reconstructing course objectives and other measures, the framework aims to strengthen human-machine collaboration, solidify ethical boundaries, and build a new ecosystem for future-oriented computer science talent cultivation.

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