Analysis explores GenAI-driven improvements in course content and collaborative teaching models, indicating potential advancements in education quality.
The uneven distribution of higher education resources between eastern and western China has a long history. In particular, computer science education in Western regions, constrained by geographical, economic, and brain drain issues, lags significantly behind that in Eastern regions. The development of GenAI technology brings new opportunities for Western computer science education to catch up. This paper focuses on GenAI-empowered reform in computer science education and explores its application in optimizing teaching content and innovating teaching models. The study proposes GenAI-based strategies for optimizing course teaching content, including enhancing basic, practical, and cutting-edge content of computer courses. Additionally, it explores the innovation of a “teacher-student-machine” collaborative teaching model, emphasizing the improvement of teaching effectiveness and students’ learning experience through human-machine collaboration. This study provides new ideas and methods for the reform of computer science education and offers practical references for the application of GenAI technology in higher education.
No takes yet. Share an insight, caveat, or question.
Qiao et al. (2025) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: