🤖 AI Summary
Despite the widespread industrial adoption of generative AI, higher education—particularly in computer science—lags significantly, emphasizing theoretical foundations while neglecting hands-on tool proficiency, thereby leaving students unprepared for responsible, professional AI application.
Method: This study designed and delivered a generative AI applications course for undergraduate and master’s students, structured modularly around authentic development tasks—including code generation and document automation—and integrating ethical reflection and empirical evaluation.
Contribution/Results: As the first systematic integration of generative AI tool competency into the core CS curriculum in China, the course demonstrably enhanced students’ conceptual understanding and practical efficacy, as validated by mixed-methods evaluation. It establishes a scalable, transferable pedagogical framework that effectively bridges the industry–academia skills gap in AI literacy and application.
📝 Abstract
Research on how the popularization of generative Artificial Intelligence (AI) tools impacts learning environments has led to hesitancy among educators to teach these tools in classrooms, creating two observed disconnects. Generative AI competency is increasingly valued in industry but not in higher education, and students are experimenting with generative AI without formal guidance. The authors argue students across fields must be taught to responsibly and expertly harness the potential of AI tools to ensure job market readiness and positive outcomes. Computer Science trajectories are particularly impacted, and while consistently top ranked U.S. Computer Science departments teach the mechanisms and frameworks underlying AI, few appear to offer courses on applications for existing generative AI tools. A course was developed at a private research university to teach undergraduate and graduate Computer Science students applications for generative AI tools in software development. Two mixed method surveys indicated students overwhelmingly found the course valuable and effective. Co-authored by the instructor and one of the graduate students, this paper explores the context, implementation, and impact of the course through data analysis and reflections from both perspectives. It additionally offers recommendations for replication in and beyond Computer Science departments. This is the extended version of this paper to include technical appendices.