Date
Publisher
arXiv
As Generative AI coding tools reshape professional software development, universities have begun designing courses to prepare students for AI-assisted development workflows. By analyzing the syllabi of these courses, we can gather empirical evidence about these courses, reveal how this emerging curricular area is being defined, and gain guidance for future curriculum design. We analyzed 23 publicly available syllabi and course materials of upper-division, credit-bearing courses that meet specific criteria, including explicitly addressing Generative AI in software engineering. Through iterative qualitative coding, we characterized courses' learning objectives, assessments, topics, and documented AI tools. Our analysis reveals commonalities and differences among these courses that allow researchers and educators to study and develop future courses.
Study at a glance
- Question: How are university courses teaching students to use generative AI in software engineering?
- Setting and data: 23 publicly available syllabi and course materials from upper-division, credit-bearing university courses.
- Method: Qualitative analysis of course documents (iterative qualitative coding).
Why it matters
As generative AI coding tools reshape professional software development, universities are designing courses for AI-assisted workflows. Empirical evidence is needed on how this emerging curricular area is being defined to help guide future curriculum design.
Key findings
- In 23 upper-division college courses, software engineering curricula explicitly addressed generative artificial intelligence to prepare students for professional development workflows.
- Syllabi across these courses shared commonalities in their learning objectives, instructional topics, student assessments, and documented artificial intelligence tools.
- The analyzed courses also demonstrated notable differences in how instructors structured their learning objectives, topics, assessments, and AI tools.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
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