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This study analyzes 23 syllabi from upper-division courses focused on AI-assisted software engineering to understand how educational institutions are adapting to the rise of Generative AI tools in professional development. By employing iterative qualitative coding, the research identifies key learning objectives, assessments, and topics that define this emerging curriculum. The findings highlight both commonalities and differences across courses, providing valuable insights for educators aiming to enhance curriculum design in this rapidly evolving field.
Emerging AI tools are reshaping software engineering education, revealing significant curricular trends that could redefine how future developers are trained.
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.
