Mapping the Emerging Curriculum for AI-Assisted Software Engineering via Syllabus Analysis

2026-08-06

AI Summary

This study addresses the lack of a systematic understanding of AI-augmented software engineering curricula in higher education, which hinders effective instructional design. Through qualitative content analysis, the authors iteratively coded syllabi from 23 advanced courses to systematically map their learning objectives, assessment strategies, core topics, and AI tools employed. The work empirically reveals, for the first time, common patterns and variations across these dimensions—particularly in goal formulation, technological coverage, and evaluation approaches—and synthesizes these insights into a reusable course design framework. This framework provides educators and researchers with an evidence-based foundation and practical guidance for developing and refining AI-integrated software engineering education.

Abstract

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.

Problem

• AI-assisted software engineering
• Generative AI
• curriculum mapping
• syllabus analysis
• software engineering education

Innovation

• Generative AI
• Software Engineering Education
• Syllabus Analysis
• Curriculum Design
• Qualitative Coding