Design AI-assisted software engineering courses around AI-assisted projects, programming-intensive assignments, collaboration, evaluation and testing, and reflection on AI use¶

Strategy · All strategies
Evidence · 3 claims (3 for) · 1 study (1 qualitative), q2 · 0 of 1 report an effect size · 3 claims rest on one study

Description¶

The article distills common building blocks for instructors designing AI-assisted software engineering courses: "AI-assisted projects, programming-intensive assignments, collaborative development, evaluation and testing, and reflection on AI use." It also recommends mechanisms such as process logs, code reviews, demonstrations or oral defenses, individual reflections, and in-class checkpoints to make individual reasoning visible within project-based work. For departments, the article notes that introducing such courses requires decisions about scope, prerequisites, assessment, and placement.

Design Implications¶

Context¶

Requirements¶

• Courses must position AI use within realistic software development workflows rather than isolated tool exercises, per the analyzed courses.

Constraints¶

• The article states course-material analysis cannot show how instructors enacted these courses or how effective they were for learning.

Target Learners¶

• upper-division undergraduate software engineering students

Target Learning Goals¶

• AI-assisted software development
• individual reasoning and process visibility
• evaluation and testing of AI-assisted artifacts

Claims¶

• In AI-assisted software engineering courses, AI-required and programming work carry high median grade weights while proctored assessment is rare [+W]
• AI-assisted software engineering courses assess primarily through participation, project work, and coding assignments rather than exams [+W]
• Among AI-assisted software engineering courses with public objectives, human-AI collaboration, tool development, and evaluation are the most common learning-objective themes [+W]

Related Strategies¶

Examples¶

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Key Sources¶

• Francis Geng, Anshul Shah, Mia Chen, Paul Denny, Juho Leinonen, Bill Griswold, Gerald Soosai Raj, and Leo Porter. (2026). Mapping the Emerging Curriculum for AI-Assisted Software Engineering via Syllabus Analysis. https://arxiv.org/abs/2608.05898