AI-assisted software engineering courses assess primarily through participation, project work, and coding assignments rather than exams¶

Claim · All claims
Evidence · 1 study · qualitative r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Across 14 conventional percentage-based courses, every course included participation or engagement, most included a project/capstone, and proctored assessment appeared in only a small minority. → Francis Geng 2026

Evidence¶

Francis Geng 2026¶

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 doi:10.48550/arxiv.2608.05898

q2 · i? · qualitative · r2

Analysis of grading categories extracted from 14 conventional percentage-based courses (Table 3). The article reports "Every course included participation or engagement, most included a project/capstone component"; project/capstone median weight was 50% (range 30–85%), quiz/exam appeared in only 4 courses.

"Every course included participation or engagement, most included a project/capstone component, and many included coding assignments."

Discussion¶

Related Claims¶

• In AI-assisted software engineering courses, AI-required and programming work carry high median grade weights while proctored assessment is rare — possibly the same claim (merge candidate)
• In a course that had removed examinations, most student groups designing a fair grading policy reinvented proctored or handwritten assessment — related
• Learning-by-teaching an LLM improved assignment and project performance versus historical cohorts, but not exam performance, in an undergraduate CS course — related
• Among AI-assisted software engineering courses with public objectives, human-AI collaboration, tool development, and evaluation are the most common learning-objective themes — related