Date
Publisher
arXiv
The widespread availability of generative artificial intelligence (GenAI) has created a pressing challenge in computer science (CS) education: how to incorporate powerful AI tools into programming coursework without undermining student learning through cognitive offloading. This paper presents an assessment model that permits the use of generative AI for take-home programming assignments while enforcing individual mastery through immediate, assignment-driven written quizzes. To promote authentic learning, these in-class, closed-book assessments are weighted more heavily than the assignments themselves and are specifically designed to verify the student's comprehension of the algorithms, structure, and implementation details of their submitted code. Preliminary empirical data were collected from an upper-level computer science course to examine the relationship between self-reported GenAI usage and performance on AI-free quizzes, exams, and final course grades. Statistical analyses revealed no meaningful linear correlation between GenAI usage levels and assessment outcomes, with Pearson correlation coefficients consistently near zero. These preliminary results suggest that allowing GenAI for programming assignments does not diminish students' mastery of course concepts when learning is verified through targeted, assignment-driven quizzes. Although limited by a small sample size, this study provides preliminary evidence that the risks of cognitive offloading can be mitigated by allowing AI-assisted programming practice while verifying understanding through assignment-driven, AI-free quizzes. The findings support the responsible adoption of open GenAI policies in upper-level CS courses, when paired with rigorous, independent assessment mechanisms.
Study at a glance
- Question: Does allowing generative AI on programming assignments combined with in-person verification quizzes harm student course performance?
- Setting and data: Lawrence Technological University, 14 upper-level computer science students (13 graduate, 1 undergraduate), fall 2025 semester.
- Method: Correlational and descriptive analysis of course assessment scores, student self-reported AI usage, and survey responses.
Why it matters
Allowing generative AI on programming coursework risks cognitive offloading that harms deep conceptual learning, while completely banning AI tools is ineffective and misaligned with industry practice.
Key findings
- Statistical analyses found no meaningful linear correlation between self-reported generative AI usage and overall course points (r ≈ 0.11, p = 0.720) or individual quiz scores.
- All surveyed students agreed or strongly agreed that the programming assignments helped them understand concepts even though AI was allowed, and that the assessment strategy supported effective learning.
- Ninety-three percent of students reported that allowing generative AI reduced their stress and helped them learn more effectively, while also motivating them to understand code before submitting.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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