Ensuring Computer Science Learning in the AI Era: Open Generative AI Policies and Assignment-Driven Written Quizzes
Chan-Jin Chung

TL;DR
This study explores integrating generative AI into CS education by allowing AI-assisted programming while ensuring student mastery through targeted, independent quizzes, with preliminary data indicating this approach does not impair learning outcomes.
Contribution
It proposes an assessment model combining AI-enabled assignments with verification quizzes to maintain learning integrity in the AI era.
Findings
No significant correlation between GenAI use and assessment performance
Assignment-driven quizzes effectively verify student understanding
Preliminary evidence supports responsible AI policy adoption in CS education
Abstract
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…
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Taxonomy
TopicsTeaching and Learning Programming · Artificial Intelligence in Healthcare and Education · Online Learning and Analytics
