Exploring Emerging Norms of AI Attribution and Disclosure in Programming Education
This addresses attribution uncertainty in AI-assisted programming education for students and educators, proposing a shift to process-oriented policies.
The study investigated how computer science students attribute AI assistance in programming education, finding that attribution judgments are primarily driven by levels of AI assistance and human refinement, with students' authorship perceptions predicting policy expectations.
Generative AI blurs the lines of authorship in computing education, creating uncertainty around how students should attribute AI assistance. To examine these emerging norms, we conducted a factorial vignette study with 94 computer science students across 102 unique scenarios, systematically manipulating assessment type, AI autonomy, student activity, prior knowledge, and human refinement effort. This paper details how these factors influence students' perceptions of ownership and disclosure preferences. Our findings indicate that attribution judgments are primarily driven by different levels of AI assistance and human refinement. We also found that students' perception of authorship significantly predicts their policy expectations. We conclude by proposing a shift from statement-style policies to process-oriented attribution, transforming disclosure into a pedagogical mechanism for fostering critical engagement with AI-generated content.