Exploring Emerging Norms of AI Attribution and Disclosure in Programming Education

TL;DR

This study investigates how computer science students perceive AI attribution and disclosure norms in programming education, revealing that AI assistance level and human refinement influence ownership judgments and suggesting a shift towards process-oriented policies.

Contribution

It provides empirical insights into student perceptions of AI authorship and proposes a novel pedagogical approach to AI attribution in education.

Findings

Attribution judgments depend on AI assistance and human refinement levels.

Students' authorship perceptions influence their disclosure policy expectations.

A shift to process-oriented attribution can enhance critical engagement with AI.

Abstract

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.