Visible Adoption, Untracked Contribution: GitHub Evidence of the Accountability Gap Across Three Cohorts of an HCI Prototyping Course

2026-06-11

AI Summary

This study addresses the accountability gap in human-AI collaborative creation, specifically the disparity between “disclosure” and “attribution.” While students increasingly disclose their use of generative AI—rising from 0% to 66% across three academic terms from 2022 to 2025—they rarely specify its actual contributions. Drawing on longitudinal data from a graduate course, including 203 GitHub repositories and 23,065 commits, combined with computational analysis and qualitative case studies, the work proposes a two-tiered “disclosure–attribution” accountability framework. Findings reveal that by 2025, AI had become embedded as course infrastructure; however, current norms centered solely on disclosure are insufficient for managing the persistent, ambient nature of AI collaboration. The study thus provides an empirical foundation for developing fine-grained mechanisms of accountable AI co-creation.

Abstract

This paper presents a longitudinal, observational case study of how student GenAI adoption shifted across three cohorts (Fall 2022, 2023, and 2025) of the same graduate-level HCI prototyping course, using computational analysis of 203 GitHub repositories with student activity and 23,065 student commits. Building on a prior qualitative study of the 2023 cohort, we distinguish two levels of AI accountability trace: disclosure (naming that an AI tool was used) and attribution (crediting a specific artifact or task to an AI tool). We find that tool disclosure grew from 0% to 66% of repositories across the three cohorts, while explicit contribution attribution remains a minority practice, and the gap between the two reveals where accountability is missing even among students who disclose. By 2025, AI is infrastructure embedded in course templates and student-built devices: students increasingly name the tools they used, but rarely specify what those tools contributed. We argue that disclosure-based frameworks are insufficient for the vibe-coding era. The failure is not that students conceal AI use; it is that a norm built for episodic, identifiable acts cannot capture continuous, ambient co-creation. We offer this case study as grounding for the workshop's conversation about what genuine co-thinking accountability looks like.

Problem

• AI accountability
• GenAI adoption
• attribution gap
• disclosure
• human-AI collaboration

Innovation

• AI accountability
• disclosure vs. attribution
• GenAI adoption
• GitHub mining
• human-AI co-creation