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.

翻译：本文通过计算分析203个包含学生活动的GitHub仓库及23,065次学生提交，对三届研究生HCI原型设计课程（2022年秋季、2023年秋季、2025年秋季）中学生生成式AI采纳行为的演变开展了纵向观察案例研究。基于此前对2023届学生的质性研究，我们区分了两种AI问责追踪层级：披露（说明使用了AI工具）与归因（将特定成果或任务归功于AI工具）。研究发现，工具披露率在三届学生中从0%增长至66%，但明确的贡献归因仍属少数实践，二者之间的差距揭示了即便在主动披露的学生群体中依然存在问责缺失。至2025年，AI已成为嵌入课程模板与学生自建设备的基础设施：学生日益频繁地列举所用工具，却鲜少说明这些工具的具体贡献。我们认为，基于披露的问责框架已无法适应当今"氛围编程"时代。问题不在于学生隐瞒AI使用情况，而在于为离散可识别行为而构建的规范，无法捕捉持续性的环境协同创作。我们以此案例研究为基础，邀请研讨会深入探讨何为真正的协同思维问责。