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.

翻译：生成式AI模糊了计算教育中的作者归属界限，使学生对如何标注AI辅助行为产生不确定性。为考察这些新兴规范，我们开展了一项析因情境研究，面向94名计算机科学学生设置102个独特场景，系统操控评估类型、AI自主性、学生活动、先验知识与人工精炼程度。本文详述了这些因素如何影响学生的所有权认知与披露偏好。研究结果表明，署名判断主要受AI辅助及人工精炼的不同程度驱动。我们还发现，学生对作者身份的感知显著预测其政策预期。最后我们提出从声明式政策转向过程导向式署名，将披露转化为培育批判性参与AI生成内容的教学机制。