探索编程教育中人工智能归属与披露的新兴规范
Exploring Emerging Norms of AI Attribution and Disclosure in Programming Education
AI总结：
研究通过因子情景研究探讨学生在不同情境下对AI协助归属和披露的判断，发现AI协助程度和人类修正影响学生归属感知，提出从声明式政策转向过程导向的归属机制。
摘要：
生成式AI模糊了计算教育中的作者身份，导致学生如何归因AI帮助存在不确定性。为探讨这些新兴规范，我们对102个独特情境中的94名计算机科学学生进行了因子情景研究，系统操纵评估类型、AI自主性、学生活动、先验知识和人类修正努力。本文详细阐述了这些因素如何影响学生对所有权和披露偏好的认知。我们的发现表明，归因判断主要由不同水平的AI协助和人类修正驱动。我们还发现，学生对作者身份的感知显著预测其政策期望。本文最后提出从声明式政策转向过程导向的归因，将披露转变为促进学生批判性参与AI生成内容的教育机制。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
