发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过调查443名CS本科生，发现自我效能感与冒充者现象负相关，且冒充者现象与将AI相关情景视为严重作弊正相关，强调需关注学生AI认知与学术诚信。
AI 中文摘要
学术不端是计算机科学（CS）教育中一个紧迫的问题，而人工智能（AI）的兴起和后疫情时代在线学习的普及加剧了这一问题的严重性。自我效能感和冒充者现象是与学术不端相关的心理构念，但很少有研究探讨它们与学生如何看待学术不端之间的关系。本研究在在线学习和AI代理已变得普遍的情境下，调查了冒充者现象、自我效能感与CS学生学术诚信认知之间的相互作用。我们针对美国东南部一所大型大学的入门至中级计算课程本科生（n = 443）进行了一项在线异步调查。调查收集了自我效能感的测量数据、对13个潜在作弊情景的反应、冒充者现象（Clance冒充者现象量表）以及人口统计信息。我们观察到自我效能感与冒充者现象之间存在显著的负相关，而冒充者现象与将AI相关情景判定为严重作弊之间存在正相关。冒充者现象还与感知的课程投入度和先前助教（TA）经历相关，而自我效能感则与种族相关。这些发现强调需要更好地理解学生对AI的认知，并在高等教育情境中的学术诚信讨论中持续关注冒充者现象。
英文摘要
Academic dishonesty is an urgent problem in computer science (CS) education, compounded by the rise of artificial intelligence (AI) and the prevalence of online learning in a post-COVID world. Self-efficacy and the impostor phenomenon are psychological constructs linked to academic dishonesty, yet little research has examined how they relate to how students perceive it. This study investigates the interplay between the impostor phenomenon, self-efficacy, and CS students' perceptions of academic integrity, in a context where online learning and AI agents have become commonplace. We conducted an online, asynchronous survey of undergraduate students (n = 443) in introductory to mid-level computing courses at a large Southeastern U.S. university. The survey collected measures of self-efficacy, responses to 13 potential cheating scenarios, the impostor phenomenon (Clance Impostor Phenomenon Scale), and demographic information. We observed a significant negative correlation between self-efficacy and the impostor phenomenon, and a positive correlation between the impostor phenomenon and judging an AI-related scenario as serious cheating. The impostor phenomenon was also correlated with perceived course dedication and prior teaching assistant (TA) experience, whereas self-efficacy was correlated with ethnicity. These findings highlight the need to better understand students' perceptions of AI and to keep the impostor phenomenon present in conversations about academic integrity in higher education scenarios.
CommentsPaper accepted to ACM SIGCSE TS 2027
