Problem
Research questions and friction points this paper is trying to address.
academic dishonesty
self-efficacy
impostor phenomenon
artificial intelligence
computer science education
When Does It Become Cheating? Exploring the Roles of Self-Efficacy and the Impostor Phenomenon in Computing Students'Perceptions of Academic Dishonesty
🤖 AI Summary
This study addresses how self-efficacy and imposter syndrome among computer science students shape their perceptions of AI-assisted academic misconduct in online learning environments. Through an asynchronous survey of 443 undergraduates, the research integrates the Clance Impostor Phenomenon Scale with statistical correlation analyses to jointly quantify these psychological constructs for the first time, examining their differential effects on judgments regarding AI-enabled cheating. The findings reveal a significant negative correlation between self-efficacy and imposter syndrome, with the latter positively predicting perceived cheating severity associated with AI use. By demonstrating the critical role of psychological factors in delineating the boundaries of AI-related academic integrity, this work provides empirical evidence to inform and enhance integrity education in higher education institutions.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Academic Dishonesty
Impostor Phenomenon
Self-Efficacy
Artificial Intelligence
Computer Science Education
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