Impostor feelings affect cheating views among computing students
When Does It Become Cheating? Exploring the Roles of Self-Efficacy and the Impostor Phenomenon in Computing Students' Perceptions of Academic Dishonesty
Computers and Society
Summary
Academic dishonesty is a growing concern, especially with online learning and AI tools becoming common. The paper explores how students' confidence in their abilities (self-efficacy) and feelings of being a fraud (impostor phenomenon) influence their views on cheating. Students who feel like impostors tend to see AI-related actions as more serious cheating and have different attitudes about time spent on courses. The study shows the complex relationships between psychology and perceptions of cheating in computer science education.
What this means in practice
- •For academic integrity officers: Design interventions that consider impostor feelings and self-confidence when addressing cheating perceptions and policy enforcement in computer science courses.
- •For educational technology developers: Develop AI learning tools that account for how students with impostor feelings perceive AI-related cheating scenarios to better support ethical use.
Authors
Chandler C. Payne, Jhonathan Sora-Cardenas, Olufisayo Omojokun, Pedro Guillermo Feijóo-García
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.