Choices, Concerns, and Connections: Computing Students' AI Use and Links to Imposter Syndrome Subtypes

Abstract

Imposter syndrome is a persistent issue in computing education, shaping how students perceive their abilities, belonging, and potential place within computing-related subfields. Artificial intelligence (AI) tools have become increasingly common in tertiary education coursework and are often assumed to support learning and confidence. However, their growing presence raises questions about how learning, effort, and competence are evaluated in academic work. Little is known about how students experience them in relation to imposter syndrome, which centers on doubts about competence and legitimacy. This mixed-methods study examined how AI use relates to imposter syndrome experiences among computing students. Quantitative survey data were collected from N = 285 computing students from historically marginalized racial and ethnic backgrounds across three U.S. institutions. Measures included the Clance Imposter Phenomenon Scale, items aligned with Valerie Young’s imposter syndrome subtypes, and questions assessing AI-related legitimacy concern focused on authenticity and independence. Results indicated that AI use–related concerns are common and positively associated with all imposter syndrome subtypes. Perfectionist and Natural Genius beliefs emerged as the strongest predictors of AI-related concerns. Concern varied with imposter belief intensity rather than subtype classification. Qualitative analysis of open-ended responses (N = 254) demonstrated that students rarely described AI as reassuring. Instead, AI use often intensified concerns about authenticity and independence. Together, these findings suggest that AI tools do not uniformly build confidence. Instead, imposter beliefs shaped how AI was interpreted, highlighting the need for clear instructional guidance and attention to legitimacy as an equity concern.