Profiles of Artificial Intelligence Anxiety and Associated Individual Differences
Publisher
University of Tennessee at Chattanooga
Place of Publication
Chattanooga (Tenn.)
Abstract
Artificial intelligence (AI) is increasingly integrated into everyday life and the workplace, and individuals may experience different forms and levels of anxiety about its use and potential consequences. AI anxiety is a complex psychological response that may vary across individuals and across different aspects of AI. However, research often examines AI anxiety as an overall construct. The present study aims to identify distinct profiles of AI anxiety and examine how profile membership relates to individual differences in AI-related attitudes, knowledge, technology readiness, and psychological characteristics. Participants will complete a voluntary, online survey containing demographic questions and self-report measures of AI anxiety and related constructs. AI anxiety will be measured using the Artificial Intelligence Anxiety Scale (AIAS). The scale’s dimensions will serve as indicators in a latent profile analysis (LPA), a person-centered statistical approach used to identify subgroups of individuals with similar patterns of AI anxiety. Subsequently, individual differences in general attitudes toward AI, technology readiness, familiarity with and frequency of AI use, personality traits, and workplace-related perceptions will be examined as potential correlates or predictors of latent profile membership. The IRB proposal has been finalized and is ready for submission for approval. Before the primary analysis, data will be screened for missing responses, outliers, and other data-quality concerns, and descriptive statistics will be calculated. LPA models with different numbers of profiles will be compared using various statistical fit indices. Model selection will also consider profile size, interpretability, and theoretical relevance. Following profile identification, analyses will examine associations between profile membership and other variables measured in the study. By identifying distinct patterns of AI anxiety, this study seeks to provide a more nuanced understanding of how individuals experience AI-related concerns. From an I-O psychology perspective, examining how AI anxiety profiles relate to technology readiness, perceived employment threats, and job satisfaction may provide insights into employees' experiences with AI and inform future research on technology adoption, employee well-being, and organizational change.
Date
November 2026
Subject
Industrial and organizational psychology
Document Type
posters
Language
English
Rights
http://rightsstatements.org/vocab/InC/1.0/
License
http://creativecommons.org/licenses/by/4.0/
Profiles of Artificial Intelligence Anxiety and Associated Individual Differences
Artificial intelligence (AI) is increasingly integrated into everyday life and the workplace, and individuals may experience different forms and levels of anxiety about its use and potential consequences. AI anxiety is a complex psychological response that may vary across individuals and across different aspects of AI. However, research often examines AI anxiety as an overall construct. The present study aims to identify distinct profiles of AI anxiety and examine how profile membership relates to individual differences in AI-related attitudes, knowledge, technology readiness, and psychological characteristics. Participants will complete a voluntary, online survey containing demographic questions and self-report measures of AI anxiety and related constructs. AI anxiety will be measured using the Artificial Intelligence Anxiety Scale (AIAS). The scale’s dimensions will serve as indicators in a latent profile analysis (LPA), a person-centered statistical approach used to identify subgroups of individuals with similar patterns of AI anxiety. Subsequently, individual differences in general attitudes toward AI, technology readiness, familiarity with and frequency of AI use, personality traits, and workplace-related perceptions will be examined as potential correlates or predictors of latent profile membership. The IRB proposal has been finalized and is ready for submission for approval. Before the primary analysis, data will be screened for missing responses, outliers, and other data-quality concerns, and descriptive statistics will be calculated. LPA models with different numbers of profiles will be compared using various statistical fit indices. Model selection will also consider profile size, interpretability, and theoretical relevance. Following profile identification, analyses will examine associations between profile membership and other variables measured in the study. By identifying distinct patterns of AI anxiety, this study seeks to provide a more nuanced understanding of how individuals experience AI-related concerns. From an I-O psychology perspective, examining how AI anxiety profiles relate to technology readiness, perceived employment threats, and job satisfaction may provide insights into employees' experiences with AI and inform future research on technology adoption, employee well-being, and organizational change.
Department
University of Tennessee at Chattanooga. Dept. of Psychology