IntroductionArtificial intelligence (AI) anxiety has emerged as a significant phenomenon accompanying the digital transformation and increasing adoption of AI in workplace settings. This study aims to identify and synthesize the different dimensions of AI anxiety discussed in prior research.MethodsThis systematic literature review combines the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) guidelines with the Theory–Context–Characteristics–Methodology (TCCM) analytical framework. The review addresses the 3W1H research questions (What, Where, When, and How) related to AI anxiety dimensions and provides a comprehensive analysis of the theories, contexts, characteristics, and methodologies used in this research domain.ResultsThe findings reveal that Conservation of Resources (COR) theory and Social Cognitive Theory (SCT) are the most frequently applied theoretical perspectives. Research on AI anxiety dimensions has been conducted predominantly in China and Türkiye, particularly within the healthcare sector. General AI anxiety is the most extensively examined dimension, with numerous antecedents, mediators, moderators, and outcomes identified. In contrast, dimensions such as job replacement anxiety, AI ethics anxiety, AI learning anxiety, collective anxiety, and configuration anxiety remain relatively underexplored. Furthermore, regression analysis is the most commonly employed statistical technique in the reviewed studies.DiscussionThe findings indicate a strong concentration on general AI anxiety and a limited focus on more specific dimensions across different levels of analysis. This review contributes to a comprehensive understanding of AI anxiety and its dimensions while identifying important research gaps. Practical implications for practitioners and researchers, along with study limitations and directions for future research, are also discussed.