The AI stress and anxiety scale (AISAS): development, initial validation and insight on the diffusion of AI-related stress and anxiety

Abstract

Growing concerns about the psychological impact of generative AI have highlighted the need for better measurement tools. Existing scales are limited by narrow samples and a failure to distinguish anxiety from stress. This research introduces the Artificial Intelligence Stress and Anxiety Scale (AISAS), a psychometrically validated instrument assessing both constructs in the general population. Development followed best-practice guidelines across two preregistered studies using representative UK adult samples for age, gender, and ethnicity. An initial 62-item pool was refined via expert ratings and a Content Validity Index. Exploratory factor analysis (Study 1, N  = 301) identified four factors: job-related concerns, AI adaptation stress, privacy concerns, and existential/consciousness concerns, explaining 72% of variance. The adaptation stress dimension was retained as a brief two-item indicator ( r = .68, Spearman-Brown ρ = 0.81) rather than a fully developed subscale. The general scale showed excellent internal consistency (α = 0.92), full gender measurement invariance, and preliminary evidence of convergent, discriminant, and criterion-related validity. Specific dimensions differentially predicted AI usage frequency and behavioral intention. Study 2 ( N  = 324) confirmed the factor structure’s stability across an independent sample. Descriptive findings suggest privacy concerns are common, while stress related to adapting to and learning about AI is relatively infrequent. Overall, the AISAS provides a reliable, multidimensional tool for researchers and practitioners assessing psychological responses to artificial intelligence.