Comparison of the target quote (according to EUROSTAT 2021) and our sample (in terms of sex x age).

AimArtificial intelligence anxiety refers to fear due to challenges caused by AI-related changes in one’s own life. As the first study, our aim was to translate and validate the German version of the Artificial Intelligence Anxiety Scale (AIAS-G). Furthermore, norm values (i.e., reference scores derived from the population) were presented.MethodsData were used from a quota-based sample of the adult population in Germany spanning from 18 to 74 years (n = 3,270) reflecting the German population in terms of sex, age and federal state. Data were collected in January 2025. The translation process followed established guidelines. Reliability was determined (in terms of Cronbach’s alpha and McDonald’s omega). Confirmatory factor analysis was conducted to examine construct validity. Concurrent validity was investigated by calculating pairwise correlations of AIAS-G with depressive symptoms, anxiety symptoms, life satisfaction and ikigai (Japanese concept mainly referring to meaning/purpose in life). Moreover, norm values were offered (also for specific sociodemographic groups). The AIAS-G sum score ranges from 21 to 147, with higher values corresponding to a higher AI anxiety level.ResultsCronbach’s alpha was .97 for the AIAS-G (subscales from .94 to .98). The mean AI anxiety level was 69.6 (SD: 32.6), with highest mean levels among women, older adults, individuals being divorced/widowed, individuals with low education, and retired individuals. The four-factor model originally proposed was substantiated by the findings of the confirmatory factor analysis. Higher levels of AI-related anxiety were associated with more depressive symptoms (r = .32, p < .001), more anxiety symptoms (r = .34, p < .001), lower life satisfaction (r = −.16, p < .001) and lower ikigai levels (r = −.21, p < .001).ConclusionThe AIAS-G is a psychometrically sound instrument designed to determine AI anxiety levels among German speakers. Further translation and validation studies are necessary to enable comparisons across various countries.