Evidence map›Paper›PMID 41060923›Full record

ArticlePloS one2025

Translation and validation of the artificial intelligence anxiety scale in German.

André Hajek, Larissa Zwar, Ariana Neumann, Razak M Gyasi, Dong Keon Yon, Supa Pengpid, Karl Peltzer, Hans-Helmut König

Abstract readValidation Study
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

André HajekDepartment of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg Center for Health Economics, Hamburg, Germany.ORCID https://orcid.org/0000-0002-6886-2745
Larissa ZwarDepartment of Psychology, Brock University, St. Catharines, Ontario, Canada.
Ariana NeumannDepartment of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg Center for Health Economics, Hamburg, Germany.
Razak M GyasiAfrican Population and Health Research Center, Nairobi, Kenya.
Dong Keon YonCenter for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea.
Supa PengpidDepartment of Health Education and Behavioral Sciences, Faculty of Public Health, Mahidol University, Bangkok, Thailand.
Karl PeltzerDepartment of Health Education and Behavioral Sciences, Faculty of Public Health, Mahidol University, Bangkok, Thailand.ORCID https://orcid.org/0000-0002-5980-0876
Hans-Helmut KönigDepartment of Health Economics and Health Services Research, University Medical Center Hamburg-Eppendorf, Hamburg Center for Health Economics, Hamburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

AnxietyArtificial IntelligenceAdolescentAdultAgedFactor Analysis, StatisticalFemaleGermanyHumansMaleMiddle AgedPsychometricsReproducibility of ResultsSurveys and QuestionnairesTranslationsYoung Adult

Identifiers

PMID41060923
PMCPMC12507318

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.