Age, Education, and Occupational Gradients in Artificial Intelligence Anxiety

Klaus F. Zimmermann, Gökhan Karabulut, Zeynep Keklik and Kıvanç Altıntaş

No 1863, GLO Discussion Paper Series from Global Labor Organization (GLO)

Abstract: Higher levels of anxiety about artificial intelligence (AI) among older and less-educated adults have now been documented in large population-based samples from high-income countries. Much less is known about how these differences arise, whether age and education interact, and whether occupational position helps understand them, particularly in middle-income economies. It evaluates the measurement structure of the scale, tests flexible functional forms and age-education interactions, and decomposes both gradients by occupational skill level, task class, and routine-task intensity. AI anxiety increases approximately linearly with age, whereas its association with education is strongly negative and nonlinear, with the largest gap between the lowest and highest education categories. Age and education largely show additive associations with AI anxiety. The age gradient is weaker among university graduates, although evidence for an interaction is limited for the total score and more evident for several subdimensions. Among the 409 employed respondents, occupational skill level and task class substantially reduce the education gradient but leave the age gradient unchanged: labor-market position helps explain educational but not age differences. Routine-task intensity contributes little beyond broad occupational categories, while professionals and managers report relatively high anxiety conditional on education and task class. Because the cross-sectional design precludes causal and age-period-cohort interpretations, the estimates are conditional associations. Even so, the results show that AI anxiety is not randomly distributed: it is systematically structured by human capital, age, and occupational position in a large middle-income economy.

Keywords: artificial intelligence anxiety; human adaptation to AI; AI adoption; age and education gradients; occupational exposure; task content; human capital; technological change; automation; Turkey (search for similar items in EconPapers)
JEL-codes: D91 I21 J14 J24 O33 (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/344574/1/GLO-DP-1863.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:glodps:1863

Access Statistics for this paper

More papers in GLO Discussion Paper Series from Global Labor Organization (GLO) Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().