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The Roles of Personality Traits, AI Anxiety, and Demographic Factors in Attitudes toward Artificial Intelligence
Open accessAbstract
The present study adapted the General Attitudes toward Artificial Intelligence Scale (GAAIS) to Turkish and investigated the impact of personality traits, artificial intelligence anxiety, and demographics on attitudes toward artificial intelligence. The sample consisted of 259 female (74%) and 91 male (26%) individuals aged between 18 and 51 (Mean = 24.23). Measures taken were demographics, the Ten-Item Personality Inventory, the Artificial Intelligence Anxiety Scale, and the General Attitudes toward Artificial Intelligence Scale. The Turkish GAAIS had good validity and reliability. Hierarchical Multiple Linear Regression Analyses showed that positive attitudes toward artificial intelligence were significantly predicted by the level of computer use (β = 0.139, p = 0.013), level of knowledge about artificial intelligence (β = 0.119, p = 0.029), and AI learning anxiety (β = −0.172, p = 0.004). Negative attitudes toward artificial intelligence were significantly predicted by agreeableness (β = 0.120, p = 0.019), AI configuration anxiety (β = −0.379, p < 0.001), and AI learning anxiety (β = −0.211, p < 0.001). Personality traits, AI anxiety, and demographics play important roles in attitudes toward AI. Results are discussed in light of the previous research and theoretical explanations.
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“while Kaya et al. (2024) showed that age did not predict attitudes toward AI.”
From Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs (2024) · cites this paper as Kaya et al., 2024
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Metadata
- Authors
- Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M.
- Affiliations
- Atatürk University (TR)
- Department of Counseling and Guidance, Sivas Cumhuriyet University, Sivas, Turkey
- University of Chester (GB)
- Department of Social Sciences, Bayburt University, Bayburt, Turkey
- Department of Psychology, Ataturk University, Erzurum, Turkey
- Corresponding author
- Schepman, Astrid
- Venue
- International Journal of Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2022-12-07
- Volume
- 40
- Issue
- 2
- Pages
- 497-514
- ISSN
- 1044-7318 (ISSN-L)
- 1532-7590
- OpenAlex
- W4311843323
- Cited by
- 688
- References
- 125
- Citations by year
- 2026: 264 · 2025: 283 · 2024: 120 · 2023: 20 · 2022: 1
- FWCI
- 146.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Human-Computer Interaction (publishedVersion, cc-by-nc-nd)
- ChesterRep (University of Chester) (submittedVersion, cc-by-nc-nd)
- DSpace@Bayburt (submittedVersion, other-oa)
- Topics
- Social Sciences › Gender Studies › Gender and Technology in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- attitudes toward AI, AI anxiety, personality traits, agreeableness, computer use, AI knowledge, scale adaptation, Turkish adaptation, demographic factors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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