Generative artificial intelligence acceptance scale: A validity and reliability study
Abstract
The purpose of this study is to formulate an acceptance scale grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The scale is designed to scrutinize students’ acceptance of generative artificial intelligence (AI) applications. This tool assesses students’ acceptance levels toward generative AI applications. The scale development study was conducted in three phases, encompassing 627 university students from various faculties who have utilized generative AI tools such as ChatGPT during the 2022–2023 academic year. To evaluate the face and content validity of the scale, input was sought from professionals with expertise in the field. The initial sample group (n = 338) underwent exploratory factor analysis (EFA) to explore the underlying factors, while the subsequent sample group (n = 250) underwent confirmatory factor analysis (CFA) for the verification of factor structure. Later, it was seen that four factors comprising 20 items accounted for 78.349% of total variance due to EFA. CFA results confirmed that structure of the scale, featuring 20 items and four factors (performance expectancy, effort expectancy, facilitating conditions, and social influence), was compatible with the obtained data. Reliability analysis yielded Cronbach’s alpha coefficient of 0.97, and the test–retest method demonstrated a reliability coefficient of 0.95. To evaluate the discriminative power of the items, a comparative analysis was conducted between the lower 27% and upper 27% of participants, with subsequent calculation of corrected item-total correlations. The results demonstrate that the generative AI acceptance scale exhibits robust validity and reliability, thus affirming its effectiveness as a robust measurement instrument.
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The autonomous learning dimension is distinct from prior dimensions such as performance expectancy and planning with AI.
“which is distinct from the relevant devised dimensions in the prior studies, such as performance expectancy (e.g.Karaoglan Yilmaz et al., 2024) and planning with AI (Kim & Lee, 2022).”
From Development and validation of the Generative AI Literacy for Learning Scale (GenAI-LLs) (2025) · cites this paper as Karaoglan Yilmaz et al., 2024
The present study makes an original contribution to the relevant literature by moving beyond the technical and ethical aspects of AI literacy in previous studies, which focus more on learning or teaching about AI.
“The present study makes an original contribution to the relevant literature by moving beyond the technical and ethical aspects of AI literacy in previous studies, which focus more on learning or teaching about AI (e.g.Karaoglan Yilmaz et al., 2024;B. Wang et al., 2023).”
From Development and validation of the Generative AI Literacy for Learning Scale (GenAI-LLs) (2025) · cites this paper as Karaoglan Yilmaz et al., 2024
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- Authors
- Yilmaz, F. G. K., Yilmaz, R., & Ceylan, M.
- Affiliations
- Department of Computer Technology & Information Systems, Faculty of Science, Bartin University, Bartin, Turkey
- Bartin University (TR)
- Venue
- International Journal of Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2023-12-12
- Volume
- 40
- Issue
- 24
- Pages
- 8703-8715
- ISSN
- 1044-7318 (ISSN-L)
- 1532-7590
- OpenAlex
- W4389638819
- Cited by
- 244
- References
- 65
- Citations by year
- 2026: 104 · 2025: 96 · 2024: 44
- FWCI
- 62.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Human-Computer Interaction (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Gender Studies › Gender and Technology in Education
- Keywords
- generative AI, technology acceptance, UTAUT, scale development, university students, ChatGPT, performance expectancy, effort expectancy, facilitating conditions, social influence, measurement instrument validation
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.
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