Educators’ Acceptance and Use of AI Software: UTAUT2 Model

AÇIKGÜL K., ŞAD S. N.

SAGE Open, cilt.16, sa.1, 2026 (SSCI, Scopus) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 1
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1177/21582440251413449
  • Dergi Adı: SAGE Open
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, Education Abstracts, ERIC (Education Resources Information Center), Directory of Open Access Journals
  • Anahtar Kelimeler: acceptance, artificial intelligence, education, teacher, university lecturer, UTAUT2
  • İnönü Üniversitesi Adresli: Evet

Özet

The research aimed to examine the factors affecting educators’ acceptance and use of artificial intelligence (AI) software based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Research data was collected from 280 educators (teachers and education university lecturers) working at schools or universities across seven regions of Türkiye. The data was gathered using a self-report questionnaire including demographic information questions and Artificial Intelligence Acceptance Scale for Education (AIASE). Within the scope of this study, the AIASE was developed to determine educators’ acceptance of artificial intelligence technology. In the scale, the performance expectancy (PE) subscale included seven items, while the effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), and habit (H) subscales each comprised five items. The price value (PV) subscale contained three items, and the behavioral intention (BI) subscale was represented by six items. In conclusion, the developed scale provides a valid and reliable tool for educators. According to the goodness-of-fit values calculated in the Structural Equation Modeling analysis, the research model indicated an adequate fit. The results showed PE, HM, and H were significant determinants of BI toward educators’ acceptance of artificial intelligence software, but only H was a significant determinant of use behavior (UB). The model variables altogether explained 68% of the variability in educators’ BI and 34% of the variability in use behavior. According to the results, incentives and practices should be implemented that reinforce the factors of HM, PE and H that are effective in the AI acceptance.