Trends and Achievements in Learning Technology

Trends and Achievements in Learning Technology

Validation of the Generative Artificial Intelligence Acceptance Scale Among University Students: Testing a Model Based on the Unified Theory of Acceptance and Use of Technology (UTAUT)

Document Type : Original Article

Authors
Khalil Zandi 1
Seyed Adnan Hosseini 2
Elahe Niknam 3
1 Assistant Professor, Department of Educational Administration, Farhangian University, Tehran, Iran.
2 Assistant Professor, Department of Psychology Education and Counselling, Farhangian University, Tehran, Iran.
3 Ph.D Candidate of Political Science, Ker.C., Islamic Azad University, Kermanshah, Iran.
10.22034/jlt.2025.2079509.1067
Abstract
The purpose of this study was to validate the Generative Artificial Intelligence Acceptance Scale, grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), among Iranian university students. The statistical population consisted of all undergraduate students nationwide, from whom 351 participants were selected through convenience sampling. Data were collected using the Persian version of the Generative AI Acceptance Scale, comprising 20 items across four subscales: performance expectancy, effort expectancy, facilitating conditions, and social influence. Prior to administration, cultural adaptation was ensured through back-translation procedures and expert review for face validity. Data analyses included first- and second-order confirmatory factor analyses, Cronbach’s alpha coefficients, and one-sample t-tests. First-order CFA results indicated that item loadings ranged from 0.52 to 0.87, confirming the four-factor structure (χ²/df = 2.12; RMSEA = 0.056; CFI = 0.95). Second-order CFA further demonstrated that the four components collectively formed a higher-order construct representing generative AI acceptance (χ²/df = 2.26; RMSEA = 0.060; CFI = 0.94). Factor loadings of the components on the higher-order construct ranged from 0.51 to 0.82. The overall reliability of the scale was 0.90, and the subscales showed reliability values ranging from 0.61 to 0.89. Examination of the construct level also revealed that the generative AI acceptance score among the participating students was significantly above average (p < 0.01). Based on these findings, the scale can be effectively used in future research to assess generative AI acceptance among university students.
Keywords
Artificial intelligence
Effort expectancy
Facilitating conditions
Performance expectancy
Social influence
Subjects
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Trends and Achievements in Learning Technology
Volume 2, Issue 7 - Serial Number 7
Autumn 2025
Pages 143-166

  • Receive Date 01 July 2025
  • Revise Date 15 August 2025
  • Accept Date 30 August 2025
  • Publish Date 23 September 2025