International Journal of Reliability, Risk and Safety: Theory and Application

International Journal of Reliability, Risk and Safety: Theory and Application

Normalization of the Artificial Intelligence Literacy Scale Based on Classical Test Theory (CTT)

Document Type : Original Research Article

Author
Sedigheh Heydari
Department of Psychology, Faculty of Psychology, Amiralmoemenin University of Ahvaz, Ahvaz, Iran.
10.22034/IJRRS.2025.8.1.4
Abstract
The rapid advancement of artificial intelligence (AI) has brought about transformative changes in various aspects of human life, leading to an exponential increase in the number of AI users. It has now become a part of everyone's lives. Therefore, the present study aimed to validate the Persian version of the "AI Literacy" scale to measure the competence of Iranian users (people over 15 years old) to use AI in the second half of 2024 to the beginning of 2025. The research method relied on psychometric methods (factor analysis), and the tool used was the AI Literacy Scale of Wang et al. (2023). The statistical population was comprised of Iranian users over 15 years old, and the statistical sample consisted of 480 of them who voluntarily participated in the research. The findings showed that Cronbach's alpha coefficient was 0.714, and the factor structure obtained through EFA had favorable fit indices in CFA (CFI, GFI, RFI, NFI, IFI) covering four factors (awareness, usage, evaluation, and ethics). The results showed that this scale has an acceptable factor structure and reliability among users over 15 years of age; therefore, considering that when using artificial intelligence, paying attention to ethical, security and human aspects is essential, this tool can be used as a valid tool to measure user competence in using artificial intelligence.
Keywords
Artificial Intelligence
Literacy
Normalization
scale
Subjects
  1. Mehrparsa, “Artificial Intelligence and its Application in Education,” Management and Entrepreneurship Studies, vol. 1, no. 33, pp. 32-46, 2020, (in Persian).
  2. Pinski and A. Benlian, “AI literacy for users–A comprehensive review and future research directions of learning methods, components, and effects,” Computers in Human Behavior: Artificial Humans, vol. 2, no. 1, 2024, Art. no. 100062, https://doi.org/10.1016/j.chbah.2024.100062.
  3. Zafari, A. Esmaeily, and A. Sadeghi-Niaraki, “An overview of the applications of artificial intelligence and virtual reality in education,” Educational Measurement and Evaluation Studies, vol. 11, no. 36, pp. 89-116, 2021, (in Persian), https://doi.org/10.22034/emes.2021.251559.
  4. Hamedanchi, A. Nafei, H. Hamzezadeh, M.R. Lakpour, and F. Rezaei, “Artificial intelligence and aging,” Gerontology. vol. 8, no. 2, pp. 80-85, 2023, (In Persian), http://dx.doi.org/10.22034/JOGE.8.2.80.
  5. Rajabi, “Liability of Artificial Intelligence; the Reflection of Developments in the Liability Rules,” Comparative Law Review, vol. 10, no. 2, pp. 449-466, 2019, (In Persian) doi: https://10.22059/jcl.2019.274782.633787.
  6. Peng et al., “Check your facts and try again: Improving large language models with external knowledge and automated feedback,” arXiv preprint, 2023, Art. no. 2302.12813, https://doi.org/10.48550/ arXiv.2302.12813.
  7. Mirzavand, M. Abdoli, and A. Hassanzadeh, “The dangers of artificial intelligence,” 16th National Conference on Management and Humanities Research in Iran, Tehran, 2023, (in Persian).
  8. Wang, P-L. P. Rau, and T. Yuan, “Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale,” Behaviour & information technology, vol. 42, no. 9, pp. 1324-1337, 2023, https://doi.org/10.1080/ 0144929X.2022.2072768.
  9. T. K. Ng, W. Wu, J. K. L. Leung, T. K. F. Chiu, and S. K. W. Chu, “Design and validation of the AI literacy questionnaire: The affective, behavioral, cognitive and ethical approach,” British Journal of Educational Technology, vol. 55, no. 3, pp. 1082-1104, 2024, https://doi.org/10.1111/bjet.13411.
  10. Celik, “Towards intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education,” Computers in Human Behavior, vol. 138, 2023, Art. no. 107468, https://doi.org/10.1016/j.chb.2022.107468
  11. C. Laupichler, A. Aster, N. Haverkamp, and T. Raupach, “Development of the scale for the assessment of non-experts’ AI literacy–An exploratory factor analysis,” Computers in Human Behavior Reports, vol. 12, no. 1, 2023, Art. no. 100338, https://doi.org/10.1016/j.chbr.2023.100338
  12. G. K. Yılmaz and R. Yılmaz, “Adaptation of artificial intelligence literacy scale into Turkish”, Journal of Information and Communication Technologies, vol. 5, no. 2, pp. 172-190, 2023, (in Turkish), https://doi.org/10.53694/bited.1376831.
  13. , Vu, S. T. Druga, E. Likhith, and T. Qiu, “Inclusive AI literacy for kids around the world,” in FabLearn Conference, NewYork, USA, 2019, pp. 104-111, https://doi.org/10.1145/3311890.331190.
  14. Kandlhofer, G. Steinbauer, S. Hirschmugl-Gaisch, and P. Huber, “Artificial intelligence and computer science in education: From kindergarten to university.” In 2016 IEEE Frontiers in Education Conference, 2016, https://doi.org/10.1109/FIE.2016. 7757570.
  15. Xiao and M. Bie, “The reform and practice of educational technology major in the age of artificial intelligence 2.0”, In IOP Conference Series: Materials Science and Engineering, IOP Publishing, vol. 677, no. 5, 2019, Art. no. 052094, https://doi.org/10.1088/1757-899X/677/5/052094.  
  16. D. Topal, A. T. Gökçe, C. D. Eren, and A. K. Geçer, “Artificial intelligence literacy scale: A study of reliability and validity in Turkish university students,” Journal of Learning and Teaching in Digital Age, vol. 10, no. 1, pp. 58-67, 2025, https://doi.org/10.53850/joltida.1440845
  17. H. Chen and K. Zhang, “Impact of basic artificial intelligence (AI) course on understanding concepts, literacy, and empowerment in AI among students,” Computer Applications in Engineering Education, e22806, 2025, https://doi.org/10.1002/cae.22806
  18. Guan, Y. Zhang, and M. M. Gu, “Pre-service teachers preparedness for AI-integrated education: An investigation from perceptions, capabilities, and teachers’ identity changes,” Computers and Education: Artificial Intelligence, vol. 8, 2025, Art. no. 100341, https://doi.org/10.1016/j.caeai.2024.100341
  19. C. Wang, H. Wang, Y. Li, J. Dai, X. Gu, and T. Yu, “Factors influencing university students’ behavioral intention to use generative artificial intelligence: Integrating the theory of planned behavior and AI literacy,” International Journal of Human-Computer Interaction, vol. 41, no. 11, pp. 6649-6671, 2025. https://doi.org/10.1080/10447318.2024.2383033
International Journal of Reliability, Risk and Safety: Theory and Application
Volume 8, Issue 1
June 2025
Pages 47-54

  • Receive Date 08 March 2025
  • Revise Date 04 May 2025
  • Accept Date 06 May 2025