Investigating the Psychometric Properties of the Short form of the Attitude Scale Towards Artificial Intelligence in Iranian Society

Subject Areas :

zahra Akhavi Samarein ¹ , Ali Ghorbaninejad ² , Saeed Khakdalgojebagloo ³

1 - Dept. of Counseling, Faculty of Education and Psychology, Mohaghegh Ardabili University, Ardabil, Iran
2 - PhD student in Counseling, Faculty of Educational Sciences and Psychology, University of Mohaghegh Ardabili, Ardabil, Iran
3 - PhD student in counseling

Received: 2025-04-17 Accepted : 2025-05-29 Published : 2025-12-03

Keywords: Attitude, Psychometric Properties, Artificial Intelligence, Short Form,

Abstract :

Technological advances in recent decades, especially in the field of artificial intelligence, have caused extensive changes in people's lives. In the meantime, people's attitudes towards artificial intelligence play a decisive role in accepting or resisting this technology. The aim of the present study was to investigate the psychometric properties of the short form of the Attitude towards Artificial Intelligence Scale in an Iranian sample. The method of the present study was descriptive in terms of its purpose and applied in terms of its psychometric type. To collect data, 340 people were selected using convenience sampling. To collect data, the short form of the Attitude towards Artificial Intelligence scale by Sinderman et al, the Self-Efficacy Scale of Artificial Intelligence Users by Wang and Chuang, and the Motivations for Using Artificial Intelligence Questionnaire by Yurt and Kasarji were used. In addition to descriptive statistics, the Pearson correlation test, Cronbach's alpha coefficient, and confirmatory factor analysis were used to analyze the data. The results of confirmatory factor analysis confirmed two factors: acceptance and fear of AI. The fit indices of the confirmatory factor analysis model are CFI=0.96, NFI=0.94, NNFI=0.95, RMSEA=0.061. The results of Pearson correlation coefficient analysis to examine concurrent validity showed that the acceptance factor of the short form of the attitude towards artificial intelligence scale had a positive and significant correlation with the self-efficacy scale of the artificial intelligence user and the motivations for using artificial intelligence Questionnaire, and the fear factor of artificial intelligence had a negative and significant correlation with the aforementioned scales.. The short form of the Attitudes towards AI Questionnaire has the necessary validity and reliability to measure this construct in an Iranian sample. Therefore, the results obtained confirmed the reliability and validity of this tool in the sample in question.

References:

References
Azarbayjani, M., Salarifar, M. R., Abbasi, A., Kaviani, M., & Mousavi Asl, S. M. (2003). Social psychology with an attitude towards Islamic sources. Hozeh and University Research College. [In Persian]
Azdrifam, Nasser. (1403). A systematic review of robotics research in the field of education: lessons from the past and future directions. Quarterly Journal of Information and Communication in Educational Sciences. 15 (1), 147-165. [In Persian]
Becker, D. (2018). Possibilities to Improve Online Mental. Advances in azInformation and Communication Networks: Proceedings of the 2018 Future of Information and Communication Conference (FICC), Vol. 1,
Brooks, A. (2019). The benefits of AI: 6 societal advantages of automation. Rasmussen University. Available online: https://www. rasmussen. edu/degrees/technology/blog/benefits-of-ai/(accessed on 15 March 2023).
Bujang, M. A., Ab Ghani, P., Soelar, S. A., & Zulkifli, N. A. (2012). Sample size guideline for exploratory factor analysis when using small sample: Taking into considerations of different measurement scales. 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE),
Chen, L. (2017). International competitiveness and the fourth industrial revolution. Entrepreneurial Business and Economics Review, 5(4), 111-133.
Costley, K. C. (2014). The positive effects of technology on teaching and student learning. Online submission.
Darko, A., Chan, A. P., Adabre, M. A., Edwards, D. J., Hosseini, M. R., & Ameyaw, E. E. (2020). Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in construction, 112, 103081.
Gansser, O. A., & Reich, C. S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 65, 101535.
Grassini, S. (2023). Development and validation of the AI attitude scale (AIAS-4): a brief measure of general attitude toward artificial intelligence. Frontiers in psychology, 14, 1191628.
Harry, A. (2023). Role of AI in Education. Interdiciplinary Journal & Hummanity (INJURITY), 2(3).
Jones, A., & Castellano, G. (2018). Adaptive robotic tutors that support self-regulated learning: A longer-term investigation with primary school children. International Journal of Social Robotics, 10, 357-370.
Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial intelligence (AI) enabled e-learning. The International Journal of Information and Learning Technology, 38(1), 1-19.
Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O., & Demir Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 497-514.
Kaye, S.-A., Lewis, I., Forward, S., & Delhomme, P. (2020). A priori acceptance of highly automated cars in Australia, France, and Sweden: A theoretically-informed investigation guided by the TPB and UTAUT. Accident Analysis & Prevention, 137, 105441.
Kim, S.-W., & Lee, Y. (2024). Investigation into the influence of socio-cultural factors on attitudes toward artificial intelligence. Education and Information Technologies, 29(8), 9907-9935.
Kumar, D. (2021). Importance of Artificial Intelligence in Education. International Journal of Scientific Research in Engineering and Management, 2(05), 1-5.
Liu, J., Chang, H., Forrest, J. Y.-L., & Yang, B. (2020). Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors. Technological Forecasting and Social Change, 158, 120142.
Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021). Artificial intelligence for good health: a scoping review of the ethics literature. BMC medical ethics, 22, 1-17.
Mohsenin, S., Esfidani, M. R. (2014). Structural Equations Based on the Partial Least Squares Approach Using Smart-PLS Software: Educational and Practical Applications. Mehraban Publishing Institute. [In Persian]
Neha, Sonia. Enakshi Khular Sharmaa. Narotam, Singhab& Amita Kapoor. (2020). Artificial Intelligence in Business: From research and innovation to market deployment. Procedia Computer Science.
OECD. (2019). Artificial intelligence in society. OECD.
Sadri Damirchi, Esmaeil , Ghahremanloo, Maryam and Khakdal Ghojebegloo, Saeed. (2025). Investigating the psychometric properties of the Artificial Intelligence User Self-Efficacy Scale in Iranian Society. Quarterly Journal of Applied Psychology, (),. doi: 10.48308/apsy.2025.238117.1749. [In Persian]
Schepman, A., & Rodway, P. (2020). Initial validation of the general attitudes towards Artificial Intelligence Scale. Computers in human behavior reports, 1, 100014.
Sheykholeslami, A., Khakdal Ghojebeyglou, S., Zardi Gikloo,B. (2025). Psychometric Properties of the Questionnaire of Artificial Intelligence Use Motives in University Students.Quarterly of educational measurement, In print. Doi:10.22054/JEM.2024.81632.3561. [In Persian]
Sindermann, C., Sha, P., Zhou, M., Wernicke, J., Schmitt, H. S., Li, M., Sariyska, R., Stavrou, M., Becker, B., & Montag, C. (2021). Assessing the attitude towards artificial intelligence: Introduction of a short measure in German, Chinese, and English language. KI-Künstliche intelligenz, 35(1), 109-118.
Wang, Y.-Y., & Chuang, Y.-W. (2024). Artificial intelligence self-efficacy: Scale development and validation. Education and Information Technologies, 29(4), 4785-4808.
Yurt, E., & Kasarci, I. (2024). A Questionnaire of Artificial Intelligence Use Motives: A contribution to investigating the connection between AI and motivation. International Journal of Technology in Education, 7(2).