ISSN: 2225-8329
Open access
This study investigates the factors influencing students' intention to use artificial intelligence (AI) in higher education institutions, highlighting the critical role that perceptions of ease of use, self-efficacy, and perceived usefulness play in shaping these intentions. The study's primary aim is to explore how these constructs, alongside attitude as a mediator, impact the adoption of AI tools among students. A quantitative research design was employed, utilising a structured survey to collect data from 424 students across various institutions, resulting in 331 valid responses for analysis. Following data cleaning, 319 responses were deemed suitable for further study. The data analysis used Structural Equation Modeling (SEM) through SmartPLS software, allowing for rigorous hypothesis testing. The results indicated significant positive relationships, demonstrating that self-efficacy and perceived usefulness are crucial predictors of students' intention to use AI, while perceived ease of use showed less direct impact. The study concludes with suggestions for future research, including longitudinal studies to understand the evolving influences on AI adoption and qualitative approaches to capture more profound insights into student experiences. Furthermore, it recommends that higher education institutions implement training programs to enhance self-efficacy and simplify user experiences with AI technologies. The implications of this study are significant for educators and administrators, as they underscore the necessity of creating supportive environments that facilitate AI adoption. By enhancing the identified constructs, institutions can effectively empower students to leverage AI, leading to improved academic outcomes and better preparation for the digital workforce. The findings contribute valuable insights into integrating AI into educational practices, advancing the discourse on technology adoption in higher education.
Ali, J., Azeem, M., Marri, M. Y. K., & Khurram, S. (2021). University social responsibility and self-efficacy as antecedents of intention to use e-learning: Examining mediating role of student satisfaction. Psychology and Education, 58(2), 4219-4230.
Alkhawaja, M. I., Halim, M. S. A., Abumandil, M. S., & Al-Adwan, A. S. (2022). System Quality and Student's Acceptance of the E-Learning System: The Serial Mediation of Perceived Usefulness and Intention to Use. Contemporary Educational Technology, 14(2).
Anwar, I., Jamal, M. T., Saleem, I., & Thoudam, P. (2021). Traits and entrepreneurial intention: testing the mediating role of entrepreneurial attitude and self-efficacy. Journal for International Business and Entrepreneurship Development, 13(1), 40-60.
Bai, X., Guo, R., & Gu, X. (2024). Effect of teachers’ TPACK on their behavioral intention to use technology: chain mediating effect of technology self-efficacy and attitude toward use. Education and Information Technologies, 29(1), 1013-1032.
Bandura, A. (1997). Self-efficacy: The exercise of control. W.H. Freeman.
Chen, D., Liu, W., & Liu, X. (2024). What drives college students to use AI for L2 learning? Modeling the roles of self-efficacy, anxiety, and attitude based on an extended technology acceptance model. Acta Psychologica, 249, 104442.
Chirchir, L. K., Aruasa, W. K., & Chebon, S. K. (2019). Perceived usefulness and ease of use as mediators of the effect of health information systems on user performance. European Journal of Computer Science and Information Technology, 7(1), 22-37.
Chou, C. M., Shen, T. C., Shen, T. C., & Shen, C. H. (2022). Influencing factors on students’ learning effectiveness of AI-based technology application: Mediation variable of the human-computer interaction experience. Education and Information Technologies, 27(6), 8723-8750.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155–159. doi:10.1037/0033- 2909.112.1.155
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q., 13, 319–340.
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks, CA: SAGE.
Hair, J. F. L. D. S., Gabriel, M., da Silva, D. and Braga Junior, S. (2019). Development and validation of attitudes measurement scales: fundamental and practical aspects, RAUSP Management Journal, 54 (4), 490-507. https://doi.org/10.1108/RAUSP-05-2019-0098
Hair, J. F. M., Sarstedt, C. M., Ringle, and Gudergan. (2018). Advanced issues in partial least squares structural equation modeling. Thousand Oakes, CA: Sage Publications
Henseler, J., Ringle, C. M., and Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling, Journal of the Academy of Marketing Science, 43(1): 115-135.
Hong, J. W. (2022). I was born to love AI: The influence of social status on AI self-efficacy and intentions to use AI. International Journal of Communication, 16, 20.
Intaratat, K., Osman, Z., Nguyen, H. A. T., Suhandoko, A. D. J., & Sultana, N. (2024). Peer and tutor interaction effects on collaborative learning: The role of learning self-efficacy. Edelweiss Applied Science and Technology, 8(4), 2108-2121.
Kang, Y-N., Chang, C-H., Kao, C-C., Chen, C. Y., Wu, C-C. (2019) Development of a short and universal learning self-efficacy scale for clinical skills. PLoS ONE 14(1): e0209155. https://doi.org/ 10.1371/journal.pone.0209155
Kebah, M., Raju, V., & Osman, Z. (2019). Growth of online purchase in Saudi Arabia retail industry. International Journal of Recent Technology and Engineering, 8(3), 869-872.. ISSN: 2277-3878
Kebah, M., Raju, V., & Osman, Z. (2019). Online purchasing trend in the retail industry in Saudi. International Journal of Recent Technology and Engineering (IJRTE), 8(3), 865-868. ISSN: 2277-3878
Khan, N. A. (2024). Artificial intelligence, self-efficacy and engagement in religious tourism: evidence from Arbaeen pilgrimage. Journal of Hospitality and Tourism Insights.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10.
Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546-580.
Lee, Y. Y., Gan, C. L., & Liew, T. W. (2023). Thwarting instant messaging phishing attacks: the role of self-efficacy and the mediating effect of attitude towards online sharing of personal information. International Journal of Environmental Research and Public Health, 20(4), 3514.
Li, X. T., Rahman, A., Connie, G., & Osman, Z. (2020). Examining customers' perception of electronic shopping mall's e-service quality. International Journal of Services, Economics and Management, 11(4), 329-346.
Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, Yet Forsaken? Introducing a Cross-validated Predictive Ability Test in Partial Least Squares Path Modeling. Decision Sciences, 52(2), 362-392.
Liesa-Orús, M., Latorre-Cosculluela, C., Sierra-Sánchez, V., & Vázquez-Toledo, S. (2023). Links between ease of use, perceived usefulness and attitudes towards technology in older people in university: A structural equation modelling approach. Education and Information Technologies, 28(3), 2419-2436.
Moslehpour, M., Pham, V. K., Wong, W. K., & Bilgiçli, ?. (2018). E-purchase intention of Taiwanese consumers: Sustainable mediation of perceived usefulness and perceived ease of use. Sustainability, 10(1), 234.
Osman, Z., Mohamad, W., Mohamad, R. K., Mohamad, L., & Sulaiman, T. F. T. (2018). Enhancing students’ academic performance in Malaysian online distance learning institutions. Asia Pacific Journal of Educators and Education, 33, 19-28.
Pan, X. (2020). Technology Acceptance, Technological Self-Efficacy, and Attitude Toward Technology-Based Self-Directed Learning: Learning Motivation as a Mediator. Front. Psychol., 11, 564294.
Prayudi, I. G., Sukaatmadja, I. P. G., Yasa, N. N. K., & Giantari, I. G. A. K. (2022). The role of trust in mediation the effect of perception of ease of use and perception of usefulness on intention to re-using the mobile banking service. International Research Journal of Management, IT and Social Sciences, 9(4), 482-493.
Rawashdeh, A. M., Elayan, M. B., Alhyasat, W., & Shamout, M. D. (2021). Electronic human resources management perceived usefulness, perceived ease of use and continuance usage intention: The mediating role of user satisfaction in Jordanian hotels sector. International Journal for Quality Research, 15(2), 679.
Ringle, C. M., and Sarstedt. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems. 116: 1865–1886.
Saadé, R. G., & Kira, D. (2007). Mediating the impact of technology usage on perceived ease of use by anxiety. Computers & education, 49(4), 1189-1204.
Saidi, S., Basir, A., Juhaidi, A., & Salabi, A. (2022). Mediating Role of Attitude and Impact of Social Support, Technical Support, And Perceived Ease of Use in Adoption of Technology During COVID-19. Eurasian Journal of Educational Research, 100(100), 1-17.
Shao, C., Nah, S., Makady, H., & McNealy, J. (2024). Understanding User Attitudes Towards AI-Enabled Technologies: An Integrated Model of Self-Efficacy, TAM, and AI Ethics. International Journal of Human–Computer Interaction, 1-13.
Shmueli, G., M. Sarstedt, J.F. Hair, J.-H. Cheah, H. Ting, S. Vaithilingam, and Ringle. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing. 53: 2322–2347.
Shmueli, G., S. Ray, J. M., Velasquez Estrada, and Chatla. (2016). The elephant in the room: predictive performance of PLS models. Journal of Business Research, 69: 4552–4564.
Syaharani, D. P., & Yasa, N. K. (2022). The role of trust as mediation between perceived usefulness and perceived ease of use on repurchase intention. European Journal of Development Studies, 2(3), 36-40.
Toros, E., Asiksoy, G., & Sürücü, L. (2024). Refreshment students’ perceived usefulness and attitudes towards using technology: a moderated mediation model. Humanities and Social Sciences Communications, 11(1), 1-10.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.
Yao, N., & Wang, Q. (2024). Factors influencing pre-service special education teachers’ intention toward AI in education: Digital literacy, teacher self-efficacy, perceived ease of use, and perceived usefulness. Heliyon, 10(14).
Yousaf, U., Ali, S. A., Ahmed, M., Usman, B., & Sameer, I. (2021). From entrepreneurial education to entrepreneurial intention: a sequential mediation of self-efficacy and entrepreneurial attitude. International Journal of Innovation Science, 13(3), 364-380.
Osman, Z. (2025). Attitude as a Catalyst: The Role of Perceived Ease of Use, Perceived Usefulness, and Self-Efficacy in Shaping Student Intentions to Use Artificial Intelligence in Higher Education. International Journal of Academic Research in Accounting, Finance and Management Sciences, 15(1), 201–215.
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