What Does it Take to Trigger Intention to Use Artificial Intelligence among Students in Higher Education Institutions?

Zahir Osman; Ratna Khuzaimah  Mohamad; Nadzurah  Kasbun

The increasing integration of Artificial Intelligence (AI) in higher education institutions necessitates a student prepared for this transformative change. This study investigates the factors influencing students' intention to use AI tools in their study. Drawing upon the Technology Acceptance Model (TAM), the research aims to understand how perceived ease of use, and perceived usefulness impact students' intention to use with attitude, and self-efficacy as mediators. Data collection employed a survey instrument distributed to a sample of 319 students from public and private higher education institutions. The survey measured participants' perceptions of AI ease of use, perceived usefulness, attitude towards AI, self-efficacy, and intention to use AI tools in their study. Statistical analysis utilized Partial Least Squares (PLS) to assess the relationships between the proposed variables and test the formulated hypotheses. The results of the hypothesis testing confirmed the positive influence of perceived ease of use and perceived usefulness on students' intention to use AI tools, aligning with TAM principles. Furthermore, the study revealed that attitude and self-efficacy act as mediating factors, bridging the gap between perceived ease of use and perceived usefulness and intention to use. These findings suggest that beyond just the technical aspects of AI, students' perceptions, attitudes, and confidence levels significantly influence their willingness to use AI in their study. The study's implications are significant for organizations implementing AI. By prioritizing the user-centered design of AI tools, emphasizing training and skill development to enhance perceived ease of use, and communicating the benefits of AI to address perceived usefulness, organizations can foster a more positive student attitude towards AI. Additionally, promoting a culture of learning and support can boost student’s self-efficacy and ultimately encourage wider usage of AI tools within the organization.

Acosta-Enriquez, B. G., Arbulú Ballesteros, M. A., Huamaní Jordan, O., López Roca, C., & Saavedra Tirado, K. (2024). Analysis of college students' attitudes toward the use of ChatGPT in their academic activities: effect of intent to use, verification of information and responsible use. BMC psychology, 12(1), 255.
AlGerafi, M. A., Zhou, Y., Alfadda, H., & Wijaya, T. T. (2023). Understanding the factors influencing higher education students’ intention to adopt artificial intelligence-based robots. IEEE Access.
An, X., Chai, C. S., Li, Y., Zhou, Y., Shen, X., Zheng, C., & Chen, M. (2023). Modeling English teachers’ behavioral intention to use artificial intelligence in middle schools. Education and Information Technologies, 28(5), 5187-5208.
Bonsu, E. M., & Baffour-Koduah, D. (2023). From the consumers’ side: Determining students’ perception and intention to use ChatGPT in Ghanaian higher education. Journal of Education, Society & Multiculturalism, 4(1), 1-29.
Chai, C. S., Lin, C. Y., & Lai, Y. L. (2020). The effects of learner self-efficacy and perceived ease of use on behavioral intention to use mobile learning technologies. Journal of Educational Technology Development and Exchange (JETDE), 13(2), 141-156.
Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K., & Huang, B. (2020, August). Factors influencing students' behavioral intention to continue artificial intelligence learning. In 2020 international symposium on educational technology (ISET) (pp. 147-150). IEEE.
Chai, C. S., Lin, P. Y., Jong, M. S. Y., Dai, Y., Chiu, T. K., & Qin, J. (2021). Perceptions of and behavioral intentions towards learning artificial intelligence in primary school students. Educational Technology & Society, 24(3), 89-101.
Chai, C. S., Wang, X., & Xu, C. (2020). An extended theory of planned behavior for the modelling of Chinese secondary school students’ intention to learn artificial intelligence. Mathematics, 8(11), 2089.
Chatterjee, S., & Bhattacharjee, K. K. (2020). Usage of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463.
Chen, M., Siu-Yung, M., Chai, C. S., Zheng, C., & Park, M. Y. (2021, August). A Pilot Study of Students’ Behavioral Intention to Use AI for Language Learning in Higher Education. In 2021 International Symposium on Educational Technology (ISET) (pp. 182-184). IEEE.
Chen, Y., Li, N., & Xie, J. (2022). University students' perceived service quality and attitude towards hybrid learning: Ease of use and usefulness as mediators. International Journal of Educational Development, 100, 105083.
https://dl.acm.org/doi/fullHtml/10.1145/3446590.3446595
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 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., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3 ed.). Thousand Oaks, CA: Sage.
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.
Huang, Y. (2020b). The influence of self-efficacy, perceived usefulness, perceived ease of use, and cognitive load on students' learning motivation, learning attitude, and learning satisfaction in blended learning methods. International Journal of Educational Technology in Higher Education, 17(1), 1-17.
https://dl.acm.org/doi/fullHtml/10.1145/3446590.3446595
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.
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
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.
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach.
International Journal of e-Collaboration, 11(4), 1-10.
Kwak, Y., Seo, Y. H., & Ahn, J. W. (2022). Nursing students' intent to use AI-based healthcare technology: Path analysis using the unified theory of acceptance and use of technology. Nurse Education Today, 119, 105541.
Lavidas, K., Voulgari, I., Papadakis, S., Athanassopoulos, S., Anastasiou, A., Filippidi, A., ... & Karacapilidis, N. (2024). Determinants of Humanities and Social Sciences Students’ Intentions to Use Artificial Intelligence Applications for Academic Purposes. Information, 15(6), 314.
Li, L., Zhao, Y., & Liu, X. (2020). Analyzing Students' Attitudes and Behavior toward Artificial Intelligence Technologies in Higher Education. International Journal of Recent Technology and Engineering (IJRTE), 9(2 Special Issue 4), 1081-1085.
Li, X., Jiang, M. Y. C., Jong, M. S. Y., Zhang, X., & Chai, C. S. (2022). Understanding medical students’ perceptions of and behavioral intentions toward learning artificial intelligence: A survey study. International Journal of Environmental Research and Public Health, 19(14), 8733.
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.
Lu, H., Liu, C., Yu, N., & Zhao, Y. (2023). Determinants of College Students' Actual Use of AI-Based Systems: An Extension of the Technology Acceptance Model. Sustainability, 15(6), 4024.
Ma, S., & Lei, L. (2024). The factors influencing teacher education students’ willingness to adopt artificial intelligence technology for information-based teaching. Asia Pacific Journal of Education, 44(1), 94-111.
Milicevic, N., Kalas, B., Djokic, N., Malcic, B., & Djokic, I. (2024). Students’ Intention toward Artificial Intelligence in the Context of Digital Transformation. Sustainability, 16(9), 3554.
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.
Park, D. Y., & Kim, H. (2023). Determinants of intentions to use Digital mental Healthcare content among university students, faculty, and staff: motivation, perceived usefulness, perceived ease of use, and Parasocial interaction with AI Chatbot. Sustainability, 15(1), 872.
Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A. E., & Bañeres, D. (2022). Applying the UTAUT model to explain the students' acceptance of an early warning system in Higher Education. Computers & Education, 182, 104468.
Ringle, C. M., and Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The
importance-performance map analysis. Industrial Management & Data Systems. 116: 1865–1886.
Ringle, Christian M., Wende, Sven, & Becker, Jan-Michael. (2022). SmartPLS 4. Oststeinbek:
SmartPLS. Retrieved from https://www.smartpls.com
Roy, R., Babakerkhell, M. D., Mukherjee, S., Pal, D., & Funilkul, S. (2022). Evaluating the intention for the usage of artificial intelligence-based robots in the university to educate the students. IEEE Access, 10, 125666-125678.
Sangapu, H. Y. (2020). Students' Perceptions of the Use of Artificial Intelligence in Educational Service. Amfiteatru Economic, 22(53), 544-558.
Strzelecki, A. (2023). To use or not to use ChatGPT in higher education? A study of students’ acceptance and use of technology. Interactive Learning Environments, 1-14.
Sudaryanto, M. R., Hendrawan, M. A., & Andrian, T. (2023). The Effect of Technology Readiness, Digital Competence, Perceived Usefulness, and Ease of Use on Accounting Students Artificial Intelligence Technology Usage. In E3S Web of Conferences (Vol. 388, p. 04055). EDP Sciences.
Sun, P., & Yeh, Y. (2019). Students' acceptance of educational artificial intelligence: A Taiwanese perspective. Journal of Educational Technology Development and Exchange (JETDE), 12(3), 197-210.
Tan, J. P. L., Teo, T., & Hew, K. F. (2018). Factors influencing technology acceptance in e-learning: A review of the literature. Education and Information Technologies, 23(3), 1245-1280
Van der Meijden, H., & De Croon, E. (2019). The role of knowledge, attitudes, and perceived behavioral control in young adults' intentions to use a smartphone app to improve their mental health. Computers in Human Behavior, 93, 222-232.
Vieira, D. C., & Almeida, V. G. de A. (2021). University students' perception of artificial intelligence in education. International Journal of Educational Technology and Higher Education, 19(1). [DOI: 10.18039/ijethe.v19i1.2301]
Vinichenko, M. V., Melnichuk, A. V., Karácsony, P. (2020). Technologies of improving the university efficiency by using artificial intelligence: Motivational aspect. Entrep. Sustain., 7, 2696–2714.
Wang, F., King, R. B., Chai, C. S., & Zhou, Y. (2023). University students’ intentions to learn artificial intelligence: the roles of supportive environments and expectancy–value beliefs. International Journal of Educational Technology in Higher Education, 20(1), 51.
Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors affecting the usage of AI-based applications in higher education. Educational Technology & Society, 24(3), 116-129.
Wu, Y., Liu, X., Wang, Z., & Chen, J. (2020). University students' perceptions of artificial intelligence in English learning: A case study in China. International Journal of Emerging Technologies in Learning (IJETL), 15(11), 182-194.
Xu, D., & Zhao, J. L. (2018). A framework for analyzing the acceptance of learning analytics. Computers & Education, 122, 435-449.
Zhang, Y., Zhao, J., Li, J., & Sun, P. (2022). Exploring the factors influencing university students' intention to use AI-powered writing assistants. Educational Technology Research and Development, 70(3), 915-933.
Zhang, Y., Zhao, J., Li, J., & Sun, P. (2023). University Students' Perceived Usefulness and Ease of Use of AI-Powered Writing Assistants: A Moderated Mediation Model. Sustainability, 15(3), 1822.
Zhang, Y., Zhao, J., Li, J., & Sun, P. (2023). University Students' Perceived Usefulness and Ease of Use of AI-Powered Writing Assistants: A Moderated Mediation Model. Sustainability, 15(3), 1822.

(Osman et al., 2024)
Osman, Z., Mohamad, R. K., & Kasbun, N. (2024). What Does it Take to Trigger Intention to Use Artificial Intelligence among Students in Higher Education Institutions? International Journal of Academic Research in Business and Social Sciences, 14(7), 1412–1429.

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What Does it Take to Trigger Intention to Use Artificial Intelligence among Students in Higher Education Institutions?

Zahir Osman, Ratna Khuzaimah Mohamad, Nadzurah Kasbun

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