Validating the Acceptance of Artificial Intelligence (AI) in Higher Education Institutions using the Technology Acceptance Model (TAM)

Azza Amir-Rudin; Aiman Haiqah Abdul Hamid; Izzat Ifwat Rosmail; Sharidatul Akma Abu Seman; Norasekin Abd Rashid

doi:10.21834/e-bpj.v10iSI26.6810

Authors

Azza Amir-Rudin Department of Technology and Supply Chain Management Studies, Faculty of Business and Management, Universiti Teknologi MARA, Puncak Alam Shah Alam, Selangor, Malaysia
Aiman Haiqah Abdul Hamid Department of Technology and Supply Chain Management Studies, Faculty of Business and Management, Universiti Teknologi MARA, Puncak Alam Shah Alam, Selangor, Malaysia
Izzat Ifwat Rosmail Department of People and Culture, Carsome Malaysia Head Quarters, Mutiara Damansara, Petaling Jaya, Selangor, Malaysia
Sharidatul Akma Abu Seman Department of Technology and Supply Chain Management Studies, Faculty of Business and Management, Universiti Teknologi MARA, Puncak Alam Shah Alam, Selangor, Malaysia
Norasekin Abd Rashid Department of Technology and Supply Chain Management Studies, Faculty of Business and Management, Universiti Teknologi MARA, Puncak Alam Shah Alam, Selangor, Malaysia

DOI:

https://doi.org/10.21834/e-bpj.v10iSI26.6810

Keywords:

Artificial intelligence, Perceived Usefulness, Perceived Ease-of-Use, Intention.

Abstract

The incorporation of artificial intelligence (AI) into the education system in Malaysia remains unclear. This research will adopt the Technology Acceptance Model (TAM) to assess users’ perceived usefulness, ease of use, and intention to accept AI-powered learning applications. An online survey questionnaire was randomly distributed to students and staff of higher education institutions in Malaysia. Three hundred eighty-three responses were collected for data analysis. The findings revealed that perceived usefulness and perceived ease of use positively influence the intention to use AI in higher education institutions. Furthermore, the intention to use AI influences the acceptability of AI in higher education institutions.

References

Albtoosh, Q. A. A., & Ngah, A. H. (2022). Testing the Expectation Confirmation Theory on the Training Satisfaction Context: The Mediation Role of Mind Wandering. International Journal of Public Administration, 47(1), 26–40. https://doi.org/10.1080/01900692.2022.2081338 DOI: https://doi.org/10.1080/01900692.2022.2081338

Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, 100099. https://doi.org/10.1016/j.caeai.2022.100099 DOI: https://doi.org/10.1016/j.caeai.2022.100099

Aziz, N. A., Rahman, N. H. A., & Hashim, H. (2023). Factors Influencing the Adoption of Artificial Intelligence in Malaysian Higher Education Institutions: A Review. Journal of Education and E-Learning Research, 10(1), 42–49. https://doi.org/10.20448/journal.509.2023.101.42.49

Burton, J. W., Stein, M.-K., & Jensen, T. B. (2020). A systematic review of the impact of artificial intelligence on trust in human-machine relationships. Journal of Management Information Systems, 37(4), 1-37.

Chai, C.S., Wang, X. and Xu, C. (2020). “An extended theory of planned behavior for the modelling of Chinese secondary school students’ intention to learn artificial intelligence”, Mathematics 2020, Vol. 8 No. 11, p. 2089, doi: 10.3390/MATH8112089. DOI: https://doi.org/10.3390/math8112089

Chen, H., & Xu, X. (2023). The digital divide and AI acceptance: Exploring disparities in perceived usefulness across educational contexts. Journal of Educational Technology Research and Development, 71(2), 145-162.

Cheng, Y., Wang, M., & Liu, S. (2022). Personalized learning with AI: Understanding university students' acceptance of AI-powered educational technologies. Computers & Education, 176, 104353.

Choi, Y. (2021). A study of employee acceptance of artificial intelligence technology. European Journal of Management and Business Economics, 30(3), 318–330. https://doi.org/10.1108/ejmbe-06-2020-0158 DOI: https://doi.org/10.1108/EJMBE-06-2020-0158

Darmansyah, F. B. A., Hendratmi, A., & Aziz, P. F. (2020). Factors determining behavioral intentions to use Islamic financial technology: Three competing models. Journal of Islamic Marketing, 12(4), 794–812. https://doi.org/10.1108/JIMA-12- 2019-0252 DOI: https://doi.org/10.1108/JIMA-12-2019-0252

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. DOI: https://doi.org/10.2307/249008

Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019, December). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157–169. https://doi.org/10.1016/j.ijinfomgt.2019.03.008 DOI: https://doi.org/10.1016/j.ijinfomgt.2019.03.008

Hafaz Ngah, A., Jeevan, J., Haqimin, N., Salleh, M., Tae, T., Lee, H., Marsila, S., Ruslan, M. (2020). Willingness to pay for halal transportation cost: The moderating effect of knowledge on the theory of Planned Behavior. J. Environ. Treat. Techniques. http://www.jett.dormaj.com

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/ebr-11-2018-0203 DOI: https://doi.org/10.1108/EBR-11-2018-0203

Halimi, F. F., Gabarre, S., Rahi, S., Al-Gasawneh, J. A., & Ngah, A. H. (2021). Modelling Muslims’ revisit intention of non-halal certified restaurants in Malaysia. Journal of Islamic Marketing, 13(11), 2437–2461. https://doi.org/10.1108/jima-01-2021-0014 DOI: https://doi.org/10.1108/JIMA-01-2021-0014

Hwang, G. J., Sung, H. Y., Chang, S. C., & Huang, X. C. (2020). A fuzzy expert system-based adaptive learning approach to improving students’ learning performances by considering afective and cognitive factors. Computers and Education: Artifcial Intelligence, 1, 100003. DOI: https://doi.org/10.1016/j.caeai.2020.100003

Kashive, N., Powale, L., & Kashive, K. (2020). Understanding user perception toward artificial intelligence (AI) enabled e-learning. International Journal of Information and Learning Technology, 38(1), 1–19. https://doi.org/10.1108/ijilt-05-2020-0090 DOI: https://doi.org/10.1108/IJILT-05-2020-0090

Li, Q., & Zhao, J. (2024). Teachers’ acceptance of AI-driven formative assessment tools in secondary education. Education and Information Technologies, 29(3), 423–441.

Nguyen, T., & Pham, H. (2022). Exploring the role of perceived ease of use in the acceptance of AI-driven language learning applications. Interactive Learning Environments, 30(6), 885-900.

Razia, B., Awwad, B., & Taqi, N. (2022). The relationship between artificial intelligence (AI) and its aspects in higher education. Development in Learning Organizations an International Journal, 37(3), 21–23. https://doi.org/10.1108/dlo-04-2022-0074 DOI: https://doi.org/10.1108/DLO-04-2022-0074