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International Journal of Academic Research in Accounting, Finance and Management Sciences

Open Access Journal

ISSN: 2225-8329

Optimizing Artificial Intelligence Usage among Academicians in Higher Education Institutions

Zahir Osman, Noral Hidayah Alwi, Khairul Hamimah Mohamad Jodi, Bibi Nabi Ahmad Khan, Mohammad Naim Ismail, Yuzery Yusoff

http://dx.doi.org/10.6007/IJARAFMS/v14-i2/20935

Open access

Artificial Intelligence (AI) is becoming a transformative force in higher education and offers tremendous potential to reshape the academic landscape. With the power of artificial intelligence, educators and researchers can use advanced tools to perform complex tasks such as data analysis, predictive modeling, and breakthrough insights. This study delves into the complex relationships between attitudes, perceived usefulness, perceived ease of use, trust, intentions, and AI usage in academia. The research framework is based on four different variables: attitude, perceived usefulness, perceived ease of use, and trust, with intention as the mediator and AI use as the outcome variable. To collect primary data, a thorough survey was designed and conducted based on previous research. Structural equation modeling, known for its ability to analyze complex interactions between variables, was used to analyze a comprehensive data set of 362 responses, and convergent and discriminant validity was confirmed. Evaluation of the structural model crucially confirmed the hypotheses and revealed nine direct relationships and four mediated relationships. Notably, seven of the nine direct hypotheses and all four mediating hypotheses were supported. These results highlight the profound importance of these factors in shaping user intentions and facilitating the effective integration of AI into academia. In addition to the empirical findings, this study contains very important theoretical implications, highlighting the central role that attitudes, perceived usefulness, perceived ease of use, and trust play in influencing intentions and subsequent behavior related to AI integration. As educators around the world work to integrate artificial intelligence into their teaching and research, the relevance of this research extends to actionable insights that can inform policymaking, curriculum development, and the development of educational paradigms.

Ahmed, S., Khalil, M. I., Chowdhury, B., Haque, R., bin S Senathirajah, A. R., & bin Omar Din,
F. M. (2022). Motivators and barriers of artificial intelligent (AI) based teaching. Eurasian Journal of Educational Research, 100(100), 74-89.
Ahmad, S. F., Rahmat, M. K., Mubarik, M. S., Alam, M. M., & Hyder, S. I. (2021). Artificial intelligence and its role in education. Sustainability, 13(22), 12902.
Alam, A. (2022). Employing adaptive learning and intelligent tutoring robots for virtual classrooms and smart campuses: reforming education in the age of artificial intelligence. In Advanced Computing and Intelligent Technologies: Proceedings of ICACIT 2022 (pp. 395-406). Singapore: Springer Nature Singapore.
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.
Andrews, J. E., Ward, H., & Yoon, J. (2021). UTAUT as a model for understanding intention to
adopt AI and related technologies among librarians. The Journal of Academic Librarianship, 47(6), 102437.
Ashaari, M. A., Singh, K. S. D., Abbasi, G. A., Amran, A., & Liebana-Cabanillas, F. J. (2021). Big
data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: A multi-analytical SEM & ANN perspective. Technological Forecasting and Social Change, 173, 121119.
Bearman, M., Ryan, J., & Ajjawi, R. (2023). Discourses of artificial intelligence in higher education: A critical literature review. Higher Education, 86(2), 369-385.
Bhattacharjee, K. K. (2019). Research output on the usage of artificial intelligence in Indian higher education-A scientometric study. In 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 916-919). IEEE.
Chaudhry, M. A., & Kazim, E. (2022). Artificial Intelligence in Education (AIEd): A high-level
academic and industry note 2021. AI and Ethics, 1-9.
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463.
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8,
75264-75278.
Chiu, T. K., & Chai, C. S. (2020). Sustainable curriculum planning for artificial intelligence education: A self-determination theory perspective. Sustainability, 12(14), 5568.
Choi, S., Jang, Y., & Kim, H. (2023). Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. International Journal of Human–Computer Interaction, 39(4), 910-922.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of
information technology. MIS Quarterly, 319-340.
Gupta, P., & Yadav, S. (2022). A TAM-based Study on the ICT Usage by the Academicians in Higher Educational Institutions of Delhi NCR. In Congress on Intelligent Systems: Proceedings of CIS 2021, Volume 2 (pp. 329-353). Singapore: Springer Nature Singapore.
Gurung, L., & Ashmita, K. C. (2023). Considerations of Artificial Intelligence for Higher Education and Implications of ChatGPT for Teaching and Learning. Journal of Education and Research, 13(1), 1-7.
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., 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.
Jasielska, D., Rogoza, R., Zajenkowska, A. et al. (2021). General trust scale: Validation in cross-cultural settings. Curr Psychol 40, 5019–5029. https://doi.org/10.1007/s12144-019-00435-2
Jokhan, A., Chand, A. A., Singh, V., & Mamun, K. A. (2022). Increased digital resource
consumption in higher educational institutions and the artificial intelligence role in informing decisions related to student performance. Sustainability, 14(4), 2377.
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.
Khalid, N. (2020). Artificial intelligence learning and entrepreneurial performance among
university students: evidence from malaysian higher educational institutions. Journal of Intelligent & Fuzzy Systems, 39(4), 5417-5435.
Kock, N. (2015) Common method bias in PLS-SEM: A full collinearity assessment approach, International Journal of e-Collaboration, 11(4), 1-10
Kuleto, V., Ili?, M., Dumangiu, M., Rankovi?, M., Martins, O. M., P?un, D., & Mihoreanu, L.
(2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424.
Lew, S. L., Lau, S. H., & Leow, M. C. (2019). Usability factors predicting continuance of
intention to use cloud e-learning application. Heliyon, 5(6).
Li, X., Du, J., Long, H. (2020) Mechanism for green development behavior and performance of
industrial enterprises (GDBP-IE) using partial least squares structural equation modeling (PLS-SEM). Int J Environ Res Public Health 17(22):8450. https://doi.org/10.1007/s11356-018-04090-1
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.
Mohanachandran, D. K., Yap, C. T., Ismaili, Z., & Govindarajo, N. S. (2021). Smart university
and artificial intelligence. In The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success (pp. 255-279). Cham: Springer International Publishing.
Pisica, A. I., Edu, T., Zaharia, R. M., & Zaharia, R. (2023). Implementing Artificial Intelligence in
Higher Education: Pros and Cons from the Perspectives of Academics. Societies, 13(5), 118.
Qin, F., Li, K., & Yan, J. (2020). Understanding user trust in artificial intelligence?based
educational systems: Evidence from China. British Journal of Educational Technology, 51(5), 1693-1710.
Rahim, N. I. M., Iahad, N. A., Yusof, A. F., & Al-Sharafi, M. A. (2022). AI-Based chatbots
adoption model for higher-education institutions: A hybrid PLS-SEM-Neural network modelling approach. Sustainability, 14(19), 12726.
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.
Sánchez-Prieto, J. C., Cruz-Benito, J., Therón Sánchez, R., & García-Peñalvo, F. J. (2020).
Assessed by machines: Development of a TAM-based tool to measure AI-based assessment acceptance among students. International Journal of Interactive Multimedia and Artificial Intelligence, 6(4), 80.
Sharawy, F. S. (2023). The Use of Artificial Intelligence in Higher Education: A Study on Faculty
Perspectives in Universities in Egypt.
Sinniah, S., Al Mamun, A., Md Salleh, M. F., Makhbul, Z. K. M., & Hayat, N. (2022). Modeling
the significance of motivation on job satisfaction and performance among the academicians: the use of hybrid structural equation modeling-artificial neural network analysis. Frontiers in Psychology, 13, 935822.
Wang, S., Sun, Z., & Chen, Y. (2023). Effects of higher education institutes’ artificial intelligence
capability on students' self-efficacy, creativity and learning performance. Education and Information Technologies, 28(5), 4919-4939.
Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors affecting the adoption of AI-based applications in
higher education. Educational Technology & Society, 24(3), 116-129.
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial
intelligence among pre-service teachers: a multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), 49.
Kock, N. (2015) Common method bias in PLS-SEM: A full collinearity assessment approach, International Journal of e-Collaboration, 11(4), 1-10
Kuleto, V., Ili?, M., Dumangiu, M., Rankovi?, M., Martins, O. M., P?un, D., & Mihoreanu, L.
(2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424.
Lew, S. L., Lau, S. H., & Leow, M. C. (2019). Usability factors predicting continuance of
intention to use cloud e-learning application. Heliyon, 5(6).
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.
Mohanachandran, D. K., Yap, C. T., Ismaili, Z., & Govindarajo, N. S. (2021). Smart university
and artificial intelligence. In The Fourth Industrial Revolution: Implementation of Artificial Intelligence for Growing Business Success (pp. 255-279). Cham: Springer International Publishing.
Pisica, A. I., Edu, T., Zaharia, R. M., & Zaharia, R. (2023). Implementing Artificial Intelligence in
Higher Education: Pros and Cons from the Perspectives of Academics. Societies, 13(5), 118.
Qin, F., Li, K., & Yan, J. (2020). Understanding user trust in artificial intelligence?based
educational systems: Evidence from China. British Journal of Educational Technology, 51(5), 1693-1710.
Rahim, N. I. M., Iahad, N. A., Yusof, A. F., & Al-Sharafi, M. A. (2022). AI-Based chatbots
adoption model for higher-education institutions: A hybrid PLS-SEM-Neural network modelling approach. Sustainability, 14(19), 12726.
Ringle, C. M., andSarstedt, M. (2016). Gain more insight from your PLS-SEM results: The
importance-performance map analysis. Industrial Management & Data Systems 116: 1865–1886.
Sánchez-Prieto, J. C., Cruz-Benito, J., Therón Sánchez, R., & García-Peñalvo, F. J. (2020).
Assessed by machines: Development of a TAM-based tool to measure AI-based assessment acceptance among students. International Journal of Interactive Multimedia and Artificial Intelligence, 6(4), 80.
Sharawy, F. S. (2023). The Use of Artificial Intelligence in Higher Education: A Study on Faculty
Perspectives in Universities in Egypt.
Shmueli, G., M. Sarstedt, J.F. Hair, J.-H. Cheah, H. Ting, S. Vaithilingam, and C.M. Ringle.
(2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict.
European Journal of Marketing 53: 2322–2347.
Sinniah, S., Al Mamun, A., Salleh, M. F., Makhbul, Z. K. M., & Hayat, N. (2022). Modeling
the significance of motivation on job satisfaction and performance among the academicians: the use of hybrid structural equation modeling-artificial neural network analysis. Frontiers in Psychology, 13, 935822.
Wang, S., Sun, Z., & Chen, Y. (2023). Effects of higher education institutes’ artificial intelligence
capability on students' self-efficacy, creativity and learning performance. Education and Information Technologies, 28(5), 4919-4939.
Wang, Y., Liu, C., & Tu, Y. F. (2021). Factors affecting the adoption of AI-based applications in
higher education. Educational Technology & Society, 24(3), 116-129.
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial
intelligence among pre-service teachers: a multigroup analysis. International Journal of Educational Technology in Higher Education, 20(1), 49.

(Osman et al., 2024)
Osman, Z., Alwi, N. H., Jodi, K. H. M., Khan, B. N. A., Ismail, M. N., & Yusoff, Y. (2024). Optimizing Artificial Intelligence Usage among Academicians in Higher Education Institutions. International Journal of Academic Research in Accounting Finance and Management Sciences, 14(2), 1–19.