Determinants of Artificial Intelligence and Its Effects on Learning Motivation among Students in Higher Education Institutions
Keywords:
attitudes, artificial intelligence, Expectancy Theory, motivation to learn, opportunity to use, perceived importanceAbstract
Artificial intelligence (AI) is widely acknowledged for its capacity to revolutionise higher education by enabling more dynamic, personalised, and efficient learning environments. This study examines the influence of opportunities to utilise AI, the perceived significance of AI, and students’ attitudes towards AI on their motivation to learn. Data were collected from 208 students at Universiti Teknologi MARA (UiTM) Kelantan Branch using a quantitative cross-sectional survey design, based on a targeted sample size of 331. The findings reveal significant positive relationships between students’ attitudes, the perceived importance of AI, and their motivation to engage with it in their learning process. The opportunity to utilise AI also emerged as a notable factor in enhancing student motivation. Interpreted through the lens of Expectancy Theory, the results suggest that students are more motivated when they believe their efforts will lead to improved learning outcomes (expectancy), that these outcomes are attainable (instrumentality), and that they are personally meaningful (valence). The effective integration of AI in education requires careful consideration of factors such as faculty preparedness, equitable access, and ethical implications. Institutions must ensure that AI adoption extends beyond mere technological implementation and instead supports students’ values, promotes inclusivity, and cultivates an engaging learning environment. This study highlights the importance of a strategic, theory-informed approach to AI integration to enhance student engagement and academic success in higher education.
References
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43.
Chiu, T. K. F. (2021). Digital support for student engagement in blended learning based on self-determination theory. Computers in Human Behavior, 124, 106909. https://doi.org/10.1016/j.chb.2021.106909
Chiu, T. K. F. (2022). Applying the Self-determination Theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(sup1), 14–30. https://doi.org/ 10.1080/15391523.2021.1891998
Chiu, T. K., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student motivation to learn with an Artificial Intelligence (AI) based chatbot. Interactive Learning Environments, 1-17.
Chiu, T. K., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118.
Dawes, J. (2008). Do data characteristics change according to the number of scale points used? An experiment using 5-point, 7-point and 10-point scales. International Journal of Market Research, 50(1), 61-104. https://doi.org/10.1177/147078530805000106
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Ebadi, S., & Amini, A. (2024). Examining the roles of social presence and human-likeness on Iranian EFL learners’ motivation using artificial intelligence technology: A case of CSIEC chatbot. Interactive Learning Environments, 32(2), 655-673.
Ehsan, U., Passi, S., Liao, Q. V., Chan, L., Lee, I., Muller, M., & Riedl, M. O. (2021). The Who in explainable AI: How AI background shapes perceptions of AI explanations. Arxiv.
eSchool News. (2024, February 5). What are the Benefits and Risks of Artificial Intelligence in Education? ESchool News. https://www.eschoolnews.com/digital-learning/2024/02/05/what-are-the-benefits-and-risks-of-artificial-intelligence-in-education/
Fahmy, Y. (2024). Student perception on AI-driven assessment: motivation, engagement and feedback capabilities (Bachelor's thesis, University of Twente). https://purl.utwente.nl/essays/100985
Fosner, A. (2024). University students’ attitudes and perceptions towards AI tools: implications for sustainable educational practices. Sustainability, 16(19), 8668.
Hsu, Y. C., & Ching, Y. H. (2023). Generative Artificial Intelligence in Education, Part Two: International Perspectives. TechTrends, 67(6), 885-890.
https://doi.org/10.24059/olj.v24i2.2053
Huang, F., Wang, Y., & Zhang, H. (2024). Modelling Generative AI Acceptance, Perceived Teachers' Enthusiasm and Self‐Efficacy to English as a Foreign Language Learners' Well‐Being in the Digital Era. European Journal of Education, e12770.
Hunziker, S., & Blankenagel, M. (2024). Cross-sectional research design. In Research design in business and management: A practical guide for students and researchers (pp. 187-199). Wiesbaden: Springer Fachmedien Wiesbaden.
Hung, C. M., Hwang, G. J., & Huang, I. (2012). A project-based digital storytelling approach for improving students' learning motivation, problem-solving competence and learning achievement. Journal of Educational Technology & Society, 15(4), 368-379.
Jia, X. H., & Tu, J. C. (2024). Towards a New Conceptual Model of AI-Enhanced Learning for College Students: The Roles of Artificial Intelligence Capabilities, General Self-Efficacy, Learning Motivation, and Critical Thinking Awareness. Systems, 12(3), 74.
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.
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.
Krejcie, R. V. and Morgan, D. W. (1970). Table for determining sample size from a given population. Educational and Psychological Measurement, 30(3), 607-610.
Lim, C. P., Liew, S. K., & Hashim, H. (2023). Attitudes toward AI-assisted learning: A Malaysian perspective. Malaysian Journal of Learning and Instruction, 20(1), 103–121. https://doi.org/10.32890/mjli2023.20.1.5
Lin, P. Y., Chai, C. S., Jong, M. S. Y., Dai, Y., Guo, Y., & Qin, J. (2021). Modelling the structural relationship among primary students’ motivation to learn artificial intelligence. Computers and Education: Artificial Intelligence, 2, 100006.
Lu, G., Hussin, N. B., & Sarkar, A. (2024, May). Navigating the future: Harnessing artificial intelligence-generated content (AIGC) for enhanced learning experiences in higher education. In 2024 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE) (pp. 1-12). IEEE.
Lunenburg, F. C. (2011). Expectancy theory of motivation: Motivating by altering expectations. International Journal of Management, Business, and Administration, 15(1), 1–6.
Marr, B. (2021, July 2). Understanding the 4 Types of Artificial Intelligence. Bernard Marr. https://bernardmarr.com/understanding-the-4-types-of-artificial-intelligence/
Martin, F., Stamper, B., & Flowers, C. (2020). Examining student perception of their readiness for online learning: Importance and confidence. Online Learning, 24(2), 38-58.
Moybeka, A. M., Syariatin, N., Tatipang, D. P., Mushthoza, D. A., Dewi, N. P. J. L., & Tineh, S. (2023). Artificial Intelligence and English classroom: the implications of AI toward EFL students’ motivation. Edumaspul: Jurnal Pendidikan, 7(2), 2444-2454.
Murakami, Y., Sho, Y., & Inagaki, T. (2024). Improving motivation in learning AI for undergraduate students by case study. Journal of Information Processing, 32, 175-181.
Noe, R. A. and Schmitt, N. (1986). The influence of trainee attitudes on training effectiveness: Test of a model. Personnel psychology, 39(3), 497-523.
Nunnally, J. C. & Bernstein, I. H. (1994). Psychometric theory (3rd Ed.). New York: McGraw-Hill
Reeve, J. (2024). Understanding motivation and emotion. John Wiley & Sons.
Richardson, J. T. (2005). Instruments for obtaining student feedback: A review of the literature. Assessment & evaluation in higher education, 30(4), 387-415.
Rizvi, Samreen. (2023). Revolutionizing Student Engagement: Artificial Intelligence’s Impact on Specialized Learning Motivation. International Journal of Advanced Engineering Research and Science. 10. 10.22161/ijaers.109.4.
Roscoe, J. T. (1975). Fundamental research statistics for the behavioural sciences [by] John T. Roscoe. New York, NY: Holt, Rinehart and Winston.
Ryan, R. M., & Vansteenkiste, M. (2023). Self-determination theory. In The Oxford Handbook of Self-Determination Theory (pp. 3-30). Oxford University Press.
Ryan, R. M., Duineveld, J. J., Di Domenico, S. I., Ryan, W. S., Steward, B. A., & Bradshaw, E. L. (2022). We know this much is (meta-analytically) true: A meta-review of meta-analytic findings evaluating self-determination theory. Psychological Bulletin, 148(11-12), 813.
Salloum, S. A., Al-Emran, M., & Shaalan, K. (2023). Artificial intelligence and student engagement: A systematic review. Education and Information Technologies, 28, 1–22. https://doi.org/10.1007/s10639-022-11118-5
Sharma, C., & Ojha, C. S. P. (2020). Statistical parameters of hydrometeorological variables: Standard deviation, SNR, skewness and kurtosis. In Advances in Water Resources Engineering and Management (pp. 59-70). Springer: Singapore. DOI: 10.1007/978-981-13-8181-2_5
Soria, K. M., Chirikov, I., & Jones-White, D. (2020). The obstacles to remote learning for undergraduate, graduate, and professional students. SERU Consortium, University of California - Berkeley and University of Minnesota. https://cshe.berkeley.edu/serucovid-survey-report
Stosic, M. D., Murphy, B. A., Duong, F., Fultz, A. A., Harvey, S. E., & Bernieri, F. (2024). Careless responding: Why many findings are spurious or spuriously inflated. Advances in Methods and Practices in Psychological Science, 7(1), 25152459241231581.
Tracey, J. B., Tannenbaum, S. I. and Kavanagh, M. J. (1995). Applying trained skills on the job: The importance of the work environment. Journal of Applied Psychology, 80(2), 239.
Velada, R., & Caetano, A. (2007). Training transfer: The mediating role of perception of learning. Journal of European Industrial Training, 31(4), 283–296. https://doi.org/10.1108/03090590710746441
Wang, Y. Y., & Wang, Y. S. (2022). Development and validation of an artificial intelligence anxiety scale: An initial application in predicting motivated learning behavior. Interactive Learning Environments, 30(4), 619-634.
Yi, M. Y. and Davis, F. D. (2003). Developing and validating an observational learning model of computer software training and skill acquisition. Information Systems Research, 14(2), 146-169.Chomeya, R. (2010). Quality of psychology test between Likert scale 5 and 6 points. Journal of Social Sciences, 6(3), 399-403. https://thescipub.com/abstract/10.3844/jssp.2010.399.403
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education—Where are the educators? International Journal of Educational Technology in Higher Education, 16, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Zhang, H., Lee, I., Ali, S., DiPaola, D., Cheng, Y., & Breazeal, C. (2023). Integrating ethics and career futures with technical learning to promote AI literacy for middle school students: An exploratory study. International Journal of Artificial Intelligence in Education, 33(2), 290-324.
Zou, D., Zhang, R., Xie, H., & Wang, F. L. (2021). Digital game-based learning of information literacy: Effects of gameplay modes on university students’ learning performance, motivation, self-efficacy and flow experiences. Australasian Journal of Educational Technology, 37(2), 152-170
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Copyright (c) 2025 Nik Sarina Nik Md Salleh, Noorazzila Shamsuddin, Norshamsiah Ibrahim, Roseliza Hamid, Sakinah Mat Zin, Nazatul Shahreen Zainal Abidin
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