Acceptance of Generative Artificial Intelligence (GenAI) for Learning Additional Mathematics among Malaysian Secondary Students: A Technology Acceptance Model Approach

Authors

Nur Hidayah Md Noh

Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, Kampus Dungun, 23000 Dungun, Terengganu, Malaysia (Malaysia)

Nur Idalisa Norddin

Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, Kampus Dungun, 23000 Dungun, Terengganu, Malaysia (Malaysia)

Rohayati Mat Ripin

Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, Kampus Dungun, 23000 Dungun, Terengganu, Malaysia (Malaysia)

Zamzulani Mohamed

Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, Kampus Dungun, 23000 Dungun, Terengganu, Malaysia (Malaysia)

Nor Aini Hassanuddin

Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, Kampus Dungun, 23000 Dungun, Terengganu, Malaysia (Malaysia)

Roslina Ramli

Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, Kampus Dungun, 23000 Dungun, Terengganu, Malaysia (Malaysia)

Article Information

DOI: 10.47772/IJRISS.2026.100800166

Subject Category: Education

Volume/Issue: 10/8 | Page No: 2376-2389

Publication Timeline

Submitted: 2026-08-14

Accepted: 2026-08-19

Published: 2026-08-29

Abstract

Generative Artificial Intelligence (GenAI) has increasingly become a learning support tool among secondary-school students, including those studying Additional Mathematics. This study examined the factors influencing Malaysian secondary school students’ behavioural intention to use GenAI for learning Additional Mathematics based on the Technology Acceptance Model (TAM). The study employed a quantitative approach using an online questionnaire administered to 131 secondary students taking Additional Mathematics. The data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The measurement model demonstrated satisfactory reliability and validity, while the structural model showed acceptable predictive performance. The findings indicated that perceived ease of use significantly influenced perceived usefulness, while perceived usefulness significantly affected both attitude and behavioural intention. Attitude also had a significant positive effect on behavioural intention. However, perceived ease of use did not significantly influence attitude. Overall, four of the five proposed relationships were supported, indicating that TAM provides a useful framework for explaining GenAI acceptance among Malaysian secondary students learning Additional Mathematics. The findings further suggest that students’ intention to use GenAI is influenced more strongly by its perceived usefulness and their attitude towards the technology than by ease of use alone. The study provides insights into the factors that may support the effective integration of GenAI into Additional Mathematics learning, particularly among students preparing for the SPM examination.

Keywords

GENERATIVE ARTIFICIAL INTELLIGENCE, TECHNOLOGY ACCEPTANCE MODEL

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References

1. Aineh, M. A., & Ngui, W. (2024). TEACHERS’ AND STUDENTS’ PERCEPTIONS TOWARDS THE USE OF CHATGPT TO IMPROVE WRITING IN THE MALAYSIAN SECONDARY SCHOOL CONTEXT. International Journal on E-Learning Practices (IJELP), 7(1). https://doi.org/10.51200/ijelp.v7i1.5399 [Google Scholar] [Crossref]

2. Al Giffari, H. A., Mayukh, N., Mat Najib, N. A., Chun Ma, X., & Ruslan, N. (2023). EXPLORING IIUM GOMBAK STUDENTS’ PERCEPTION AND ACTUAL USE OF CHATGPT FOR EDUCATIONAL PURPOSES: A QUANTITATIVE STUDY. Journal of Communication Education, 2(37–58). [Google Scholar] [Crossref]

3. Anderson, J. C., & Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411 [Google Scholar] [Crossref]

4. Cain, M. K., Zhiyong Zhang, & Yuan, K.-H. (2017). Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation. Behavior Research Methods, 49(5), 1716–1735. https://doi.org/10.3758/s13428-016-0814-1 [Google Scholar] [Crossref]

5. Chin, W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336. [Google Scholar] [Crossref]

6. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. In Technometrics (Vol. 31, Issue 4). Lawrence Erlbaum. https://doi.org/10.1080/00401706.1989.10488618 [Google Scholar] [Crossref]

7. 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/10.1016/j.cell.2017.08.036 [Google Scholar] [Crossref]

8. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models | Management Science. Management Science, 35(8), 982–1003. [Google Scholar] [Crossref]

9. Demir, B. (2026). Pre-service mathematics teachers’ use of artificial intelligence: An extended technology acceptance model with self-efficacy (the case of ChatGPT). Frontiers in Psychology, 17, 1826980. https://doi.org/10.3389/fpsyg.2026.1826980 [Google Scholar] [Crossref]

10. Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. 41, 1149–1160. [Google Scholar] [Crossref]

11. Foroughi, B., Iranmanesh, M., Ghobakhloo, M., Senali, M. G., Annamalai, N., Naghmeh-Abbaspour, B., & Rejeb, A. (2025). Determinants of ChatGPT adoption among students in higher education: The moderating effect of trust. The Electronic Library, 43(1), 1–21. https://doi.org/10.1108/EL-12-2023-0293 [Google Scholar] [Crossref]

12. Giffari, A., Adha, H., Mayukh, N., Mat Najib, N. A., Chun Ma, X., & Ruslan, N. (2023). Al Giffari, H. A., Mayukh, N., Najib, N. A. M., Ma, X. C., & Ruslan, N. (2023). Exploring Iium Gombak students’ perception and actual use of ChatGPT for educational purposes: A quantitative study. Journal of Communication Education, 2(37–58). https://mysitasi.mohe.gov.my/journal-website/get-meta-article?artId=bec40e05-2e95-11f0-8f88-005056b163df&env=web&jnlId=dfdc23ba-5f76-11ef-a699-005056a6a970&template=_ARTICLE&utm_source=chatgpt.com [Google Scholar] [Crossref]

13. Hair, J., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). [Google Scholar] [Crossref]

14. Henseler, J., Ringle, C., & 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. [Google Scholar] [Crossref]

15. Ibrahim, R., Leng, N. S., Yusoff, R. C. M., Samy, G. N., Masrom, S., & Rizman, Z. I. (2017). E-Learning Acceptance Based on Technology Acceptance Mode (TAM). Journal of Fundamental and Applied Sciences ISSN, 9(4S), 871–889. https://doi.org/10.4314/jfas.v9i4S.50 [Google Scholar] [Crossref]

16. Jamin, N. H., Yahya, H., Mohd Noor, S. S., Shahlan, H., Noh, N., Abdul Rahman, S. M., & Othman, F. Z. (2026). Panduan Literasi Kecerdasan Buatan (AI) Kementerian Pendidikan. Bahagian Sumber dan Teknologi Pendidikan Kementerian Pendidikan. https://www.moe.gov.my/surat-pekeliling-ikhtisas-kpm-ai [Google Scholar] [Crossref]

17. Jen, S. L., & Salam, A. R. (2025). Teaching Secondary School Essay Writing Using Generative AI. International Journal of Research and Innovation in Social Science, IX(IIIS), 6065–6070. https://doi.org/10.47772/IJRISS.2025.903SEDU0438 [Google Scholar] [Crossref]

18. Man, S. C. S., Mosikon, J. @ J., Olaybal, D. L., & Willibrord, O. (2025). AI in Sabah secondary schools: A case study on ethical implications. Jurnal Pemikir Pendidikan, 13(1), 143–156. https://doi.org/10.51200/jpp.v13i1.6922 [Google Scholar] [Crossref]

19. Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research | UNESCO. United Nations Educational, Scientific and Cultural Organization. https://doi.org/https://doi.org/10.54675/EWZM9535 [Google Scholar] [Crossref]

20. Mohamad Zabhi, M. Z. I., & Hassan, M. H. S. (2025). Use of ChatGPT among form Two Students in a Secondary School in Hulu Selangor: A Survey Method. International Journal of Research and Innovation in Social Science (IJRISS), 9(26), 9171–9183. https://doi.org/10.47772/IJRISS.2025.903SEDU0695 [Google Scholar] [Crossref]

21. Ni, A., & Cheung, A. (2023). Understanding secondary students’ continuance intention to adopt AI-powered intelligent tutoring system for English learning. Education and Information Technologies, 28(3), 3191–3216. https://doi.org/10.1007/s10639-022-11305-z [Google Scholar] [Crossref]

22. Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 [Google Scholar] [Crossref]

23. Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt. [Google Scholar] [Crossref]

24. Setälä, M., Heilala, V., Sikström, P., & Kärkkäinen, T. (2025). The Use of Generative Artificial Intelligence for Upper Secondary Mathematics Education Through the Lens of Technology Acceptance. Proceedings of the 40th ACM/SIGAPP Symposium on Applied Computing, 74–82. https://doi.org/10.1145/3672608.3707817 [Google Scholar] [Crossref]

25. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses, H., Babin, B., Money, A. H., & Samouel, P. (2003). Essentials of business research methods. Johns Wiley & Sons. https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Hair%2C+J.+F.+%282003%29.+Essentials+of+Business+Research+Methods&btnG= [Google Scholar] [Crossref]

26. Sun, P., Li, L., Hossain, M. S., & Zabin, S. (2025). Investigating students’ behavioral intention to use ChatGPT for educational purposes. Sustainable Futures, 9, 100531. https://doi.org/10.1016/j.sftr.2025.100531 [Google Scholar] [Crossref]

27. Tiwari, C. K., Bhat, M. A., Khan, S. T., Subramaniam, R., & Khan, M. A. I. (2024). What Drives Students toward ChatGPT? An Investigation of the Factors Influencing Adoption and Usage of ChatGPT. Interactive Technology and Smart Education, 21(3), 333–355. https://doi.org/10.1108/ITSE-04-2023-0061 [Google Scholar] [Crossref]

28. Valle, L., Anwar, M. T., Ton, N. T. H., Osabel, M., Obiasada, R., & Pirnazarov, D. (2026). Mathematics learners’ adoption of generative artificial intelligence: A structural equation modeling approach. Social Sciences & Humanities Open, 13, 103053. https://doi.org/10.1016/j.ssaho.2026.103053 [Google Scholar] [Crossref]

29. Xie, S., Li, S., & Zhang, Q. (2026). Investigating Secondary School Students’ Use of AI-Based Intelligent Technology in Mathematics Learning: A Mixed-Methods Study Using the Technology Acceptance Model. IEEE Access. [Google Scholar] [Crossref]

30. Yap, Y. Y., Tan, S. H., & Tan, B. C. (2025). Beyond the Hype: Generative Artificial Intelligence Adoption and Academic Performance among Higher Education Students. 241–245. https://doi.org/10.1109/ICETC66579.2025.11387548 [Google Scholar] [Crossref]

31. Yilmaz, H., Maxutov, S., Baitekov, A., & Balta, N. (2023). Student’s perception of Chat GPT: A technology acceptance model study. International Educational Review, 1(1), 57–83. [Google Scholar] [Crossref]

32. Yuli Bangun, N., Sujadi, I., Kurniawati, I., Andriatna, R., & Wulandari, A. N. (2025). The emergent role of artificial intelligence in Mathematics education: Examining students’ acceptance and perception. Journal of Educational Management and Instruction (JEMIN), 5(2), 249–264. [Google Scholar] [Crossref]

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