FROM SOCIAL INFLUENCE TO AI ADOPTION IN STEM EDUCATION: THE MEDIATING ROLE OF INTENTION

Authors

  • AMINAH JEKRI Fakulti Pendidikan dan Pengajian Sukan, Universiti Malaysia Sabah, Sabah, Malaysia.
  • CRISPINA GREGORY K HAN Fakulti Pendidikan dan Pengajian Sukan, Universiti Malaysia Sabah, Sabah, Malaysia.
  • NUR FARHA SHAAFI Fakulti Pendidikan dan Pengajian Sukan, Universiti Malaysia Sabah, Sabah, Malaysia.

DOI:

https://doi.org/10.55197/qjssh.v7i4.1295

Keywords:

social influence, behavioral intention, artificial intelligence, mediating role, STEM education

Abstract

The adoption of artificial intelligence (AI) is pivotal in enhancing teaching and learning STEM in secondary education. However, the adoption rate among teacher in Sabah remains inconsistent. Therefore, the main objective of this study is to examine the mediating role of behavioural intention in the relationship between social influence and AI usage for STEM education, using Structural Equation Modelling (SEM). This study employs a quantitative approach, utilizing a cross-sectional survey to gather data from 345 secondary school science teacher in Sabah. Finding indicated that behavioral intention fully mediates the relationship between social influence and AI usage. The mediating role suggests that SI alone will not shape AI usage, rather BI is needed to encourage teachers to use this technology. This research contributes to the existing literature by highlighting the importance of behavioral intention as a mediating factor and contributes practical implications for stakeholders aiming to optimize technology adoption initiatives among science teacher in Sabah.

References

[1] Adelana, O., Ayanwale, M., Sanusi, I. (2024): Exploring pre-service biology teachers' intention to teach genetics using an AI intelligent tutoring-based system. – Cogent Education 11: 26p.

[2] 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: 5187-5208.

[3] Chahal, J., Rani, N. (2022): Exploring the acceptance for e-learning among higher education students in India: Combining technology acceptance model with external variables. – Journal of Computing in Higher Education 34: 844-867.

[4] Guan, L., Zhang, Y.E., Gu, M.Y.M. (2025): Pre-service teachers preparedness for AI-integrated education: An investigation from perceptions, capabilities, and teachers’ identity changes. – Computers and Education: Artificial Intelligence 8: 10p.

[5] Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M. (2022): A primer on partial least squares structural equation modeling (PLS-SEM). – SAGE Publications 384p.

[6] Hallal, K., Hamdan, R., Tlais, S. (2023): Exploring the potential of AI-chatbots in organic chemistry: An assessment of ChatGPT and Bard. – Computers and Education: Artificial Intelligence 5: 8p.

[7] Kim, K., Kwon, K. (2023): Exploring the AI competencies of elementary school teachers in South Korea. – Computers and Education: Artificial Intelligence 4: 11p.

[8] Krejcie, R.V., Morgan, D.W. (1970): Determining sample size for research activities. – Educational and Psychological Measurement 30(3): 607-610.

[9] Li, W., Zhang, X., Li, J., Yang, X., Li, D., Liu, Y. (2024): An explanatory study of factors influencing engagement in AI education at the K-12 level: An extension of the classic TAM model. – Scientific Reports 14: 9p.

[10] Park, J., Teo, T.W., Teo, A., Chang, J., Huang, J.S., Koo, S. (2023): Integrating artificial intelligence into science lessons: Teachers’ experiences and views. – International Journal of STEM Education 10: 22p.

[11] Shakib Kotamjani, S., Shirinova, S., Fahimirad, M. (2023): Lecturers’ perceptions of using artificial intelligence in tertiary education in Uzbekistan. – In Proceedings of the 7th International Conference on Future Networks and Distributed Systems (ICFNDS ’23), ACM, New York 9p.

[12] Songkram, N., Osuwan, H. (2022): Applying the technology acceptance model to elucidate K-12 teachers’ use of digital learning platforms in Thailand during the COVID-19 pandemic. – Sustainability 14(10): 12p.

[13] Venkatesh, V. (2000): Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. – Information Systems Research 11(4): 342-365.

[14] Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D. (2003): User acceptance of information technology: Toward a unified view. – MIS Quarterly 27(3): 425-478.

[15] Wardat, Y., Tashtoush, M.A., AlAli, R., Jarrah, A.M. (2023): ChatGPT: A revolutionary tool for teaching and learning mathematics. – EURASIA Journal of Mathematics, Science and Technology Education 19(7): 18p.

[16] Xue, Y., Wang, Y. (2022): [Retracted] Artificial intelligence for education and teaching. – Wireless Communications and Mobile Computing 2022 10p.

[17] Zhai, J. (2024): The use of roblox in elementary school science education during pandemics. – Open Journal of Social Sciences 12: 462-472.

[18] Zhai, X., Krajcik, J. (Eds.) (2024): Uses of artificial intelligence in STEM education. – Oxford University Press 624p.

Downloads

Published

2026-08-31

Issue

Section

Articles

How to Cite

FROM SOCIAL INFLUENCE TO AI ADOPTION IN STEM EDUCATION: THE MEDIATING ROLE OF INTENTION. (2026). Quantum Journal of Social Sciences and Humanities, 7(4), 546-554. https://doi.org/10.55197/qjssh.v7i4.1295