AI ACCEPTANCE AND USE IN MATHEMATICS PRE-SERVICE TEACHERS: A THEORY OF PLANNED BEHAVIOUR APPROACH
DOI:
https://doi.org/10.35334/yn58ya38Keywords:
Artificial Intelligence Acceptance, Artificial Intelligence UseAbstract
Artificial Intelligence (AI) is increasingly integrated into education, including mathematics learning, yet its successful implementation largely depends on teachers’ acceptance and readiness. This study aims to examine the determinants of AI acceptance and use among pre-service mathematics teachers by employing the Theory of Planned Behaviour (TPB), which comprises Attitude (AT), Social Norms (SN), Perceived Behavioral Control (PBC), Behavioral Intention (BI), and AI Use (AIU). A quantitative, cross-sectional survey design was used. Data were collected from 427 Generation Z pre-service mathematics teachers enrolled in three Indonesian universities using an online questionnaire. The instrument was adapted from a previously validated TPB-based scale, and data were analyzed using PLS-SEM with SmartPLS. The measurement model demonstrated excellent reliability and validity, and the structural model indicated very good fit (SRMR = 0.044). All hypothesized relationships were supported (p < 0.001). SN emerged as the strongest predictor of BI, followed by AT and PBC, while BI was the most powerful determinant of AIU (β = 0.849). These findings highlight the central role of social influence, positive attitudes, and technological efficacy in shaping pre-service teachers’ intentions and actual use of AI. The study implies that teacher education programs should design courses and practicum experiences that foster positive beliefs about AI, enhance digital competence, and leverage supportive social environments to promote meaningful AI integration in mathematics classrooms.
Keywords: Artificial Intelligence Acceptance, Artificial Intelligence Use, Educational technology, Mathematics Pre-service Teacher, Theory of Planned Behaviour,
Artificial Intelligence (AI) semakin banyak dimanfaatkan dalam pendidikan, termasuk pada pembelajaran matematika, namun keberhasilan integrasinya sangat bergantung pada penerimaan dan kesiapan calon guru. Penelitian ini bertujuan menganalisis faktor-faktor yang memengaruhi penerimaan dan penggunaan AI pada calon guru matematika menggunakan kerangka Theory of Planned Behaviour (TPB), yang mencakup Attitude (AT), Social Norms (SN), Perceived Behavioral Control (PBC), Behavioral Intention (BI) dan AI Use (AIU). Penelitian ini menggunakan pendekatan kuantitatif dengan desain cross-sectional. Sebanyak 427 calon guru matematika generasi Z dari tiga universitas di Indonesia berpartisipasi melalui pengisian kuesioner daring. Instrumen diadaptasi dari penelitian terdahulu berbasis TPB dan dianalisis menggunakan PLS-SEM melalui SmartPLS. Hasil menunjukkan bahwa model pengukuran memiliki reliabilitas dan validitas yang sangat baik, serta model struktural menunjukkan kelayakan yang tinggi (SRMR = 0,044). Semua hipotesis didukung secara signifikan (p < 0,001). SN muncul sebagai prediktor terkuat BI, diikuti AT dan PBC, sedangkan BI menjadi penentu utama AIU (β = 0,849). Temuan ini menegaskan pentingnya dukungan sosial, sikap positif, dan efikasi teknologi dalam mendorong niat dan perilaku penggunaan AI. Implikasinya, program pendidikan guru perlu merancang pelatihan dan pengalaman praktik yang menumbuhkan sikap positif, meningkatkan kompetensi digital, dan memanfaatkan pengaruh sosial yang konstruktif terhadap penggunaan AI dalam pembelajaran matematika.
Kata Kunci: Artificial Intelligence Acceptance, Artificial Intelligence Use, Educational technology, Mathematics Pre-service Teacher, Theory of Planned Behaviour.
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References
Ab Hamid, M. R., Sami, W., & Sidek, M. M. (2017). Discriminant validity assessment: Use of Fornell & Larcker criterion versus HTMT criterion. Journal of Physics: Conference Series, 890(1), 1-5.
Açıkyıldız, G., & Şahin, A. (2025). Investigation of preservice secondary school mathematics teachers’ views on artificial intelligence. Journal of Pedagogical Sociology and Psychology, 7(3), 78–90.
Akendita, P. A., Boateng, F. O., Arthur, Y. D., & Banson, G. M. (2025). Investigating the effect of perception of artificial intelligence on pre-service teachers’ mathematics performance in Ghana: The moderating role of interest. Artificial Intelligence in Education, 1(1), 93–113.
Aktulun, O. U., Kasapoglu, K., & Aydogdu, B. (2024). Comparing Turkish Pre-Service STEM and Non-STEM Teachers’ Attitudes and Anxiety toward Artificial Intelligence. Journal of Baltic Science Education, 23(5), 950–963.
Alhwaiti, M. (2023). Acceptance of artificial intelligence application in the post-covid ERA and its impact on faculty members’ occupational well-being and teaching self efficacy: A path analysis using the utaut 2 model. Applied Artificial Intelligence, 37(1), 584-604.
Ali, I., Warraich, N. F., & Butt, K. (2025). Acceptance and use of artificial intelligence and AI-based applications in education: A meta-analysis and future direction. Information Development, 41(3), 859–874.
Anani, G. E., Nyamekye, E., & Bafour-Koduah, D. (2025). Determinants of Postgraduate Students′ Use of Artificial Intelligence (AI) in Academic Writing in Ghana: A Structural Equation Modelling Analysis. Human Behavior and Emerging Technologies, 2025(1), 9943540.
Awang, L. A., Yusop, F. D., & Danaee, M. (2025). Current practices and future direction of artificial intelligence in mathematics education: A systematic review. International Electronic Journal of Mathematics Education, 20(2), em0823.
Ayanwale, M. A., Frimpong, E. K., Opesemowo, O. A. G., & Sanusi, I. T. (2025). Exploring factors that support pre-service teachers’ engagement in learning artificial intelligence. Journal for STEM Education Research, 8(2), 199–229.
Boulhrir, T., & Hamash, M. (2025). Unpacking Artificial Intelligence in Elementary Education: A Comprehensive Thematic Analysis Systematic Review. Computers and Education: Artificial Intelligence, 9,100442, 1-16.
Cabrera-Sánchez, J.-P., Villarejo-Ramos, Á. F., Liébana-Cabanillas, F., & Shaikh, A. A. (2021). Identifying relevant segments of AI applications adopters–Expanding the UTAUT2’s variables. Telematics and Informatics, 58, 101529.
Chai, C. S., Chiu, T. K., Wang, X., Jiang, F., & Lin, X.-F. (2022). Modeling Chinese secondary school students’ behavioral intentions to learn artificial intelligence with the theory of planned behavior and self-determination theory. Sustainability, 15(1), 605.
Chai, C. S., Wang, X., & Xu, C. (2020). An extended theory of planned behavior for the modelling of Chinese secondary school students’ intention to learn artificial intelligence. Mathematics, 8(11), 2089.
Che Ghazali, R., Abdul Hanid, M. F., Mohd Said, M. N. H., & Lee, H. Y. (2025). The advancement of Artificial Intelligence in Education: Insights from a 1976–2024 bibliometric analysis. Journal of Research on Technology in Education, 1–17.
Cheng, C.-C., Wang, J.-S., Zhai, X., & Yang, Y.-T. C. (2025). AI literacy and gender equity in elementary education: A quasi-experimental study of a STEAM–PBL–AIoT course with questionnaire validation. International Journal of STEM Education, 12(1), 50.
Cho, H. Y., Yang, H. C., & Hwang, B. J. (2023). The Effect of ChatGPT Factors & Innovativeness on Switching Intention: Using Theory of Reasoned Action (TRA). Journal of Distribution Science, 21(8), 83–96. https://doi.org/10.15722/jds.21.08.202308.83
Dewi, H. R., Qudratuddarsi, H., Ningthias, D. P., & Cinthami, R. D. D. (2024). The Current Update of ChatGPT Roles in Science Experiment: A Systemic Literature Review. Saqbe: Jurnal Sains Dan Pembelajarannya, 1(2), 74–85.
Falebita, O. (2025). Evaluating artificial intelligence anxiety among pre-service teachers in university teacher education programs. Journal of Mathematics Instruction, Social Research and Opinion, 4(1), 1–16.
Garzón, J., Patiño, E., & Marulanda, C. (2025). Systematic review of artificial intelligence in education: Trends, benefits, and challenges. Multimodal Technologies and Interaction, 9(8), 84.
Greitemeyer, T., & Kastenmüller, A. (2024). A longitudinal analysis of the willingness to use chatGPT for academic cheating: Applying the theory of planned behavior. Technology, Mind, and Behavior, 2(3), 1–8.
Guggemos, J., & Seufert, S. (2021). Teaching with and teaching about technology–Evidence for professional development of in-service teachers. Computers in Human Behavior, 115, 106613.
Güngör, H., Kılıç, Ö., & Ünal, İ. (2025). General Attitudes of Prospective Science and Mathematics Teachers Towards Artificial Intelligence. Adıyaman University Journal of Educational Sciences, 15(1), 116–138.
Guo, S., Zheng, Y., & Zhai, X. (2024). Artificial intelligence in education research during 2013–2023: A review based on bibliometric analysis. Education and Information Technologies, 29(13), 16387–16409.
Habibi, A., Mailizar, M., Yaqin, L. N., Alqahtani, T. M., Abrar, M., Hamuddin, B., & Failasofah, F. (2024). Unlocking English proficiency: Youtube’s impact on speaking skills among indonesian university students. JOTSE: Journal of Technology and Science Education, 14(1), 142–157.
Habibi, A., Mukminin, A., Octavia, A., Wahyuni, S., Danibao, B. K., & Wibowo, Y. G. (2024). ChatGPT acceptance and use through UTAUT and TPB: A big survey in five Indonesian universities. Social Sciences & Humanities Open, 10, 101136.
Habibi, A., Mukminin, A., & Sofyan, S. (2024). Access to the digital technology of urban and suburban vocational schools. Education and Information Technologies, 29(4), 4197–4222.
Hair, J., Hult, G., Ringle, C., & Sarstedt, M. (2022). PLS-SEM book: A primer on pls-sem. Sage Thousand Oaks, CA, USA.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Huclova, M., Honzik, L., & Konigsmarkova, S. (2025). Cognitive Processes and Mental Models in Mathematics Teaching Using Artificial Intelligence. E-Learning Innovations Journal, 3(2), 4–30.
Irwanto, I. (2025). Research trends on artificial intelligence in K-12 education in Asia: A bibliometric analysis using the Scopus database (1996–2025). Discover Artificial Intelligence, 5(1), 155.
Jiao, J., & Cao, X. (2024). Research on designers’ behavioral intention toward artificial Intelligence-Aided design: Integrating the theory of planned behavior and the technology acceptance model. Frontiers in Psychology, 15, 1450717.
Marto, A., Gonçalves, A., Martins, J., & Bessa, M. (2019). Applying UTAUT Model for an Acceptance Study Alluding the Use of Augmented Reality in Archaeological Sites. VISIGRAPP (2: HUCAPP), 111–120.
Mazı, A. (2025). Developing a primary school teacher attitude scale for the use of ChatGPT in mathematics education. Acta Psychologica, 261, 105729.
Mishra, A. K., Nair, J., Gladwin, L., & Thomas, S. (2025). The Impact of Artificial Intelligence on Teaching and Learning Basic Mathematics: A Systematic Review. Journal of Cognition, Emotion & Education, 3(2), 50–61.
Nurwahid, M. (2024). Urgensi dan implementasi technological pedagogical and content knowledge (TPACK) dalam pembelajaran matematika. Mathematics Education And Application Journal (META), 6(1), 48–62.
Panaoura, R. (2025). Teaching Mathematics in the Artificial Intelligence Era: Challenges and Concerns in Higher Education. Social Education Research, 242–249.
Qudratuddarsi, H., Meivawati, E., Saputra, R., & others. (2024). Pelatihan Penelitian Metode Kuantitatif dan Systematic Literature Review bagi Dosen dan Mahasiswa. Beru’-Beru’: Jurnal Pengabdian Kepada Masyarakat, 3(1), 22–32.
Qudratuddarsi, H., Ramadhana, N., Indriyanti, N., & Ismail, A. I. (2024). Using Item Option Characteristics Curve (IOCC) to unfold misconception on chemical reaction. Journal of Tropical Chemistry Research and Education, 6(2), 105–118.
Raffaghelli, J. E., Rodríguez, M. E., Guerrero-Roldán, A.-E., & Baneres, D. (2022). Applying the UTAUT model to explain the students’ acceptance of an early warning system in Higher Education. Computers & Education, 182, 104468.
Sanusi, I. T., Ayanwale, M. A., & Tolorunleke, A. E. (2024). Investigating pre-service teachers’ artificial intelligence perception from the perspective of planned behavior theory. Computers and Education: Artificial Intelligence, 6.
Schuberth, F., Hubona, G., Roemer, E., Zaza, S., Schamberger, T., Chuah, F., Cepeda-Carrión, G., & Henseler, J. (2023). The choice of structural equation modeling technique matters: A commentary on Dash and Paul (2021). Technological Forecasting and Social Change, 194, 122665. https://doi.org/https://doi.org/10.1016/j.techfore.2023.122665
Sperling, K., Stenberg, C. J., & McGrath, C. (2024). In search of artificial intelligence (AI) literacy in teacher education: A scoping review. Computers and Education Open, 6.
Vaithilingam, S., Ong, C., & Moisescu, O. I. (2024). Robustness checks in PLS-SEM: A review of recent practices and recommendations for future applications in business research. Journal of Business Research, 173.
Van Pham, Q. (2025). The evolving landscape of AI integration in mathematics education: A systematic review of trends (2015-2025). Eurasia Journal of Mathematics, Science and Technology Education, 21.
Wang, C., Wang, H., Li, Y., & Dai, J. (2025). Factors influencing university students’ behavioral intention to use generative artificial intelligence: Integrating the theory of planned behavior and AI literacy. International Journal of Human–Computer Interaction, 41, 6649–6671.
Wang, D., & Huang, X. (2025). Transforming education through artificial intelligence and immersive technologies: Enhancing learning experiences. Interactive Learning Environments, 20(1).
Yu, H., Kim, S. H., & Lee, H. (2025). AI-Assisted Integration of Computational Thinking: Pre-service Teachers’ Experiences in Early Childhood Mathematics Education. International Journal of Early Childhood, 29.
Zhang, H. (2025). Investigating Chinese English learners’ readiness for artificial intelligence (AI) technologies: A theory of planned behavior (TPB) perspective. Learning and Motivation, 91.
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