FACTORS INFLUENCING VOCATIONAL SCHOOL STUDENTS’ BEHAVIOUR IN USING GENERATIVE AI: PERSPECTIVES FROM THE THEORY OF PLANNED BEHAVIOUR AND AI LITERACY
DOI:
https://doi.org/10.51878/edutech.v6i3.12637Keywords:
Generative Artificial Intelligence, Theory of Planned Behavior, Literasi AI, Sikap, Niat BerperilakuAbstract
This study aims to analyse the factors influencing the behaviour of vocational high school (SMK) students in using Generative Artificial Intelligence (GAI) from the perspective of the Theory of Planned Behaviour (TPB) and AI literacy. The background to this study is based on the increasing use of AI technology in education, which has not yet been fully matched by an understanding of AI literacy and the ethics of its use. This study employs a quantitative approach with an explanatory research design using a survey method. Data were collected via a Likert-scale questionnaire from 193 students at SMKN 1 Magetan in the Office Management department for the 2024/2025 academic year. Data analysis was conducted using Structural Equation Modelling Generalised Structural Component Analysis (SEM-GSCA) via the GSCA-Pro application. The results indicate that AI literacy has a significant influence on students’ attitudes, and these attitudes have a positive influence on behavioural intention regarding the use of Generative AI. These findings suggest that the higher the students’ level of AI literacy, the more positive their attitudes and intentions are towards utilising AI technology to support the learning process. The conclusion of this study indicates that strengthening AI literacy is necessary to foster responsible AI usage behaviour within the educational environment.
ABSTRAK
Penelitian ini bertujuan untuk menganalisis faktor-faktor yang memengaruhi perilaku siswa Sekolah Menengah Kejuruan (SMK) dalam menggunakan Generative Artificial Intelligence (GAI) berdasarkan perspektif Theory of Planned Behavior (TPB) dan literasi AI. Penelitian ini dilatarbelakangi oleh semakin meningkatnya pemanfaatan teknologi kecerdasan buatan dalam dunia pendidikan yang belum sepenuhnya diimbangi dengan pemahaman mengenai literasi AI serta etika penggunaannya. Penelitian menggunakan pendekatan kuantitatif dengan desain explanatory research melalui metode survei. Data dikumpulkan menggunakan kuesioner skala Likert terhadap 193 siswa Program Keahlian Manajemen Perkantoran di SMKN 1 Magetan Tahun Ajaran 2024/2025. Analisis data dilakukan menggunakan Structural Equation Modeling–Generalized Structured Component Analysis (SEM-GSCA) melalui aplikasi GSCA-Pro. Hasil penelitian menunjukkan bahwa literasi AI berpengaruh signifikan terhadap sikap siswa, dan sikap tersebut berpengaruh positif terhadap niat berperilaku dalam menggunakan Generative AI. Temuan ini mengindikasikan bahwa semakin tinggi tingkat literasi AI yang dimiliki siswa, semakin positif pula sikap dan niat mereka dalam memanfaatkan teknologi AI untuk mendukung proses pembelajaran. Penelitian ini menyimpulkan bahwa penguatan literasi AI perlu dilakukan untuk membentuk perilaku penggunaan AI yang bertanggung jawab di lingkungan pendidikan.
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References
Al-abdullatif, A. M. (2024). Modeling teachers’ acceptance of generative artificial intelligence use in higher education: The role of AI literacy, intelligent TPACK, and perceived trust.
Ali, R., Bashir, F., & Ahmad, R. (2021). Imprints of lower socioeconomic class in English speaking anxieties and academic performance of rural and urban students. IRASD Journal of Economics, 3(3), 412–425. https://doi.org/10.52131/joe.2021.0303.0055
Binjwair, A., & Alamer, A. (2025). Acta Psychologica digital drivers: How the acceptance and use of technology model shapes university students’ behavior toward generative AI. Acta Psychologica, 261, 105930. https://doi.org/10.1016/j.actpsy.2025.105930
Borekci, C., & Celik, O. (2024). Sakarya University Journal of Education. Sakarya University Journal of Education, 14(2), 228–249. https://doi.org/10.19126/suje.1468866
Caduda, S. S., & Barroso, A. S. (2024). Generative artificial intelligence: Challenges and opportunities for systems developers: A systematic mapping of literature. Journal of Multidisciplinary Learning and Research, 9(2), 39–47. https://doi.org/10.11648/j.mlr.20240902.12
Chin, W. W. (1998). The partial least squares approach to structural equation modeling.
Creswell, J. W. (2012). Qualitative, quantitative and mixed methods research. Sage.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388. https://doi.org/10.2307/3150980
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hwang, H., Cho, G., & Choo, H. (2021). GSCA Pro 1.1 user’s manual.
Ittefaq, M., & Zain, A. (2025). Factors influencing international students’ adoption of generative artificial intelligence: The mediating role of perceived values and attitudes.
Kulla, L., Yesy, N. L., P., & Firmani, P. S. (2025). Pengaruh pemanfaatan artificial intelligence dan tingkat literasi digital terhadap kemampuan berpikir kritis mahasiswa.
Meneau, L. K. (2022). Struggling to make ends meet: Can consumer financial behaviors improve? International Journal of Bank Marketing, 40(2), 263–296. https://doi.org/10.1108/IJBM-12-2020-0595
Nasiru, M., Nakama, Y., Naima, D., & Abubakar, H. (2025). Students’ behavioural intention to use content generative AI for learning and research: A UTAUT theoretical perspective. Education and Information Technologies, 30(13), 17969–17994. https://doi.org/10.1007/s10639-025-13441-8
Nguyen, A., Shalpegin, T., Lämsä, J., & Whitehead, R. (2024). AI literacy in learning and educational technology. In Effective Practices in AI Literacy Education (pp. 63–70).
Nikolic, S., Al-abdeli, Y. M., & Fairweather, H. (2025). Beyond assessment security: A critical policy analysis of four alternative strategies. Sustainability in Teaching and Engineering, 5, 564–586. https://doi.org/10.3934/steme.2025027
Niraula, S. (2024). The impact of ChatGPT on academia: A comprehensive analysis of AI policies. Advances in Mobile Learning Educational Research, 4(1), 973–982.
Or, C. (2023). The role of attitude in UTAUT model: A meta-analytic structural equation modelling study.
Poudel, R. L., & Bastakoti, C. K. (2024). Impact of behavioural intention to use generative artificial intelligence on academic performance.
Qi, K., Yunhong, G., & Changping, K. (2025). Bridging AI literacy and UTAUT constructs: Structural equation modeling of AI adoption.
Reyes, R. A., et al. (2024). The relationship between attitude toward AI and AI literacy of university students.
Ryazanov, I., Öhman, C., & Björklund, J. (2024). How ChatGPT changed the media’s narratives on AI. Frontiers in Artificial Intelligence.
Sergeeva, O. V., et al. (2025). Impact of digital media literacy on attitude toward generative AI acceptance in higher education. Frontiers in Education, 10, 1–14. https://doi.org/10.3389/feduc.2025.1563148
Siddiqui, N. (2019). Using secondary data in education research.
Soylu, M. Y., Lee, J., Hung, J.-T., Cui, C. Z., & Joyner, D. A. (2025). AI literacy as a key driver of user experience in AI-powered assessment.
Sugandini, D. (2022). Intention to adopt e-learning with anxiety: UTAUT model.
Sugiyono. (2023). Metode penelitian kuantitatif, kualitatif, dan R&D. Alfabeta.
Wang, C., Wang, H., Li, Y., Dai, J., Gu, X., & Yu, T. (2025). Factors influencing university students’ behavioral intention to use generative artificial intelligence. International Journal of Human-Computer Interaction, 41(11), 6649–6671. https://doi.org/10.1080/10447318.2024.2383033
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