PERLUKAH MENDORONG PEMBELAJARAN BERBASIS AI UNTUK CALON GURU? PERAN DUKUNGAN INSTITUSI, LITERASI AI, DAN PENERIMAAN TEKNOLOGI
DOI:
https://doi.org/10.51878/edutech.v6i4.13795Keywords:
Dukungan Institusi, Literasi AI, Intensi Menggunakan AI, Penerimaan TeknologiAbstract
Artificial intelligence (AI) technology is mostly used in the education sector to help students complete academic tasks, but some teachers and future teachers are still unsure how to integrate AI into their lessons, while AI integration has become a necessity to address digital transformation challenges. The aim of this research is to analyze the influence of institutional support and AI literacy on students' intention to apply AI-based learning, with technology acceptance as a mediating variable, based on Self-Determination Theory. This study uses a quantitative method and distributes questionnaires with a 1-4 Likert scale to 405 students from the Faculty of Teacher Training and Education at Sebelas Maret University. After the questionnaires were collected, the data were analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) provided by SmartPLS 4.. The research results show that institutional support, AI literacy, and technology acceptance have a positive and significant effect on the intention to apply AI-based learning, with an R Square value of 0.584, which means these three variables can explain 58.4% of students' intentions. This study provides empirical contributions by expanding the application of Self-Determination Theory to students and empowers universities to create a conducive learning environment, provide access to AI technology, and prepare training to enhance students' understanding and intention to use AI-based learning.
ABSTRAK
Teknologi artificial intelligence (AI) sebagian besar digunakan di sektor pendidikan untuk membantu mahasiswa menyelesaikan tugas akademik, beberapa guru dan mahasiswa calon guru masih belum yakin bagaimana cara mengintegrasikan AI ke dalam proses pelajaran mereka, sedangkan integrasi AI menjadi kebutuhan untuk mengatasi tantangan trasnformasi digital. Tujuan penelitian ini adalah menganalisis pengaruh dukungan institusi dan literasi AI terhadap intensi mahasiswa untuk menerapkan pembelajaran berbasis AI dengan penerimaan teknologi sebagai variabel mediasi berdasarkan Self-Determination Theory. Penelitian ini menerapkan metode kuantitatif dan penyebaran angket dengan skala likert 1-4 terhadap 405 mahasiswa pada Fakultas Keguruan dan Ilmu Pendidikan Universitas Sebelas Maret. Setelah kuesioner dikumpulkan, analisis data dilakukan menggunakan Partial Least Squares-Structural Equation Modeling (PLS-SEM) yang disediakan oleh SmartPLS 4. Hasil penelitian menunjukkan bahwa dukungan institusi, literasi AI, dan penerimaan teknologi berpengaruh positif dan signifikan terhadap intensi menerapkan pembelajaran berbasis AI dengan nilai R Square 0,584, yang artinya ketiga variabel mampu menjelaskan 58,4% intensi mahasiswa. Penelitian ini memberikan kontribusi empiris dengan memperluas penerapan Self-Determination Theory pada mahasiswa serta memberikan pemberdayaan bagi universitas dalam menciptakan lingkungan belajar yang kondusif, menyediakan akses ke teknologi AI, dan menyiapkan pelatihan untuk meningkatkan pemahaman dan intensi mahasiswa dalam menggunakan pembelajaran berbasis AI.
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References
Abdullatif, A. M. Al. (2024). Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of ai literacy, intelligent tpack, and perceived trust. Education Sciences, 14. https://doi.org/10.3390/educsci14111209
Asosiasi Penyelenggara Jasa Internet Indonesia. (2024). Survei penetrasi dan perilaku pengguna internet Indonesia 2024. APJII. https://survei.apjii.or.id
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443–3463. https://doi.org/10.1007/s10639-020-10159-7
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: a review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Cho, K. A., & Seo, Y. H. (2024). Dual mediating effects of anxiety to use and acceptance attitude of artificial intelligence technology on the relationship between nursing students’ perception of and intention to use them: a descriptive study. BMC Nursing, 23, 1–8. https://doi.org/10.1186/s12912-024-01887-z
Cortez, P. M., Ong, A. K. S., Diaz, J. F. T., German, J. D., & Jagdeep, S. J. S. S. (2024). Analyzing Preceding factors affecting behavioral intention on communicational artificial intelligence as an educational tool. Heliyon, 10(3), 1–18. https://doi.org/10.1016/j.heliyon.2024.e25896
Goodstats. (2024). 95% mahasiswa RI gunakan ai dalam proses pembelajaran. Goodstats. https://data.goodstats.id/statistic/95-mahasiswa-ri-gunakan-ai-dalam-proses-pembelajaran-FIm7A
Guan, L., Zhang, Y., & Gu, M. 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, 1–10. https://doi.org/10.1016/j.caeai.2024.100341
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (pls-sem) using r. Springer.
Jakalat, S., Bdeir, R., Rimawi, R., Al-Tarawneh, T. R., & Al-Ja’freh, S. (2025). Exploring artificial intelligence literacy among midwifery students. Jordan Journal of Nursing Research, 4(3), 254–264. https://doi.org/10.14525/JJNR.v4i3.04
Jeilani, A., & Abubakar, S. (2025). Perceived institutional support and its effects on student perceptions of AI learning in higher education: the role of mediating perceived learning outcomes and moderating technology self-efficacy. Frontiers in Education, 10(March). https://doi.org/10.3389/feduc.2025.1548900
Krejcie, R. V, & Morgan, D. W. (1970). Determining sample sixe for research activities. Educational and Psychological Measurement, 30, 607–610.
Lam, T. Y. T., Hu, Y., Yi, Y., Schulz, P. J., Lwin, M. O., Kee, K. M., Goh, W. W. B., et al. (2025). A model predicting artificial intelligence use by gastroenterology nurses in clinical practice: a cross-sectional multicenter survey. Journal of Gastroenterology and Hepatology, 40(9), 2275–2281. https://doi.org/10.1111/jgh.17042
Lucas, M., Bem-haja, P., Zhang, Y., Llorente-Cejudo, C., & Palacios-Rodríguez, A. (2025). A comparative analysis of pre-service teachers’ readiness for ai integration. Computers and Education: Artificial Intelligence, 8(1–9). https://doi.org/10.1016/j.caeai.2025.100396
Ode, E., Nana, R., Boro, I. O., & Ikyanyon, D. N. (2025). A cross-country analysis of self-determination and continuance use intention of ai tools in business education: does instructor support matter? Computers and Education: Artificial Intelligence, 8, 1–13. https://doi.org/10.1016/j.caeai.2025.100402
Park, T. I., Seo, J., Yoon, H. J., & Lee, K. E. (2025). Institutionalizing convergence education for medical artificial intelligence. Biomedical Engineering Letters, 15(6), 1073–1083. https://doi.org/10.1007/s13534-025-00523-2
Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.4324/9780429052675
Salhab, R., & Aboushi, M. M. (2025). Influence of ai literacy and 21st-century skills on the acceptance of generative artificial intelligence among college students. Frontiers in Education, 10, 1–12. https://doi.org/10.3389/feduc.2025.1640212
Wut, T. M., Sum, C. K., & Wong, H. S. (2025). Does perceived risk of ai matter? teachers’ ai literacy and institutional support: perspective from self-determination theory. Education and Information Technologies, 30(16), 23271–23293. https://doi.org/10.1007/s10639-025-13685-4
Zeng, Q., Huang, X., Zhu, J., Su, S., Hu, Y., & Zhang, X. (2025). Mechanisms of nurses’ ai use intention formation in Sichuan, Yunnan, and Beijing, China: mediating effects of ai literacy via self-efficacy-to-attitude pathways. Frontiers in Public Health, 13, 1–20. https://doi.org/10.3389/fpubh.2025.1622802
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023). Acceptance of artificial intelligence among pre-service teachers: a multigroup analysis. International Journal of Educational Technology in Higher Education, 20(49), 1–22. https://doi.org/10.1186/s41239-023-00420-7
Zhang, S. (2024). How ai literacy affects the intention to use aigc: an empirical tam-based study. Journal of Big Data and Computing, 2(3), 168–173. https://doi.org/10.62517/jbdc.202401326
Zhao, Z., An, Q., & Liu, J. (2025). Exploring ai tool adoption in higher education : evidence from a pls-sem model integrating multimodal literacy , self-efficacy , and university support. Frontiers in Psychology, 16, 1–14. https://doi.org/10.3389/fpsyg.2025.1619391
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