Profil Kesiapan AI Guru SD di Kabupaten Magelang: Analisis Gaussian Mixture Model dan Efektivitas Pelatihan
Abstract
This research aims to map teacher readiness profiles in integrating Artificial Intelligence (AI) and evaluate the impact of formal training on these profiles. Using the Gaussian Mixture Model (GMM) as a soft clustering approach to identify natural and flexible groupings of teacher characteristics, data were analyzed from a convenience sample of 67 primary school teachers in Magelang Regency. Three distinct profiles were identified: Low Readiness, Moderate Enthusiast, and High-Adaptive Readiness. The results show that the majority of teachers (80.6%) belong to the High-Adaptive profile, characterized by high literacy and positive attitudes. Furthermore, Chi-Square analysis and Cramer’s V (V=0.1120) revealed no significant relationship between training history and readiness profiles (p=0.6567). This suggests that current formal training has a marginal practical effect. Policy recommendations emphasize the need for differentiated training and structural support to enhance AI integration effectively.
Keywords
Artificial intelligence in education, Gaussian Mixture Model, Primary School Teachers, Teacher Readiness, Training Effectiveness
References
Alfanda, W., & Purwanti, S. (2025). Pemahaman dan tantangan guru sekolah dasar terhadap penerapan AI dalam pendidikan. Jurnal Pembelajaran dan Pengajaran Pendidikan Dasar, 8(2). http://dx.doi.org/10.33369/
Andrade, C. (2020). The limitations of online surveys. Indian Journal of Psychological Medicine, 42(6), 575–576. https://doi.org/10.1177/0253717620957496
Ardho, R. I., & Permana, M. R. (2025). Eksplorasi persepsi guru sekolah dasar tentang implementasi kecerdasan buatan dalam pembelajaran di kelas. Journal of Education for All, 3(2), 129–134. https://doi.org/10.61692/edufa.v3i2.323
Cai, H., Tang, Y., & Yan, X. (2026). A tutorial on unsupervised Gaussian mixture model for performance clustering in second language research. Research Methods in Applied Linguistics, 5(1), 100296. https://doi.org/10.1016/j.rmal.2026.100296
Ching, K. W., & Jamaludin, K. A. (2025). Understanding school teachers’ acceptance of AI in education: Insights from the technology acceptance model (TAM). International Journal of Academic Research in Progressive Education and Development, 14(3), 564–579. https://doi.org/10.6007/IJARPED/v14-i3/25196
Chiu, T. K. F. (2026). Intelligent-TPACK (I-TPACK) framework developed from TPACK through integration of artificial intelligence literacy and competency. Interactive Learning Environments, 1–16. https://doi.org/10.1080/10494820.2026.2615818
CNN Indonesia. (2024, November 14). Google ungkap 3 sektor paling sering pakai AI di Indonesia. https://www.cnnindonesia.com/teknologi/20241114071206-185-1166396/google-ungkap-3-sektor-paling-sering-pakai-ai-di-indonesia
Frismelly, A., & Rahayu, W. (2025). Kesiapan pendidik dan hambatan terhadap kecerdasan buatan generatif dalam praktik pembelajaran. https://doi.org/10.24036/jpte.v6i2.650
Gupta, S. (2025, November 18). Gaussian mixture model. https://www.geeksforgeeks.org/profile/tufan_gupta2000
Jager, J., Putnick, D. L., & Bornstein, M. H. (2017). More than just convenient: The scientific merits of homogeneous convenience samples. Monographs of the Society for Research in Child Development, 82(2), 13–30. https://doi.org/10.1111/mono.12296
Lovakov, A., & Agadullina, E. R. (2021). Empirically derived guidelines for effect size interpretation in social psychology. European Journal of Social Psychology, 51(3), 485–504. https://doi.org/10.1002/ejsp.2752
Markus, A., Pfister, J., Carolus, A., Hotho, A., & Wienrich, C. (2024). Effects of AI understanding-training on AI literacy, usage, self-determined interactions, and anthropomorphization with voice assistants. Computers and Education Open, 6, 100176. https://doi.org/10.1016/j.caeo.2024.100176
Nabila, N., Setyawati, S. T., Wasitohadi, W., & Putra, P. (2025). Analisis kesiapan guru dalam penerapan artificial intelligence (AI) pada mata pelajaran biologi di SMAN 1 Ampel. Kurikula: Jurnal Pendidikan, 9(2). https://doi.org/10.56997/kurikula.v9i2.1663
Ng, D. T. K., Leung, J. K. L., Chu, S. K. W., & Qiao, M. S. (2021). Conceptualizing AI literacy: An exploratory review. Computers and Education: Artificial Intelligence, 2, 100041. https://doi.org/10.1016/j.caeai.2021.100041
Putra, A. S., & Saputra, D. (2025). Utilization of artificial intelligence (AI) in Indonesian language learning in elementary schools: Teacher perceptions and challenges. Journal of Elementary Education, 2(2). https://doi.org/10.38040/jeleduc.v2i2.1374
Qureshi, N. M., Qureshi, F. S., Bandeali, D. M. M., & Akbar, S. (2025). Teacher perceptions and readiness for AI-enhanced teaching in developing countries. 3(3).
Schreiber, J. B., & Amin, M. E. K. (2025). Core reporting expectations for quantitative manuscripts using independent and dependent t-tests, one-way ANOVA, OLS regression, and chi-square. Currents in Pharmacy Teaching and Learning, 17(12), 102462. https://doi.org/10.1016/j.cptl.2025.102462
Scrucca, L., Fop, M., Murphy, T. B., & Raftery, A. E. (2016). mclust 5: Clustering, classification and density estimation using Gaussian finite mixture models. The R Journal, 8(1), 289. https://doi.org/10.32614/RJ-2016-021
Scrucca, L., Saqr, M., López-Pernas, S., & Murphy, K. (2023). An introduction and tutorial to model-based clustering in education via Gaussian mixture modelling. arXiv. https://doi.org/10.48550/arXiv.2306.06219
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167
Published
How to Cite
Issue
Section
Copyright
Copyright (c) 2026 Fatikha Akfini Anantaputri, Diyah Ayu Karunianingsih, Yolanda Presiana Desi, Ardian Setio Utomo
The author(s) retain full copyright of this work. The copyright holder grants the journal the right of first publication and the right to distribute the work under the terms of the selected open-access license.
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
All published content in Takuana: Jurnal Pendidikan, Sains, dan Humaniora is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY NC SA 4.0). This license allows others to share, copy, redistribute, and adapt the work for non-commercial purposes, as long as proper credit is given to the original author(s) and source, and any derivative works are distributed under the same license.
Attribution must include a clear citation of the original work and a statement indicating whether any changes were made. Commercial use is not permitted under this license and there is no additional legal or technological restrictions may be applied. For more information about the license terms and permissible use, please refer to the full license text available here.
