DETERMINING FACTORS IN THE UTILIZATION OF ARTIFICIAL INTELLIGENCE: PERCEPTIONS AND BEHAVIORS OF PROSPECTIVE PRIMARY SCHOOL TEACHERS IN COMPLETING SCIENCE ASSIGNMENTS

Abstract

Artificial Intelligence (AI) holds significant potential to transform education, particularly in teaching methodologies and task completion. This study aims to identify the factors influencing the perceptions and behaviors of elementary education students in utilizing ChatGPT and Gemini to complete science-related assignments. The research design employs a quantitative approach with both descriptive and causal methodologies. Data testing and analysis are conducted using Structural Equation Modeling (SEM), P-value, and Prediction-Oriented Segmentation (POS). Path analysis results reveal that perceived benefits significantly impact perception (.403) and behavior (.406). AI effectiveness significantly affects perception (.303) but minimally influences behavior (.018). Preference for AI usage positively influences behavior (.305), whereas dependence on AI negatively impacts perception (-.050). Restrictions on AI usage reduce perception (-.077) but increase behavior (.115). The p-value analysis indicates that the perceived benefits of AI use significantly influence behavior (.000) and perception (.000), supporting the hypothesis that perceived benefits play a crucial role in enhancing AI adoption and fostering positive attitudes toward its use. Conversely, AI effectiveness significantly affects perception (.000) but not behavior (.862). Dependence, restrictions, and the impact of AI show no significant effects on either behavior or perception, except for AI usage preferences, which significantly influence behavior (.033). Segment analysis reveals that perceived benefits influence behavior in Segment 1 (.510) and perception in Segment 2 (.493). AI effectiveness negatively impacts behavior in Segment 2 (-.633) but shows moderate effects in Segment 1 (.214). Preferences for AI usage exert a more substantial influence on behavior in Segment 2 (.614), while the effects of dependence and restrictions vary across segments. The perceived benefits of AI encourage technology adoption among students, while dependence and restrictions introduce complexities in formulating AI‑based educational policies.

Keywords: Artificial intelligence, ChatGPT, gemini, behaviors.

REFERENCES

Al-Debei, M. M., Akroush, M. N., & Ashouri, M. I. (2015). Consumer attitudes towards online shopping: The effects of trust, perceived benefits, and perceived web quality. Internet Research, 25(5), 707-733. https://doi.org/10.1108/IntR‑05‑2014-0146

Alam, A., & Mohanty, A. (2023). Educational technology: Exploring the convergence of technology and pedagogy through mobility, interactivity, AI, and learning tools. Cogent Engineering, 10(2), 2283282. https://doi.org/10.1080/23311916.2023.2283282

Alsajri, A., Salman, H. A., & Steiti, A. (2024). Generative Models in Natural Language Processing: A Comparative Study of ChatGPT and Gemini. Babylonian Journal of Artificial Intelligence, 2024, 134-145. https://doi.org/10.58496/BJAI/2024/015

Arora, N., Manchanda, P., Aggarwal, A., & Maggo, V. (2024). Tapping generative AI capabilities: a study to examine continued intention to use ChatGPT in the travel planning. Asia Pacific Journal of Tourism Research, 1-20. https://doi.org/10.1080/10941665.2024.2405134

Atchley, T. W., Wingenbach, G., & Akers, C. (2013). Comparison of course completion and student performance through online and traditional courses. The International Review of Research in Open and Distributed Learning, 14(4). https://doi.org/10.19173/irrodl.v14i4.1461

Baskara, F. R. (2025). ChatGPT and Google Gemini in EFL education: A qualitative exploration of pedagogical efficacy among Indonesian Sophomores. Journal of Languages and Language Teaching, 13(1), 436-447. https://doi.org/10.33394/jollt.v13i1.9926

Bayer, H., Araci, F. G. I., & Gürkan, G. (2024). ChatGPT-4o, ChatGPT-4 and Google Gemini are compared with students: A study in higher education. International Journal of Technology in Education and Science, 8(4), 627-644. https://doi.org/10.46328/ijtes.585

Calzada, K. P. D. (2024). Anti-dependency teaching strategy for innovation in the age of AI among technology-based students. Environment and Social Psychology, 9(8), 1-18. https://doi.org/10.59429/esp.v9i8.3026

Cao, G., Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106, 102312. https://doi.org/10.1016/j.technovation.2021.102312

Carr, N. (2020). The shallows: What the Internet is doing to our brains. WW Norton & Company.

Choi, J., Park, J., & Suh, J. (2023). Evaluating the current state of ChatGPT and its disruptive potential: An empirical study of Korean users. Asia Pacific Journal of Information Systems, 33(4), 1058-1092. https://doi.org/10.14329/apjis.2023.33.4.1058

Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS quarterly.

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International journal of information management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

Firdaus, T. (2022). Penerapan model direct instruction berbasis sets pada pembelajaran ipa untuk meningkatkan keterampilan berpikir kritis siswa. Natural Science Education Research (NSER), 5(1), 119-134. https://doi.org/10.21107/nser.v5i1.15759

Firdaus, T. (2023). Representative platform cyber metaverse terkoneksi BYOD sebagai upaya preventive urgensi digital pada sistem pendidikan Indonesia. Jurnal Integrasi dan Harmoni Inovatif Ilmu-Ilmu Sosial, 3(2), 123-131. https://doi.org/10.17977/um063v3i2p123-131  

Firdaus, T., Sholeha, S. A., Jannah, M., & Setiawan, A. R. (2024). Comparison of ChatGPT and Gemini AI in answering higher-order thinking skill biology questions: Accuracy and evaluation. International Journal of Science Education and Teaching, 3(3), 126-138. https://doi.org/10.14456/ijset.2024.11

Firdaus, T., Nuryanti, E., Adawiyah, N. R., Sari, D. I., & Rahmah, F. (2025). Research trends in mental health and the effect on students’ learning disorder. Journal of Education and Learning Reviews, 2(1), 1-20. https://doi.org/10.60027/jelr.2025.952    

Gao, B. (2023). Understanding smart education continuance intention in a delayed benefit context: An integration of sensory stimuli, UTAUT, and flow theory. Acta Psychologica, 234, 103856. https://doi.org/10.1016/j.actpsy.2023.103856  

Hochschild, A. R. (2018). Strangers in their own land: Anger and mourning on the American right. The New Press.

Hwang, G. J., Lai, C. L., & Wang, S. Y. (2015). Seamless flipped learning: a mobile technology-enhanced flipped classroom with effective learning strategies. Journal of computers in education, 2, 449-473. https://doi.org/10.1007/s40692‑015‑0043-0

Imran, M., Almusharraf, N. (2024). Google Gemini as a next generation AI educational tool: a review of emerging educational technology. Smart Learn. Environ, 11, 22 (2024). https://doi.org/10.1186/s40561-024-00310-z

Lai, C. L. (2021). Exploring university students’ preferences for AI-assisted learning environment. Educational Technology & Society, 24(4), 1-15.

Lall, S. (1992). Technological capabilities and industrialization. World development, 20(2), 165-186. https://doi.org/10.1016/0305-750X(92)90097-F    

Luckin, R. (2018). Machine learning and human intelligence. The future of education for the 21st century. UCL institute of education press.

Mahmud, H., Islam, A. N., & Mitra, R. K. (2023). What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness. Technological Forecasting and Social Change, 193, 122641. https://doi.org/10.1016/j.techfore.2023.122641

Nikolic, S., Sandison, C., Haque, R., Daniel, S., Grundy, S., Belkina, M., ... & Neal, P. (2024). ChatGPT, Copilot, Gemini, SciSpace and Wolfram versus higher education assessments: an updated multi-institutional study of the academic integrity impacts of Generative Artificial Intelligence (GenAI) on assessment, teaching and learning in engineering. Australasian journal of engineering education, 29(2), 126-153. https://doi.org/10.1080/22054952.2024.2372154

Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and practice in technology enhanced learning, 12(1), 12-22. https://doi.org/10.1186/s41039-017-0062-8

Rane, N. L., Choudhary, S. P., Tawde, A., & Rane, J. (2023). ChatGPT is not capable of serving as an author: Ethical concerns and challenges of large language models in education. International Research Journal of Modernization in Engineering Technology and Science, 5(10), 851-874. https://www.doi.org/10.56726/IRJMETS45212

Ritchie, J., Lewis, J., & Elam, R. G. (2013). Selecting samples. Qualitative research practice: A guide for social science students and researchers. Thousand Oaks, CA: Sage, 111. 

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. https://doi.org/10.2307/30036540   

Vidgen, B., & Yasseri, T. (2016). P-values: misunderstood and misused. Frontiers in Physics, 4, 6. https://doi.org/10.3389/fphy.2016.00006  

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     

World Bank. (2024). Who on Earth Is Using Generative AI? https://hdl.handle.net/10986/42071

Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: a systematic review. Smart Learning Environments, 11(1), 11-28. https://doi.org/10.1186/s40561-024-00316-7