The Implementation of AI-Based Technology (Gemini AI) in English Classroom at Senior High School: Tam Model
DOI:
https://doi.org/10.26877/allure.v6i2.615Keywords:
AI-based technology, english language, perceived ease of use, perceived usefulness, TAM modelAbstract
The Industry 5.0 era is a new way of addressing social and educational issues based on artificial intelligence (AI) technology. Problems in the learning process, such as changes in the educational landscape, student motivation, teacher workload, and so on, can be solved by AI, which makes the learning process more effective. This study explores how AI-based technology can be applied in language classrooms. Researcher will investigate how AI-based technology, especially Gemini AI, is applied in language classrooms and measure its acceptance by language teachers and students through the Technology Acceptance Model (TAM), exploring the challenges faced. This study focuses on three language classes and one teacher at SMAN 1 Ungaran. This school is selected for several reasons, the first is its reputation as a transformative and accredited school, having a language study program, and integrating technology into the learning process. It measures its acceptance by 61 students and one language teacher through the Technology Acceptance Model (TAM) at SMAN 1 Ungaran. This study uses a qualitative design with a case study approach to explore phenomena in language classrooms that apply AI-based technology. The results of this study found that the learning process was integrated with AI from the planning stage to the end of the English learning process. Other findings also illustrate that usefulness is the main factor in the use of AI by students, while teachers consider that there needs to be strong control over the use of AI in the learning space. In addition, its implementation requires the maximum utilization of school facilities to produce more great outputs and learning experiences.
References
Akefe, I. O., Adegoke, V. A., Akefe, E., Schweitzer, D., & Bolaji, S. (2025). An innovative approach to medical education: Leveraging generative artificial intelligence to promote inclusion and support for indigenous students. Trends in Higher Education, 4(3), 36.
Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Algharabat, R. (2018). Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40, 125–138.
Al-Rahmi, A. M., Shamsuddin, A., Alturki, U., Aldraiweesh, A., Yusof, F. M., Al-Rahmi, W. M., & Aljeraiwi, A. A. (2021). The influence of information system success and technology acceptance model on social media factors in education. Sustainability, 13(14), 7770.
Alshurafat, H. (2023). The usefulness and challenges of chatbots for accounting professionals: Application on ChatGPT. SSRN Electronic Journal, 1–7. https://doi.org/10.2139/ssrn.4345921
Baichuan, Y. (2024). Research on improving teaching efficiency and quantifying learning plans in innovative education practice. Frontiers in Educational Research, 7(9), 254–258.
Baxi, C. O., Patel, K. J., Patel, K. M., Patel, V. B., & Acharya, V. A. (2024). Consumers' digital wallet adoption: Integration of technology task fit and UTAUT. International Journal of Asian Business and Information Management (IJABIM), 15(1), 1–23.
Cao, F., Lei, M., Lin, S., & Xiang, M. (2022). Application of artificial intelligence‐based big data AI technology in physical education reform. Mobile Information Systems, 2022(1), 4017151.
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), 43.
Chaudhry, M. A., & Kazim, E. (2022). Artificial intelligence in education (AIEd): A high-level academic and industry note 2021. AI and Ethics, 2(1), 157–165.
Chiu, T. K. F., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student motivation to learn with artificial intelligence (AI). Computers & Education, 194, 104711.
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.
Dai, W., Lin, J., Jin, F., Li, T., Tsai, Y. S., Gasevic, D., & Chen, G. (2023). Can large language models provide feedback to students? A case study on ChatGPT. OSF Preprints. https://doi.org/10.35542/osf.io/hcgzj
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(5), 319–339.
Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45(1), 19–45. https://doi.org/10.1006/ijhc.1996.0040
Fernández-Batanero, J. M., Román-Graván, P., Montenegro-Rueda, M., López-Meneses, E., & Fernández-Cerero, J. (2021). Digital teaching competence in higher education: A systematic review. Education Sciences, 11(11), 689.
Gaete, M. I., Belmar, F., Cortés, M., Alseidi, A., Asbun, D., Durán, V., ... & Varas, J. (2023). Remote and asynchronous training network: From a SAGES grant to an eight-country remote laparoscopic simulation training program. Surgical Endoscopy, 37(2), 1458–1465.
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433.
Hikmat, H. (2022). The readiness of education in Indonesia in facing the society era 5.0. Jurnal Basicedu, 6(2), 2953–2961.
Hoesni, W. E. W., Hassan, F. N. A., Ajmain, M. T., & Rosli, N. A. M. (2020). The effects of ICT towards students’ attitude. Khazanah Pendidikan Islam, 2(2), 90–99.
Katz, A., Wei, S., Nanda, G., Brinton, C., & Ohland, M. (2023). Exploring the efficacy of ChatGPT in analyzing student teamwork feedback with an existing taxonomy. arXiv preprint. https://doi.org/10.48550/arXiv.2305.11882
Kessler, M. (2020). Technology-mediated writing: Exploring incoming graduate students' L2 writing strategies with activity theory. Computers and Composition, 55, 102542. https://doi.org/10.1016/j.compcom.2020.102542
Kim, J., Giroux, M., & Lee, J. C. (2021). When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI. Journal of Consumer Behaviour, 20(5), 1101–1112.
Kong, S. C., & Abelson, H. (Eds.). (2022). Computational thinking education in K-12: Artificial intelligence literacy and physical computing. MIT Press.
Lee, I., Park, J., & Yoon, H. G. (2022). Science teachers' theory-based teaching: Connecting a learning cycle model to a lesson plan. Journal of Baltic Science Education, 21(3), 462–473.
Lin, C. H., & Chang, C. Y. (2020). Designing control mechanisms in technology-enhanced learning environments: Balancing autonomy and regulation. Computers & Education, 148, 103819.
Liu, C., Hou, J., Tu, Y. F., Wang, Y., & Hwang, G. J. (2021). Incorporating a reflective thinking promoting mechanism into artificial intelligence-supported English writing environments. Interactive Learning Environments, 31(10), 3340–3359. https://doi.org/10.1080/10494820.2021.1927115
Melnyk, M. (2019). Digitalization and development of digital technologies in Scandinavian countries. Development Management, 17(2), 10–16.
Miles, M. B. (1994). Qualitative data analysis: An expanded sourcebook (2nd ed.). SAGE Publications.
Nathania, L., Indarini, I., & Anandya, D. (2021). The effects of external factors on perceived ease of use, perceived usefulness, attitude towards use, and behavioral intention of older adults in Indonesia. Jurnal Manajemen Bisnis, 12(1), 45–58.
Orim, F. S., Ofem, U. J., Edam-Agbor, I. B., Nsan, N. N., Okri, J. A., Ekpang, P., ... & Asukwo, P. (2025). Implementation of automated classroom assessment in higher education using the technology acceptance model. Discover Education, 4(1), 87.
Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020.
Pokrivčáková, S. (2019). Preparing teachers for the application of AI-powered technologies in foreign language education. Journal of Language and Cultural Education, 7(3), 135–153. https://doi.org/10.2478/jolace-2019-0025
Porter, B., & Grippa, F. (2020). A platform for AI-enabled real-time feedback to promote digital collaboration. Sustainability, 12(24), 1–13. https://doi.org/10.3390/su122410242
Rachmawati, U., KHM, N. S., & Prananda, A. R. (2025). The technology-enhanced teaching reading of English in junior high school level: Teachers' perspectives. Allure Journal, 5(1), 60–67.
Rahimi, M., & Fathi, J. (2022). Exploring the impact of wiki-mediated collaborative writing on EFL students' writing performance, writing self-regulation, and writing self-efficacy: A mixed methods study. Computer Assisted Language Learning, 35(9-10), 2627–2674. https://doi.org/10.1080/09588221.2021.1888753
Taj, I., & Jhanjhi, N. (2022). Towards industrial revolution 5.0 and explainable artificial intelligence: Challenges and opportunities. International Journal of Computing and Digital Systems, 12(1), 295–320. https://doi.org/10.12785/ijcds/120128
Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440.
Touretzky, D., Gardner‐McCune, C., Breazeal, C., Martin, F., & Seehorn, D. (2019). A year in K–12 AI education. AI Magazine, 40(4), 88–90.
Uluuysal, B., & Kurt, A. A. (2022). Analysis of a teacher’s online teaching material planning and development processes: English lesson example. Mehmet Akif Ersoy University Journal of Education Faculty, (62), 234–262.
Umar, R., & Javaid, T. (2025). The role of AI in redesigning pandemic-affected STEAM education: Insights from ICT and blended learning research. Education and Information Technologies, 30(2), 1120–1145.
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451–481.
Wimbo, R. M., & Wahyuni, S. (2020). Perceived ease of use effect on perceived usefulness and attitude towards use and its impact on behavioural intention to use. International Journal of Advanced Research, 8(12), 439–445.
Yan, D. (2023). Impact of ChatGPT on learners in a L2 writing practicum: An exploratory investigation. Educational Technology Research and Development, 71(4), 1295–1316. https://doi.org/10.1007/s11423-023-10225-0
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39.


