ANALISIS FAKTOR PENERIMAAN ARTIFICIAL INTELLIGENCE DI LINGKUNGAN SEKOLAH MENGGUNAKAN TECHNOLOGY ACCEPTANCE MODEL (TAM)
Keywords
Artificial Intelligence, Technology Acceptance, TAM, SEM-PLS, Secondary SchoolAbstract
The rapid advancement of Artificial Intelligence (AI) opens transformative opportunities for education, yet its adoption in school environments still faces complex barriers. This study aims to analyze factors influencing teachers' and students' acceptance of AI technology in schools using an extended Technology Acceptance Model (TAM) framework incorporating Computer Self-Efficacy (CSE), Anxiety (ANX), and Subjective Norm (SN) constructs. A quantitative cross-sectional survey was conducted among 312 respondents (134 teachers and 178 students) from 15 secondary schools in DKI Jakarta and West Java. The research instrument used a validated 24-item questionnaire based on a 5-point Likert scale. Data analysis employed Partial Least Square-based Structural Equation Modeling (SEM-PLS) using SmartPLS 4.0. Measurement model evaluation confirmed all constructs met convergent validity (AVE > 0.50), discriminant validity (HTMT < 0.85), and composite reliability (CR > 0.80). Hypothesis testing through 5,000-subsample bootstrapping revealed: CSE significantly and positively affects PEOU (β = 0.512, p < 0.001), ANX significantly and negatively affects PEOU (β = -0.289, p < 0.001), PEOU positively influences PU (β = 0.387, p < 0.001), PU is the strongest predictor of BI (β = 0.421, p < 0.001), and SN significantly influences BI (β = 0.178, p < 0.001). The model explains 67.3% of Behavioral Intention variance. These findings provide strategic implications for AI integration policies in Indonesian schools.
References
[1] Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
[2] Alfalah, A. A. (2023). Factors influencing students' adoption and use of mobile learning management systems: A structural equation modeling approach. International Journal of Information Management Data Insights, 3(1), 100143. https://doi.org/10.1016/j.jjimei.2022.100143
[3] Alrawabdeh, W. (2022). The role of computer self-efficacy, computer anxiety, and perceived enjoyment in predicting university students' use of digital technologies. International Journal of Emerging Technologies in Learning, 17(12), 112–129. https://doi.org/10.3991/ijet.v17i12.30012
[4] Amara, S., Atia, L., & Belhaj-Ali, S. (2021). The impact of technology acceptance model factors on students' behavioral intention to use AI-based learning tools. Education and Information Technologies, 26(5), 5867–5890. https://doi.org/10.1007/s10639-021-10568-w
[5] Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52–62. https://doi.org/10.61969/jai.1337500
[6] Balitbangbuk. (2023). Survei nasional pemanfaatan teknologi digital dalam pembelajaran. Badan Penelitian dan Pengembangan dan Perbukuan, Kemendikbudristek.
[7] Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
[8] Cheng, X. (2020). Challenges of 'learning as usual' with COVID-19: Considering changes in the approaches of EFL learners. Frontiers in Education, 5, Article 601048. https://doi.org/10.3389/feduc.2020.601048
[9] Chua, S. L., Chen, D. T., & Wong, A. F. L. (1999). Computer anxiety and its correlates: A meta-analysis. Computers in Human Behavior, 15(5), 609–623. https://doi.org/10.1016/S0747-5632(99)00039-4
[10] Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
[11] Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189–211. https://doi.org/10.2307/249688
[12] Compeau, D., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145–158. https://doi.org/10.2307/249749
[13] Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
[14] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
[15] Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
[16] Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley.
[17] Grand View Research. (2023). Artificial intelligence in education market size report. Grand View Research, Inc.
[18] 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
[19] Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(2), 504–526. https://doi.org/10.1007/s40593-021-00239-1
[20] Huang, A. Y. Q., Lu, O. H. T., Huang, J. C. H., Yin, C. J., & Yang, S. J. H. (2021). Predicting students' academic performance by using educational big data and learning analytics. Interactive Learning Environments, 29(2), 219–237. https://doi.org/10.1080/10494820.2019.1636086
[21] Kasneci, E., Sessler, K., Kuchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Gunnemann, S., Hullermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., ... Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
[22] Kemendikbudristek. (2023). Laporan tahunan transformasi digital pendidikan 2023. Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi Republik Indonesia.
[23] King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information and Management, 43(6), 740–755. https://doi.org/10.1016/j.im.2006.05.003
[24] Kurniawan, M. T., Hidayat, R., & Pratama, A. R. (2023). Digital competence framework for Indonesian teachers in the AI era: A systematic review. Jurnal Pendidikan dan Kebudayaan, 8(1), 87–105.
[25] Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563–575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
[26] Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
[27] Rakhmat, D. A., & Syafrudin, U. (2023). Tantangan dan peluang implementasi kecerdasan buatan dalam sistem pendidikan Indonesia. Jurnal Inovasi Teknologi Pendidikan, 10(1), 45–62.
[28] Russell, S. J., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson Education.
[29] Scherer, R., Siddiq, F., & Tondeur, J. (2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers' adoption of digital technology in education. Computers & Education, 128, 13–35. https://doi.org/10.1016/j.compedu.2018.09.009
[30] Syahrul, M., & Hermawati, N. (2022). Analisis penerimaan teknologi pembelajaran berbasis AI pada guru SMA di Indonesia: Perspektif TAM. Jurnal Teknologi Pendidikan, 24(2), 112–131.
[31] Teo, T. (2011). Factors influencing teachers' intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440. https://doi.org/10.1016/j.compedu.2011.06.008
[32] Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. https://doi.org/10.1287/isre.11.4.342.11872
[33] Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926
[34] 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
[35] Zawacki-Richter, O., Marin, 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), Article 39. https://doi.org/10.1186/s41239-019-0171-0
