ANALYSIS OF CHATGPT ACCEPTANCE FOR EDUCATION USING MODIFIED TECHNOLOGY ACCEPTANCE MODEL

Mahmud Rizal Mustofa; Maria Ulfah Siregar

doi:10.52436/1.jutif.2024.5.4.2095

Mahmud Rizal Mustofa Informatics, Science and Technology Faculty, Universitas Islam Negeri Sunan Kalijaga, Indonesia
Maria Ulfah Siregar Informatics, Science and Technology Faculty, Universitas Islam Negeri Sunan Kalijaga, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2024.5.4.2095

Keywords: acceptance, artificial intelligence, ChatGPT, student, TAM

Abstract

The presence of ChatGPT provides various benefits from all sectors including education. However, despite the various benefits obtained, many researchers argue that ChatGPT also has many significant drawbacks. This research aims to analize the effect of perceived threat, perceived ease of use, perceived usefulness, attitude toward using dan behavioral intention to use the system of ChatGPT in education. The TAM modification in this research is the addition of a perceived threat variable which refers to the problem of the research object.The population in this research is active students of Universitas Islam Negeri Sunan Kalijaga Yogyakarta. The sampling technique is carried out using probability sampling or simple random sampling. While the determination the number of samples in this study used a sample table so that 377 respondents were students from various faculties. The data used in this study were obtained by distributing questionnaires and analyzed using SEM-PLS with the help of SmartPLS 3 software. The result of this research show that perceived threat and perceived ease of use affect perceived usefulness, perceived ease of use and perceived usefulness affect attitude toward using and attitude toward using affects behavioral intention to use of ChatGPT in education.

Downloads

Download data is not yet available.

References

X. Zhai, “ChatGPT User Experience: Implications for Education,” SSRN Electron. J., 2023, doi: 10.2139/ssrn.4312418.

S. Mollman, “ChatGPT gained 1 million users in under a week. Here’s why the AI chatbot is primed to disrupt search as we know it,” yahoo finance, 2022.

J. Deng and Y. Lin, “Frontiers in Computing and Intelligent Systems The Benefits and Challenges of ChatGPT: An Overview,” Front. Comput. Intell. Syst., vol. 2, no. 2, pp. 81–83, 2022.

S. Sok, “Opinion: Benefits and Risks of ChatGPT in Education. Cambodianess.,” Cambodianess, 2023.

S. Atlas, DigitalCommons@URI DigitalCommons@URI ChatGPT for Higher Education and Professional Development: A ChatGPT for Higher Education and Professional Development: A Guide to Conversational AI Guide to Conversational AI Terms of Use. 2023.

A. Barman-adhikari, S. Begun, E. Rice, A. Yoshioka-maxwell, and A. Perez-portillo, “Correspondence concerning this article should be addressed to Anamika Barman-,” vol. 33, no. 2016, pp. 1–60, 2016.

S. R. Mogali, “Initial impressions of ChatGPT for anatomy education. Anatomical Sciences Education,” Anat. Sci. Educ., 2023.

H. Yu, “Reflection on whether Chat GPT should be banned by academia from the perspective of education and teaching,” Front. Psychol., vol. 14, 2023, doi: 10.3389/fpsyg.2023.1181712.

Study.com, “Productive Teaching Tool or Innovative Cheating?,” 2023. https://study.com/resources/perceptions-of-chatgpt-in-schools

Y. Wahyu, S. Putra, M. A. Machmudi, and A. G. Naim, “ANALYSIS OF GRABAG GUIDE APPLICATION ACCEPTANCE FOR INTRODUCTION TO TOURIST ATTRACTIONS USING THE TECHNOLOGY ACCEPTANCE MODEL ( TAM ),” vol. 5, no. 2, pp. 561–569, 2024.

C. Wang et al., “An empirical evaluation of technology acceptance model for Artificial Intelligence in E-commerce,” Heliyon, vol. 9, no. 8, p. e18349, 2023, doi: 10.1016/j.heliyon.2023.e18349.

J. J. Zhu, J. Jiang, M. Yang, and Z. J. Ren, “ChatGPT and Environmental Research,” Environ. Sci. Technol., pp. 1–4, 2023, doi: 10.1021/acs.est.3c01818.

F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Q. Manag. Inf. Syst., vol. 13, no. 3, pp. 319–339, 1989, doi: 10.2307/249008.

Ronald W Rogers, “A Protection Motivation Theory of Fear Appeals and Attitude Change1.,” J. Psychol., 1975.

F. . Venkatesh, V. & Davis, “A Model of the Antecedents of Perceived Ease of Use: Development and Test,” Decis. Sci., vol. Vol 27 No, pp. 451–470, 1996.

Icek Ajzen, “The theory of planned behavior. Organizational Behavior and Human Decision Processes,” elsevier, 1991.

D. . Krejcie, R.V & Morgan, “Determining Sample Size for Research Activities,” Educ. Psychol. Meas., vol. Vol 30, no. 3, pp. 607–610, 1970.

C. . Chuan, “Sample Size Estimation Using Krejcie and Morgan and Cohen Statistical Power Analysis : A Comparison,” J. Penyelid. IPBL, vol. vol 7, no. 1, pp. 78–86, 2006.

Sugiyono, Metodelogi Penelitian Kuantitatif dan Kualitatif Dan R&D. Bandung: ALFABETA. 2019.

et al Hair, J.F., “An Introduction to Structural Equation Modeling. In: Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R,” Classr. Companion Business. Springer, Cham, 2021.

A. Muhson, “Analisis Statistik dengan SmartPLS: Path Analysis, Confirmatory Factor Analysis, & Structural Equation Modeling,” Universitas Negeri Yogyakarta, 2022. https://staffnew.uny.ac.id/upload/132232818/pendidikan/Panduan Penggunaan SmartPLS.pdf

I. Ghozali, Model Persamaan Struktural Konsep Dan Aplikasi Program AMOS 24. Semarang: Badan Penerbit Universitas Diponegoro, 2017.

G. Cao, Y. Duan, J. S. Edwards, and Y. K. Dwivedi, “Technovation Understanding managers ’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making ☆,” Technovation, vol. 106, p. 102312, 2021, doi: 10.1016/j.technovation.2021.102312.

B. F. Ilmiati, B. H. Rinuastuti, L. E. H. Mulyono, and L. M. Furkan, “Pengaruh Perceived Threat dan Perceived Efficacy terhadap Intention to Comply Preventive Behavior dalam Pemasaran Sosial Covid-19,” J. Sos. Ekon. Dan Hum., vol. 8, no. 2, pp. 183–192, 2022, doi: 10.29303/jseh.v8i2.90.