Analisis Kesesuaian Teknologi ChatGPT terhadap Aktivitas Perkuliahan Mahasiswa Menggunakan Model Task–Technology Fit (TTF)
DOI:
https://doi.org/10.61132/prosemnasproit.v2i2.119Keywords:
ChatGPT, Pendidikan Tinggi, Task–Technology Fit, Pemanfaatan Teknologi, Kinerja MahasiswaAbstract
The rapid development of artificial intelligence, particularly ChatGPT, has created new opportunities to support students’ academic activities in higher education. However, its utilization needs to be evaluated in terms of the alignment between academic task characteristics and technological capabilities to ensure optimal outcomes. This study aims to examine the feasibility of using ChatGPT in students’ academic activities by applying the Task–Technology Fit (TTF) model. This research employed a quantitative approach using Structural Equation Modeling based on Partial Least Squares (SEM-PLS). Data were collected through questionnaires distributed to university students and analyzed using SmartPLS 4 software. The variables examined included Task Characteristics, Technology Characteristics, Task–Technology Fit, Performance Impact, and Utilization. The results indicate that Task Characteristics and Technology Characteristics have a positive and significant effect on Task–Technology Fit. Furthermore, Task–Technology Fit significantly influences Performance Impact and Utilization. Performance Impact also shows a positive and significant effect on the utilization of ChatGPT by students. These findings suggest that the alignment between academic task requirements and the capabilities of ChatGPT plays a crucial role in improving students’ performance and encouraging sustained technology use. The implications of this study highlight the importance of selective and purposeful use of ChatGPT in higher education and provide a reference for higher education institutions in formulating policies related to the ethical and effective integration of artificial intelligence technologies as learning support tools.
References
Alamri, M. M., Almaiah, M. A., & Al-Rahmi, W. M. (2020). The Role of Compatibility and Task-Technology Fit (TTF): On Social Networking Applications (SNAs) Usage as Sustainability in Higher Education. IEEE Access, 8, 161668–161681. https://doi.org/10.1109/ACCESS.2020.3021944
Aldoseri, A., Al-Khalifa, K. N., & Hamouda, A. M. (2024). AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact. Sustainability, 16(5), 1790. https://doi.org/10.3390/su16051790
Bolatan, G. I. S., Giadedi, A., & Daim, T. U. (2024). Exploring Acquiring Technologies: Adoption, Adaptation, and Knowledge Management. IEEE Transactions on Engineering Management, 71, 1950–1958. https://doi.org/10.1109/TEM.2022.3168901
Brar, P. S., Shah, B., Singh, J., Ali, F., & Kwak, D. (2022). Using Modified Technology Acceptance Model to Evaluate the Adoption of a Proposed IoT-Based Indoor Disaster Management Software Tool by Rescue Workers. Sensors, 22(5), 1866. https://doi.org/10.3390/s22051866
Chavarnakul, T., Lin, Y.-C., Khan, A., & Chen, S.-C. (2024). Exploring the Determinants and Consequences of Task-Technology Fit: A Meta-Analytic Structural Equation Modeling Perspective. Emerging Science Journal, 8(1), 77–94. https://doi.org/10.28991/ESJ-2024-08-01-06
Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & Management, 36(1), 9–21. https://doi.org/10.1016/S0378-7206(98)00101-3
Dr. Aris Try Andreas Putra. (2025). METODOLODI PENELITIAN KUANTITATIF DAN KUALITATIF (Teoretis & Praktis). AMERTA MEDIA.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7
He, S., Yang, F., Zuo, J., & Lin, Z. (2023). ChatGPT for scientific paper writing—promises and perils. The Innovation, 4(6), 100524. https://doi.org/10.1016/j.xinn.2023.100524
Hollar, D. W. (2018). The Method of Path Coefficients. In Trajectory Analysis in Health Care (pp. 49–72). Springer International Publishing. https://doi.org/10.1007/978-3-319-59626-6_5
Imran, M., & Almusharraf, N. (2023). Analyzing the role of ChatGPT as a writing assistant at higher education level: A systematic review of the literature. Contemporary Educational Technology, 15(4), ep464. https://doi.org/10.30935/cedtech/13605
Kahraman, F., Aktas, A., Bayrakceken, S., Çakar, T., Tarcan, H. S., Bayram, B., Durak, B., & Ulman, Y. I. (2024). Physicians’ ethical concerns about artificial intelligence in medicine: a qualitative study: “The final decision should rest with a human.” Frontiers in Public Health, 12. https://doi.org/10.3389/fpubh.2024.1428396
Kayalı, B., Yavuz, M., Balat, Ş., & Çalışan, M. (2023). Investigation of student experiences with ChatGPT-supported online learning applications in higher education. Australasian Journal of Educational Technology, 39(5), 20–39. https://doi.org/10.14742/ajet.8915
Ley, T., Tammets, K., Sarmiento-Márquez, E. M., Leoste, J., Hallik, M., & Poom-Valickis, K. (2022). Adopting technology in schools: modelling, measuring and supporting knowledge appropriation. European Journal of Teacher Education, 45(4), 548–571. https://doi.org/10.1080/02619768.2021.1937113
SASIREKHA, DR. K. (2024). Influence of Artificial Intelligence (AI) Tools on the Research Capabilities of College Students. INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 08(10), 1–7. https://doi.org/10.55041/IJSREM37999
Strielkowski, W., Grebennikova, V., Lisovskiy, A., Rakhimova, G., & Vasileva, T. (2025). AI ‐driven adaptive learning for sustainable educational transformation. Sustainable Development, 33(2), 1921–1947. https://doi.org/10.1002/sd.3221
Sun, G. H., & Hoelscher, S. H. (2023). The ChatGPT Storm and What Faculty Can Do. Nurse Educator. https://doi.org/10.1097/NNE.0000000000001390
Thondebhavi Subbaramaiah, M., & Shanthanna, H. (2023). ChatGPT in the field of scientific publication – Are we ready for it? Indian Journal of Anaesthesia, 67(5), 407–408. https://doi.org/10.4103/ija.ija_294_23
TomassMHultt, G. (n.d.). Classroom Companion: Business Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R AAWorkbook. http://www.
Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q.-L., & Tang, Y. (2023). A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122–1136. https://doi.org/10.1109/JAS.2023.123618
Yarsasi, S., Tahyudin, I., & Hariguna, T. (2025). Analisis Validitas dan Reliabilitas Kuesioner dengan Metode Partial Least Squares Structural Equation Modeling pada Aplikasi SMARTPLS. Jurnal Pendidikan Dan Teknologi Indonesia, 5(7), 1905–1913. https://doi.org/10.52436/1.jpti.885
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Prosiding Seminar Nasional Ilmu Teknik

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.





