Comparing Student Acceptance of ChatGPT and Claude AI Through an Extended Technology Acceptance Model
DOI:
https://doi.org/10.29408/edumatic.v10i2.36272Keywords:
chatgpt, claude ai, extended technology acceptance model, prior experience, trustAbstract
The classical Technology Acceptance Model falls short in fully explaining how students embrace generative artificial intelligence, as the impact of trust on their intention to use varies by platform. This study explores the acceptance mechanisms of ChatGPT and Claude AI by extending the Technology Acceptance Model to include Trust and Prior Experience. This study involved 312 students from the Faculty of Science and Technology at Universitas Muhammadiyah Tapanuli Selatan, selected through purposive sampling. Data were gathered using a validated Likert-scale questionnaire and analyzed with Partial Least Squares Structural Equation Modeling, incorporating multi-group analysis. Both measurement models demonstrated satisfactory validity and reliability. For ChatGPT, perceived usefulness was the primary factor influencing attitude, but this did not lead to a corresponding behavioral intention. In contrast, for Claude AI, both trust and attitude significantly influenced behavioral intention. Among the eight structural paths, four showed significant differences between platforms, with the extended model providing a better explanation for Claude AI. These results suggest that the acceptance of generative AI is dependent on the platform, offering conceptual insights into the structural assumptions of the TAM and practical guidance for platform-specific AI integration policies in higher education.
References
Bai, Y., Kadavath, S., Kundu, S., Askell, A., Kernion, J., Jones, A., Chen, A., Goldie, A., Mirhoseini, A., & McKinnon, C. (2022). Constitutional AI: Harmlessness from AI feedback. arXiv preprint. https://doi.org/10.48550/arXiv.2212.08073
Balaskas, S., Tsiantos, V., Chatzifotiou, S., & Rigou, M. (2025). Determinants of ChatGPT adoption intention in higher education: Expanding on TAM with the mediating roles of trust and risk. Information, 16(2), 82. https://doi.org/10.3390/info16020082
Cabrera, C., & Neville, R. (2026). Widely used but barely trusted: Understanding student perceptions on the use of generative AI in higher education. Studies in Higher Education. https://doi.org/10.1080/13603108.2025.2595453
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Aldraiweesh, A., Alturki, U., Almutairy, S., & Soomro, R. B. (2024). Extended TAM based acceptance of AI-powered ChatGPT for supporting metacognitive self-regulated learning in education: A mixed-methods study. Heliyon, 10(8), e29317. https://doi.org/10.1016/j.heliyon.2024.e29317
Dasian, R. S. I., & Desriyeni, D. (2024). Acceptance of ChatGPT technology among students: A descriptive study of the TAM model on students of the Informatics Engineering Study Program, Padang State University. Student Research Journal, 2(2), 178-201. https://doi.org/10.55606/jsr.v2i2.2847
Elfirdaus, I., Suryanto, T. L. M., & Pratama, A. (2024). Evaluation of student acceptance of the Perplexity application as a learning aid using a simplified Technology Acceptance Model (TAM). Journal of Informatics and Applied Electrical Engineering, 12(3). https://doi.org/10.23960/jitet.v12i3.4803
Falebita, O. S., Abah, J. A., Asanre, A. A., Abiodun, T. O., Ayanwale, M. A., & Ayanwoye, O. K. (2025). Determinants of chatbot brand trust in the adoption of generative artificial intelligence in higher education. Education Sciences, 15(10), 1389. https://doi.org/10.3390/educsci15101389
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90. https://doi.org/10.2307/30036519
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
Ibrahim, H. (2023). Perception, performance, and detectability of conversational artificial intelligence across 32 university courses. Scientific Reports, 13(1). https://doi.org/10.1038/s41598023389643
Kleine, A.K., Schaffernak, I., & Lermer, E. (2025). Exploring predictors of AI chatbot usage intensity among students: Within and between person relationships based on the Technology Acceptance Model. Computers in Human Behavior: Artificial Humans, 3, 100113. https://doi.org/10.1016/j.chbah.2024.100113
Lai, C. Y., Cheung, K. Y., & Chan, C. S. (2023). Exploring the role of intrinsic motivation factors in ChatGPT adoption to support active learning: An extension of the technology acceptance model. Computers and Education: Artificial Intelligence, 5, 100178. https://doi.org/10.1016/j.caeai.2023.100178
Lim, W. M. (2025). What is quantitative research? An overview and guidelines. Australasian Marketing Journal, 33(3), 325-348. https://doi.org/10.1177/14413582241264622
Ma, J., Wang, P., Li, B., Wang, T., Pang, X. S., & Wang, D. (2024). Exploring user adoption of ChatGPT: A technology acceptance model perspective. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2024.2314358
Roumeliotis, K. I., & Tselikas, N. D. (2023). ChatGPT and Open-AI models: A preliminary review. Future Internet, 15(6), 192. https://doi.org/10.3390/fi15060192
Shahzad, M. F., Xu, S., & Javed, I. (2024). ChatGPT awareness, acceptance, and adoption in higher education: The role of trust as a cornerstone. International Journal of Educational Technology in Higher Education, 21(1), Article 46. https://doi.org/10.1186/s41239-024-00478-x
Shoufan, A. (2023). Exploring students' perceptions of ChatGPT: Thematic analysis and follow-up survey. IEEE Access, 11, 38805-38818. https://doi.org/10.1109/ACCESS.2023.3268224
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. https://doi.org/10.1287/isre.6.2.144
Zhou, X., Teng, D., & Al-Samarraie, H. (2024). The mediating role of generative AI self-regulation on students' critical thinking and problem solving. Education Sciences, 14(12), 1302. https://doi.org/10.3390/educsci14121302
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