From optimism to trust: how ChatGPT is reshaping student confidence in AI-driven learning
DOI:
https://doi.org/10.5944/ried.28.2.43238Keywords:
artificial intelligence, educational technology, machine learning, educational innovation, learning theories, trustAbstract
Generative artificial intelligence, such as ChatGPT, is transforming the field of higher education, especially in supporting academic tasks. However, its effective adoption depends on factors such as students' trust and their perception of the tool's usefulness. This study analyzes how technological optimism, perceived usefulness, and ease of use influence university students' trust in ChatGPT. A quantitative methodology was employed, using structural equation modeling (SEM) based on data collected from 316 university students in Argentina. The survey included questions about optimism, perceived usefulness, ease of use, and trust in ChatGPT, with factorial analyses conducted to validate the constructs and examine the proposed relationships in the model. The results show that students' optimism significantly impacts their perception of usefulness and trust in ChatGPT, while ease of use does not have a direct significant effect on trust. This suggests that students value the practical benefits the tool brings to their learning process more than its ease of use. These findings suggest that universities should focus on highlighting the practical value of ChatGPT through specific training programs and fostering a critical use of the tool. It is also recommended to implement strategies that enhance the interaction between students and teachers and assess the potential of ChatGPT to improve students' academic performance.
Downloads
References
Acemoglu, D., Johnson, S., & Viswanath, K. (2023). Why the Power of Technology Rarely Goes to the People. MIT Sloan Management Review, 65(1), 12-14.
Al-Abdullatif, A. M., & Alsubaie, M. A. (2024). ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy. Behavioral Sciences, 14(9). https://doi.org/10.3390/bs14090845
Al-kfairy, M., Mustafa, D., Kshetri, N., Insiew, M., & Alfandi, O. (2024). Ethical Challenges and Solutions of Generative AI: An Interdisciplinary Perspective. Informatics, 11(3). https://doi.org/10.3390/informatics11030058
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173-1182. https://doi.org/10.1037/0022-3514.51.6.1173
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? FAccT '21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. 610-623. https://doi.org/10.1145/3442188.3445922
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. JSTOR. https://doi.org/10.2307/249008
Forero-Corba, W., & Negre Bennasar, F. (2024). Técnicas y aplicaciones del Machine Learning e inteligencia artificial en educación: Una revisión sistemática. RIED-Revista Iberoamericana de Educación a Distancia, 27(1), 209-253. https://doi.org/10.5944/ried.27.1.37491
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
García Peñalvo, F. J., Llorens-Largo, F., & Vidal, J. (2024). La nueva realidad de la educación ante los avances de la inteligencia artificial generativa. RIED-Revista Iberoamericana de Educación a Distancia, 27(1), 9-39. https://doi.org/10.5944/ried.27.1.37716
Hair Jr, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. 7th edition. Prentice Hall.
Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium. Communication Monographs, 76(4), 408-420. https://doi.org/10.1080/03637750903310360
Huang, W., Zhang, H., Zhang, Z., Zhang, B., Sun, X., Huo, Y., Feng, Y., Tian, P., Mo, H., & Wang, C. (2022). A prognostic nomogram based on a new classification of combined micropapillary and solid components for stage IA invasive lung adenocarcinoma. Journal of Surgical Oncology, 125(4), 796-808. https://doi.org/10.1002/jso.26760
Ladhari, R. (2012). The lodging quality index: An independent assessment of validity and dimensions. International Journal of Contemporary Hospitality Management, 24(4), 628-652. https://doi.org/10.1108/09596111211217914
Ly, B., Doeur, B., & nat, S. (2024). Key factors influencing digital learning adoption among Cambodian university students: An integrated theoretical approach. Computers in Human Behavior Reports, 15, 100460-100460. https://doi.org/10.1016/j.chbr.2024.100460
Marimon, F., Mas-Machuca, M., & Akhmedova, A. (2024). Trusting in Generative AI: Catalyst for Employee Performance and Engagement in the Workplace. International Journal of Human-Computer Interaction, 1-16. https://doi.org/10.1080/10447318.2024.2388482
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An Integrative Model Of Organizational Trust. Academy of Management Review, 20(3), 709-734. https://doi.org/10.5465/amr.1995.9508080335
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory McGraw-Hill series. Psychology, 3.
Parasuraman, A. (2000). Technology Readiness Index (Tri): A Multiple-Item Scale to Measure Readiness to Embrace New Technologies. Journal of Service Research, 2(4), 307-320. https://doi.org/10.1177/1094670514539730
Parasuraman, A., & Colby, C. L. (2014). An Updated and Streamlined Technology Readiness Index: TRI 2.0. Journal of Service Research, 18(1), 59-74. https://doi.org/10.1177/1094670514539730
Rogers, E. M. (1995). Diffusion of innovations. Free Press.
Tang, K.-Y., Chang, C.-Y., & Hwang, G.-J. (2023). Trends in artificial intelligence-supported e-learning: A systematic review and co-citation network analysis (1998–2019). Interactive Learning Environments, 31(4), 2134-2152. https://doi.org/10.1080/10494820.2021.1875001
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
Wolfinbarger, M., & Gilly, M. C. (2003). eTailQ: dimensionalizing, measuring and predicting etail quality. Journal of Retailing, 79(3), 183-198. https://doi.org/10.1016/S0022-4359(03)00034-4
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), 39-39. https://doi.org/10.1186/s41239-019-0171-0
Zhao, X., Lynch, J. G., Jr., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis. Journal of Consumer Research, 37(2), 197-206. https://doi.org/10.1086/651257
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Frederic Marimon, María Belén Arias Valle, César Javier Coria Augusto, Claudio Marcelo Larrea Arnau
This work is licensed under a Creative Commons Attribution 4.0 International License.
The articles that are published in this journal are subject to the following terms:
1. The authors grant the exploitation rights of the work accepted for publication to RIED, guarantee to the journal the right to be the first publication of research understaken and permit the journal to distribute the work published under the license indicated in point 2.
2. The articles are published in the electronic edition of the journal under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can copy and redistribute the material in any medium or format, adapt, remix, transform, and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
3. Conditions for self-archiving. Authors are encouraged to disseminate electronically the OnlineFirst version (assessed version and accepted for publication) of its articles before publication, always with reference to its publication by RIED, favoring its circulation and dissemination earlier and with this a possible increase in its citation and reach among the academic community.
