Psychometric evaluation of the predominant constructs in the adoption of ChatGPT in higher education: a Mexican sample
DOI:
https://doi.org/10.26439/interfases2026.n023.8785Keywords:
ChatGPT, TAM, VAM, UTAUT, confirmatory factor analysis, exploratory factor analysisAbstract
The purpose of this study was to evaluate the psychometric properties of the constructs involved in the educational adoption of ChatGPT. The instrument was designed based on Morales et al. (2025), who identified the most recurrent constructs in the literature, derived from models such as TAM, VAM, and UTAUT. Perceived Utility, Social Influence, Behavioral Intention, and Ease of Use were selected, and Perceived Risk was also incorporated due to its emerging relevance. A total of 579 university students participated in the study, were randomly assigned to two samples (M1=281 and M2=298). The time used of ChatGPT by participants shows a balanced distribution between those with less than one year of experience and those who had used it for a longer period. However, regarding competence level, basic proficiency predominated, followed by intermediate proficiency, while only a minority reported advanced use of the tool. Although ChatGPT is used by all participants, it is not an exclusive tool, as they complement its use with other generative AI tools. The primary usage patterns involve information synthesis, explaining complex concepts, and providing examples or insights into current trends; in contrast, its use in critical evaluation tasks is less common. The exploratory factor analysis identified a five-factor structure that explained 59.08% of the variance. The confirmatory factor analysis showed adequate model fit indices (RMSEA=0.047; SRMR=0.058; CFI=0.942; TLI=0.934; and PClose=0.753). Highlighting that the use and adoption of ChatGPT are associated with perceived usefulness, ease of use, the social environment, and perceived risk. Therefore, the resulting instrument can be considered valid and reliable for assessing ChatGPT adoption among Mexican university students.
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Abdaljaleel, M., Barakat, M., Alsanafi, M., Salim, N. A., Abazid, H., Malaeb, D., Mohammed, A. H., Hassan, B. A., Wayyes, A. M., Farhan, S. S., El Khatib, S., Rahal, M., Sahban, A., Abdelaziz, D., Mansour, N., AlZayer, R., Khalil, R., Fekih-Romdhane, F., Hallit, R., … Sallam, M. (2024). A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT. Scientific Reports, 14(1), 1983. https://doi.org/10.1038/s41598-024-52549-8
Abdalla, R. A. M. (2024). Examining awareness, social influence, and perceived enjoyment in the TAM framework as determinants of ChatGPT. Personalization as a moderator. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 100327. https://doi.org/10.1016/j.joitmc.2024.100327
Abdi, A.-N. M., Omar, A. M., Ahmed, M. H., & Ahmed, A. A. (2025). The predictors of behavioral intention to use ChatGPT for academic purposes: Evidence from higher education in Somalia. Cogent Education, 12(1) 2460250. https://doi.org/10.1080/2331186X.2025.2460250
Abdullatif, A. M. (2024). Investigating influencing factors of learning satisfaction in AI ChatGPT for research: University students perspective. Heliyon, 10(11), e32220. https://doi.org/10.1016/j.heliyon.2024.e32220
Acosta-Enríquez, B. G., Arbulú, M. A., Huamaní, O., López, C., & Saavedra, K. (2024). Analysis of college students’ attitudes toward the use of ChatGPT in their academic activities: Effect of intent to use, verification of information and responsible use. BMC Psychology, 12, 255. https://doi.org/10.1186/s40359-024-01764-z
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), 845. https://doi.org/10.3390/bs14090845
Almogren, A. S., Al-Rahmi, W. M., & Dahri, N. A. (2024). Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective. Heliyon, 10(11), e31887. https://doi.org/10.1016/j.heliyon.2024.e31887
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
Bouteraa, M., Bin-Nashwan, A. S., Al-Daihani, M., Dirie, A. K., Benlahcene, A., Sadallah, M., Zaki, H. O., Lada, S., Ansar, R., Fook, L. M., & Chekima, B. (2024). Understanding the diffusion of AI-generative ChatGPT in higher education: Does students’ integrity matter? Computers in Human Behavior Reports, 14, 100402. https://doi.org/10.1016/j.chbr.2024.100402
Chen, S.-Y., Kuo, H. Y., & Chang, S.-H. (2024). Perceptions of ChatGPT in healthcare: Usefulness, trust, and risk. Frontiers Public Health, 12, 1457131. https://doi.org/10.3389/fpubh.2024.1457131
Costello, A. B., & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research, and Evaluation, 10, 7. https://doi.org/10.7275/jyj1-4868
Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 61(2), 228-239. https://doi.org/10.1080/14703297.2023.2190148
Dahri, N. A., Yahaya, N., Al-Rahmi, W. M., Aldraiweesh, A., Alturki, U., Almutairy, S., Shutaleva, A., & 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
Elshaer, I. A., Hasanein, A. M., & Sobaih, A. E. (2024). The moderating effects of gender and study discipline in the relationship between university students’ acceptance and use of ChatGPT. European Journal of Investigation in Health, Psychology and Education, 14(7), 1981-1995. https://doi.org/10.3390/ejihpe14070132
Espartinez, A. S. (2024). Exploring student and teacher perceptions of ChatGPT use in higher education: A Q-Methodology study. Computers and Education: Artificial Intelligence, 7, 100264. https://doi.org/10.1016/j.caeai.2024.100264
García-Peñalvo, F. J., Llorens-Largo, F., & Vidal, J. (2024). La nueva realidad de la educación ante los avances de la inteligencia artificial generativa. Revista Iberoamericana de Educación a Distancia, 27(1), 9-39. https://doi.org/10.5944/ried.27.1.37716
Güner, H., Er, E., Akçapinar, G., & Khalil, M. (2024). From chalkboards to AI-powered learning: Students’ attitudes and perspectives on use of ChatGPT in educational settings. Educational Technology & Society, 27(2), 386-404. https://www.jstor.org/stable/48766180
Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60. https://doi.org/10.21427/D7CF7R
Hsu, W. L., & Silalahi, A. D. K. (2024). Exploring the paradoxical use of ChatGPT in education: Analyzing benefits, risks, and coping strategies through integrated UTAUT and PMT theories using a hybrid approach of SEM and fsQCA. Computers and Education: Artificial Intelligence, 7, 100329. https://doi.org/10.1016/j.caeai.2024.100329
Jordan, F. M. (2021). Valor de corte de los índices de ajuste en el análisis factorial confirmatorio. Psocial, 7(1). https://www.redalyc.org/journal/6723/672371335005/672371335005.pdf
Kooli, C. (2023). Chatbots in education and research: A critical examination of ethical implications and solutions. Sustainability, 15(7), 5614. https://doi.org/10.3390/su15075614
Lai, C. Y., Cheung, K. Y., & Chan, C. S. (2023). Exploring the role of intrinsic motivation 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
Lai, K. (2021). Fit difference between nonnested models given categorical data: Measures and estimation. Structural Equation Modeling: A Multidisciplinary Journal, 28(1), 99-120. https://doi.org/10.1080/10705511.2020.1763802
Lo, C. K. (2023). What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 13(4), 410. https://doi.org/10.3390/educsci13040410
Mahmud, A., Sarower, A. H., Sohel, A., Assaduzzaman, M., & Bhuiyan, T. (2024). Adoption of ChatGPT by university students for academic purposes: Partial least square, artificial neural network, deep neural network and classification algorithms approach. Array, 21, 100339. https://doi.org/10.1016/j.array.2024.100339
Morales, A., Figueroa, G., Duana, M., Hidalgo, C., & Amador, J. (2025). Acceptance and adoption of ChatGPT in higher education: a systematic review. Revista Digital de Investigación en Docencia Universitaria, 19(2), e2119. https://doi.org/10.19083/ridu.2025.2119
Nemt-allah, M., Khalifa, W., Badawy, M., Elbably, Y., & Ibrahim, A. (2024). Validating the ChatGPT usage scale: Psychometric properties and factor structures among postgraduate students. BMC Psychology, 12, 497. https://doi.org/10.1186/s40359-024-01983-4
Panggabean, E. M., & Silalahi, A. D. K. (2025). How do ChatGPT’s benefit-risk-coping paradoxes impact higher education in Taiwan and Indonesia? Computers and Education: Artificial Intelligence, 8, 100412. https://doi.org/10.1016/j.caeai.2025.100412
Parveen, K., Phuc, T. Q. B., Alghamdi, A. A., Hajjej, F., Obidallah, W. J., Alduraywish, Y. A., & Shafiq, M. (2024). Unraveling the dynamics of ChatGPT adoption and utilization through structural equation modeling. Scientific Reports, 14, 23469. https://doi.org/10.1038/s41598-024-74406-4
Rana, M., Siddiqee, M. S., Sakib, N., & Ahamed, R. (2024). Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework. Heliyon, 10(18), e37569. https://doi.org/10.1016/j.heliyon.2024.e37569
Rizun, N., Bordean, O. N., Nikiforova, A., Beleiu, I. N., & Revina, A. (2026). Generative AI in higher education: Ethical and behavioral factors influencing students’ intentions to use ChatGPT. Computers and Education Open, 10, 100336. https://doi.org/10.1016/j.caeo.2026.100336
Romero-Rodríguez, J.-M., Ramírez-Montoya, M.-S., Buenestado-Fernández, M., & Lara-Lara, F. (2023). Use of ChatGPT at university as a tool for complex thinking: Students’ perceived usefulness. Journal of New Approaches in Educational Research, 12(2), 323-339. https://doi.org/10.7821/naer.2023.7.1458
Rudolph, J., Tan, S., & Tan, S. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning & Teaching, 6, 342-363. https://doi.org/10.37074/jalt.2023.6.1.9
Sallam, M., Salim, N. A., Barakat, M., Al-Mahzoum, K., Al-Tammemi, A. B., Malaeb, D., Hallit, R., & Hallit, S. (2023). Assessing health students’ attitudes and usage of ChatGPT in Jordan: Validation study. JMIR Medical Education, 9, e48254. https://doi.org/10.2196/48254
Schumacker, R. E., & Lomax, R. G. (2004). A beginner’s guide to structural equation modeling (2.ª ed.). Lawrence Erlbaum Associates Publishers.
Shuhaiber, A., Kuhail, M. A., & Salman, S. (2025). ChatGPT in higher education - A student’s perspective. Computers in Human Behavior Reports, 17, 100565. https://doi.org/10.1016/j.chbr.2024.100565
Sobaih, A. E., Elshaer, I. A., & Hasanein, A. M. (2024). Examining students’ acceptance and use of ChatGPT in Saudi Arabian higher education. European Journal of Investigation in Health, Psychology and Education, 14(3), 709-721. https://doi.org/10.3390/ejihpe14030047
Song, C., & Song, Y. (2023). Enhancing academic writing skills and motivation: assessing the efficacy of ChatGPT in AI-assisted language learning for EFL students. Frontiers in Psychology, 14, 1260843. https://doi.org/10.3389/fpsyg.2023.1260843
United Nations Educational, Scientific and Cultural Organization. (2023). Guidance for generative AI in education and research. https://unesdoc.unesco.org/ark:/48223/pf0000386693
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need [Presentación de escrito]. 31st Conference on Neural Information Processing Systems, Long Beach, California, Estados Unidos. https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
Velicer, W. F., & Fava, J. L. (1998). Effects of variable and subject sampling on factor pattern recovery. Psychological Methods, 3(2), 231-251. https://psycnet.apa.org/doi/10.1037/1082-989X.3.2.231
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
Worthington, R. L., & Whittaker, T. A. (2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist, 34(6), 806-838. https://doi.org/10.1177/0011000006288127
Xia, Y., & Yang, Y. (2019). RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behavior Research Method, 51, 409-428. https://doi.org/10.3758/s13428-018-1055-2
Xu, X., Su, Y., Zhang, Y., Wu, Y., & Xu, X. (2024). Understanding learners’ perceptions of ChatGPT: A thematic analysis of peer interviews among undergraduates and postgraduates in China. Heliyon, 10(4), e26239. https://doi.org/10.1016/j.heliyon.2024.e26239
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