Medición de las percepciones sobre el uso de la inteligencia artificial en la educación superior: propuesta de marco de adaptación para el contexto latinoamericano

Gerardo Antonio Hernández Torres; Silvia Patricia Aquino Zúñiga; Manuel Jesús Izquierdo Sandoval

doi:10.54104/papeles.v18n35.2317

Autores

Gerardo Antonio Hernández Torres Universidad Juárez Autónoma de Tabasco https://orcid.org/0000-0001-5498-8358
Silvia Patricia Aquino Zúñiga Universidad Juárez Autónoma de Tabasco https://orcid.org/0000-0002-7223-8582
Manuel Jesús Izquierdo Sandoval Universidad Juárez Autónoma de Tabasco https://orcid.org/0000-0001-5605-2318

DOI:

https://doi.org/10.54104/papeles.v18n35.2317

Palavras-chave:

Inteligencia artificial, percepción, enseñanza superior, cuestionario, medición

Resumo

Introducción: la adopción de herramientas de inteligencia artificial en la educación superior ha motivado un creciente interés por comprender los factores que influyen en su aceptación y por evaluar la calidad de los instrumentos utilizados para su medición. En este contexto, resulta necesario analizar de manera crítica los modelos teóricos y las evidencias psicométricas que sustentan dichos instrumentos. Metodología: se realizó una revisión sistemática siguiendo las directrices Preferred Reporting Items for Systematic reviews and Meta-Analyses (Prisma), a partir de la cual se analizaron 26 estudios empíricos publicados entre 2022 y 2025 e indexados en Scopus, Web of Science (WoS), Scientific Electronic Library Online (SciELO) y Red de Revistas Científicas de América Latina y el Caribe, España y Portugal (Redalyc). Los estudios seleccionados emplean mayoritariamente cuestionarios con escalas tipo Likert para la medición de los constructos propuestos por el Technology Acceptance Model (TAM) y la Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) que reportan información sobre procesos de validación y confiabilidad. Resultados y discusión: los resultados muestran una utilización consistente de validez de constructo y confiabilidad interna como criterios mínimos de evaluación instrumental, aunque con variaciones relevantes en los procedimientos metodológicos y en la adaptación contextual de los modelos. Asimismo, se identifican desafíos asociados a la equidad digital y a la confianza institucional que inciden en la aceptación de la inteligencia artificial (IA), en particular, en contextos latinoamericanos. A partir de estos hallazgos, se proponen lineamientos orientados a mejorar la evaluación de la aceptación de la IA en la educación superior latinoamericana. Conclusiones: la revisión sugiere que los cuestionarios basados en escalas Likert constituyen instrumentos ampliamente utilizados y potencialmente replicables; sin embargo, su aplicación en la educación superior latinoamericana requiere adaptaciones contextuales y normativas que garanticen la validez de los constructos medidos. En este sentido, se destaca la necesidad de avanzar hacia diseños instrumentales más sensibles a las condiciones estructurales e institucionales de la región.

Downloads

Não há dados estatísticos.

Referências

Acosta-Enríquez, B. G., Arbulú Ballesteros, M. A., Huamaní Jordan, O., López Roca, C. y Saavedra Tirado, 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(1). https://doi.org/10.1186/s40359-024-01764-z DOI: https://doi.org/10.1186/s40359-024-01764-z

Alhwaiti, M. (2023). Acceptance of artificial intelligence application in the post-covid ERA and its impact on faculty members’ occupational well-being and teaching self efficacy: A path analysis using the utaut 2 model. Applied Artificial Intelligence, 37(1), 2175110. https://doi.org/10.1080/08839514.2023.2175110 DOI: https://doi.org/10.1080/08839514.2023.2175110

Almogren, A. S., Al-Rahmi, W. M. y 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 DOI: https://doi.org/10.1016/j.heliyon.2024.e31887

Almulla, M. A. (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 DOI: https://doi.org/10.1016/j.heliyon.2024.e32220

Alshammari, S. H., Almankory, A. Z. y Alrashidi, M. E. (2025). Efectos de la conciencia y confianza en la disposición estudiantil para usar ChatGPT: Modelo TAM-ECM integrado. RIED: Revista Iberoamericana de Educación a Distancia, 28(2), 155-180. https://doi.org/10.5944/ried.28.2.43476 DOI: https://doi.org/10.5944/ried.28.2.43476

Alzahrani, A. y Alzahrani, A. (2024). Comprendiendo la adopción de ChatGPT en universidades: El impacto del TPACK y UTAUT2 en los docentes. RIED: Revista Iberoamericana de Educación a Distancia, 28(1). https://doi.org/10.5944/ried.28.1.41498 DOI: https://doi.org/10.5944/ried.28.1.41498

Amer Jid Almahri, F. A., Bell, D. y Gulzar, Z. (2024). Chatbot technology use and acceptance using educational personas. Informatics, 11(2). https://doi.org/10.3390/informatics11020038 DOI: https://doi.org/10.3390/informatics11020038

Ayoubi, K. (2024). Adopting ChatGPT: Pioneering a new era in learning platforms. International Journal of Data and Network Science, 8(2), 1341-1348. https://doi.org/10.5267/j.ijdns.2023.11.001 DOI: https://doi.org/10.5267/j.ijdns.2023.11.001

Barroga, E. y Matanguihan, G. J. (2022). A practical guide to writing quantitative and qualitative research questions and hypotheses in scholarly articles. Journal of Korean Medical Science, 37(16), e121. https://doi.org/10.3346/jkms.2022.37.e121 DOI: https://doi.org/10.3346/jkms.2022.37.e121

Boyle, J. y Fisher, S. (2008). Educational testing: A competence-based approach. John Wiley & Sons. DOI: https://doi.org/10.1002/9780470774090

Bryman, A. (1984). The debate about quantitative and qualitative research: A question of method or epistemology? The British Journal of Sociology, 35(1), 75-92. https://doi.org/10.2307/590553 DOI: https://doi.org/10.2307/590553

Cabero-Almenara, J., Palacios-Rodríguez, A., Loaiza-Aguirre, M. I. y Rivas-Manzano, M.ª del R. de. (2024). Acceptance of educational artificial intelligence by teachers and its relationship with some variables and pedagogical beliefs. Education Sciences, 14(7). https://doi.org/10.3390/educsci14070740 DOI: https://doi.org/10.3390/educsci14070740

Cambra-Fierro, J. J., Fuentes Blasco, M.ª, López-Pérez, M.ª E. y Trifu, A. (2025). ChatGPT adoption and its influence on faculty well-being: An empirical research in higher education. Education and Information Technologies, 30(2), 1517-1538. https://doi.org/10.1007/s10639-024-12871-0 DOI: https://doi.org/10.1007/s10639-024-12871-0

Cohen, L., Manion, L. y Morrison, K. (2018). Research methods in education (8.ª ed.). Routledge. DOI: https://doi.org/10.4324/9781315456539

Cortina, J. M. (1993). What is coefficient alpha? An examination of theory and applications. Journal of Applied Psychology, 78(1), 98-104. https://doi.org/10.1037/0021-9010.78.1.98 DOI: https://doi.org/10.1037//0021-9010.78.1.98

Dauzón-Ledesma, L. e Izquierdo, J. (2023). Language learning investment in Higher Education: Validation and implementation of a Likert-scale questionnaire in the context of compulsory EFL Learning. Education Sciences, 13(4). https://doi.org/10.3390/educsci13040370 DOI: https://doi.org/10.3390/educsci13040370

Davis, F. D., Bagozzi, R. P. y Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982 DOI: https://doi.org/10.1287/mnsc.35.8.982

Duckett, L. J. (2021). Quantitative research excellence: Study design and reliable and valid measurement of variables. Journal of Human Lactation, 37(3), 456-463. https://doi.org/10.1177/08903344211019285 DOI: https://doi.org/10.1177/08903344211019285

Fabila Echauri, A. M., Minami, H. y Izquierdo Sandoval, M. J. (2014). La escala de Likert en la evaluación docente: Acercamiento a sus características y principios metodológicos. Perspectivas docentes, 50. https://doi.org/10.19136/pd.a0n50.589

Falebita, O. S. y Kok, P. J. (2025). Artificial intelligence tools usage: A structural equation modeling of undergraduates’ technological readiness, self-efficacy and attitudes. Journal for STEM Education Research, 8(2), 257-282. https://doi.org/10.1007/s41979-024-00132-1 DOI: https://doi.org/10.1007/s41979-024-00132-1

Field, A. (2018). Discovering statistics using IBM SPSS statistics (5.ª ed.). Sage.

G. C, S. B., Bhandari, P., Gurung, S. K., Srivastava, E., Ojha, D. y Dhungana, B. R. (2024). Examining the role of social influence, learning value and habit on students’ intention to use ChatGPT: The moderating effect of information accuracy in the UTAUT2 model. Cogent Education, 11(1), 2403287. https://doi.org/10.1080/2331186X.2024.2403287 DOI: https://doi.org/10.1080/2331186X.2024.2403287

Gil-Vera, V. D. (2024). Uso de ChatGPT por estudiantes universitarios: Un análisis relacional. Formación Universitaria, 17(5), 129-138. https://doi.org/10.4067/s0718-50062024000400129 DOI: https://doi.org/10.4067/s0718-50062024000400129

Glass, G. V. y Hopkins, K. D. (2008). Statistical methods in education and psychology (3.ª ed.). Allyn & Bacon.

Gómez-García, M., Ruiz-Palmero, J., Boumadan-Hamed, M. y Soto-Varela, R. (2025). Perceptions of future teachers and pedagogues on responsible AI: A measurement instrument. RIED: Revista Iberoamericana de Educación a Distancia, 28(2), 105-130. https://doi.org/10.5944/ried.28.2.43288 DOI: https://doi.org/10.5944/ried.28.2.43288

Grassini, S., Aasen, M. L. y Møgelvang, A. (2024). Understanding university students’ acceptance of ChatGPT: Insights from the UTAUT2 model. Applied Artificial Intelligence, 38(1), 2371168. https://doi.org/10.1080/08839514.2024.2371168 DOI: https://doi.org/10.1080/08839514.2024.2371168

Gravetter, F. J., Wallnau, L. B., Forzano, L. B. y Witnauer, J. E. (2021). Essentials of statistics for the behavioral sciences (10.ª ed.). Cengage.

Haddaway, N. R., Page, M. J., Pritchard, C. C. y McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing Prisma 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis. Campbell Systematic Reviews, 18(2), e1230. https://doi.org/10.1002/cl2.1230 DOI: https://doi.org/10.1002/cl2.1230

Hernández Hernández, M. e Izquierdo Sandoval, M. J. (2020). Cambios curriculares y enseñanza del inglés: Cuestionario de percepción docente. Sinéctica: Revista Electrónica de Educación, 54, 1-22. https://doi.org/10.31391/S2007-7033(2020)0054-012 DOI: https://doi.org/10.31391/S2007-7033(2020)0054-012

Hodge, D. R. y Gillespie, D. F. (2007). Phrase completion scales: A better measurement approach than Likert scales? Journal of Social Service Research, 33(4), 1-12. http://dx.doi.org/10.1300/J079v33n04_01 DOI: https://doi.org/10.1300/J079v33n04_01

Hoy, W. K. y Adams, C. M. (2016). Quantitative research in education: A primer (2.ª ed.). Sage. DOI: https://doi.org/10.4135/9781071984307

Kanont, K., Pingmuang, P., Simasathien, T., Wisnuwong, S., Wiwatsiripong, B., Poonpirome, K., Songkram, N. y Khlaisang, J. (2024). Generative-AI, a learning assistant? Factors influencing higher-ed students’ technology acceptance. Electronic Journal of E-Learning, 22(6), 18-33. https://doi.org/10.34190/ejel.22.6.3196 DOI: https://doi.org/10.34190/ejel.22.6.3196

Köhler, T., Landis, R. S. y Cortina, J. M. (2017). From the editors: Establishing methodological rigor in quantitative management learning and education research: The role of design, statistical methods, and reporting standards. Academy of Management Learning & Education, 16(2), 173-192. https://doi.org/10.5465/amle.2017.0079 DOI: https://doi.org/10.5465/amle.2017.0079

Li, Q. y Qin, Y. (2023). AI in medical education: Medical student perception, curriculum recommendations and design suggestions. BMC Medical Education, 23(1). https://doi.org/10.1186/s12909-023-04700-8 DOI: https://doi.org/10.1186/s12909-023-04700-8

Lodico, M. G., Spaulding, D. T. y Voegtle, K. H. (2006). Methods in educational research: From theory to practice. Jossey-Bass.

Mahmud, A., Sarower, A. H., Sohel, A., Assaduzzaman, M. y 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 DOI: https://doi.org/10.1016/j.array.2024.100339

Marimon, F., Arias Valle, M. B., Coria Augusto, C. J. y Larrea Arnau, C. M. (2025). Del optimismo a la confianza: El impacto de ChatGPT en la confianza de los estudiantes en el aprendizaje asistido por IA. RIED: Revista Iberoamericana de Educación a Distancia, 28(2), 131-153. https://doi.org/10.5944/ried.28.2.43238 DOI: https://doi.org/10.5944/ried.28.2.43238

McKinley, J. y Rose, H. (eds.) (2019). The Routledge handbook of research methods in applied linguistics. Routledge. https://doi.org/10.4324/9780367824471 DOI: https://doi.org/10.4324/9780367824471

Merhi, R. (2011). Expectativas del estudiantado en la universidad del nuevo milenio. La Cuestión Universitaria, 7, 23-31. https://polired.upm.es/index.php/lacuestionuniversitaria/article/view/3353/3418

Mohd Rahim, N. I., Iahad, N. A., Yusof, A. F. y Al-Sharafi, M. A. (2022). AI-based chatbots adoption model for higher-education institutions: A hybrid PLS-SEM-neural network modelling approach. Sustainability, 14(19), 12726. https://doi.org/10.3390/su141912726 DOI: https://doi.org/10.3390/su141912726

Norman, G. (2010). Likert scales, levels of measurement and the “laws” of statistics. Advances in Health Sciences Education, 15(5), 625-632. https://doi.org/10.1007/s10459-010-9222-y DOI: https://doi.org/10.1007/s10459-010-9222-y

O’Mara‐Eves, A. y Thomas, J. (2016). Ongoing developments in meta‐analytic and quantitative synthesis methods: Broadening the types of research questions that can be addressed. Review of Education, 4(1), 5-27. https://doi.org/10.1002/rev3.3062 DOI: https://doi.org/10.1002/rev3.3062

OpenAI. (2022, 30 de noviembre). Presentamos ChatGPT. https://openai.com/es-419/index/chatgpt/

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S. … Alonso-Fernández, S. (2021). Declaración Prisma 2020: Una guía actualizada para la publicación de revisiones sistemáticas. Revista Española de Cardiología, 74(9), 790-799. https://doi.org/10.1016/j.recesp.2021.06.016 DOI: https://doi.org/10.1016/j.rec.2021.07.010

Persson, P. B., Hillmeister, P. y Persson, A. B. (2022). Perception. Acta Physiologica, 235(3), e13842. https://doi.org/10.1111/apha.13842 DOI: https://doi.org/10.1111/apha.13842

Rahman, M. S., Sabbir, M. M., Zhang, J., Moral, I. H. y Hossain, G. M. S. (2023). Examining students’ intention to use ChatGPT: Does trust matter? Australasian Journal of Educational Technology, 39(6), 51-71. https://doi.org/10.14742/ajet.8956 DOI: https://doi.org/10.14742/ajet.8956

Robles Morales, R. E. (2025). Factores determinantes en la adopción de inteligencia artificial en la educación superior dominicana. Cuaderno de Pedagogía Universitaria, 22(43), 79-103. https://doi.org/10.29197/cpu.v22i43.647 DOI: https://doi.org/10.29197/cpu.v22i43.647

Romero Calle, G. P., Tivillin-Gutama, D. M. y Bonisoli, L. (2025). La inteligencia artificial y su influencia en el comportamiento de los estudiantes. Kairós: Revista de Ciencias Económicas, Jurídicas y Administrativas, 8(14), 67-87. https://doi.org/10.37135/kai.03.14.04 DOI: https://doi.org/10.37135/kai.03.14.04

Romero-Rodríguez, J. M., Ramírez-Montoya, M. S., Buenestado-Fernández, M. y 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 DOI: https://doi.org/10.7821/naer.2023.7.1458

Ruiz Mendoza, K. K., Miramontes Arteaga, M.ª A. y Reyna García, C. (2024). Percepciones y expectativas de estudiantes universitarios sobre la IAG. European Public & Social Innovation Review, 9, 1-21. https://doi.org/10.31637/epsir-2024-357 DOI: https://doi.org/10.31637/epsir-2024-357

Saihi, A., Ben-Daya, M., Hariga, M. y As’ad, R. (2024). A structural equation modeling analysis of generative AI chatbots adoption among students and educators in higher education. Computers and Education: Artificial Intelligence, 7, 100274. https://doi.org/10.1016/j.caeai.2024.100274 DOI: https://doi.org/10.1016/j.caeai.2024.100274

Salazar-Altamirano, M. A., Martínez-Arvizu, O. J., Galván-Vela, E., Ravina-Ripoll, R., Hernández-Arteaga, L. G. y Gómez Sánchez, D. (2025). AI as a facilitator of creativity and wellbeing in business students: A multigroup approach between public and private universities. Encontros Bibli: Revista Eletrônica de Biblioteconomia e Ciência da Informação, 30, 1-30. https://doi.org/10.5007/1518-2924.2025.e103485 DOI: https://doi.org/10.5007/1518-2924.2025.e103485

Sallam, M., Salim, N. A., Barakat, M., Al-Mahzoum, K., Al-Tammemi, A. B., Malaeb, D., Hallit, R. y 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 DOI: https://doi.org/10.2196/48254

Scates, D. E. y Hoban, C. F. (1937). Critical questions for the evaluation of research. The Journal of Educational Research, 31(4), 241-254. https://doi.org/10.1080/00220671.1937.10880747 DOI: https://doi.org/10.1080/00220671.1937.10880747

Scott, D. (2007). Resolving the quantitative-qualitative dilemma: A critical realist approach. International Journal of Research & Method in Education, 30(1), 3-17. https://doi.org/10.1080/17437270701207694 DOI: https://doi.org/10.1080/17437270701207694

Segarra Ciprés, M., Grangel Seguer, R. y Belmonte Fernández, Ó. (2024). ChatGPT como herramienta de apoyo al aprendizaje en la educación superior: Una experiencia docente. Revista Tecnología, Ciencia y Educación, 28, 7-44. https://doi.org/10.51302/tce.2024.19083 DOI: https://doi.org/10.51302/tce.2024.19083

Shahzad, M. F., Xu, S. y 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), 46. https://doi.org/10.1186/s41239-024-00478-x DOI: https://doi.org/10.1186/s41239-024-00478-x

Shaker, P. y Ruitenberg, C. (2007). ‘Scientifically‐based research’: The art of politics and the distortion of science. International Journal of Research & Method in Education, 30(2), 207-219. https://doi.org/10.1080/17437270701383545 DOI: https://doi.org/10.1080/17437270701383545

Sigüenza Orellana, J., Andrade Cordero, C. y Chitacapa Espinoza, J. (2024). Validación del cuestionario para docentes: Percepción sobre el uso de ChatGPT en la educación superior. Revista Andina de Educación, 8(1), 000816. https://doi.org/10.32719/26312816.2024.8.1.6 DOI: https://doi.org/10.32719/26312816.2024.8.1.6

Strzelecki, A. (2024). Students’ acceptance of ChatGPT in higher education: An extended unified theory of acceptance and use of technology. Innovative Higher Education, 49(2), 223-245. https://doi.org/10.1007/s10755-023-09686-1 DOI: https://doi.org/10.1007/s10755-023-09686-1

Strzelecki, A., Cicha, K., Rizun, M. y Rutecka, P. (2024). Acceptance and use of ChatGPT in the academic community. Education and Information Technologies, 29(17), 22943-22968. https://doi.org/10.1007/s10639-024-12765-1 DOI: https://doi.org/10.1007/s10639-024-12765-1

Universidad Juárez Autónoma de Tabasco. (2024). Plan de Desarrollo Institucional 2024-2028. https://archivos.ujat.mx/2024/rectoria/PDI-24-28.pdf

Venkatesh, V., Thong, J. Y. y Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178. https://doi.org/10.2307/41410412 DOI: https://doi.org/10.2307/41410412

Villegas-Ch, W., García-Ortiz, J. y Sánchez-Viteri, S. (2024). Personalization of learning: Machine learning models for adapting educational content to individual learning styles. IEEE Access, 12, 121114-121130. https://doi.org/10.1109/ACCESS.2024.3452592 DOI: https://doi.org/10.1109/ACCESS.2024.3452592

Walsh, C., Stein, M. M., Tapping, R., Smith, E. M. y Holmes, N. G. (2021). Exploring the effects of omitted variable bias in physics education research. Physical Review Physics Education Research, 17(1), 010119. https://doi.org/10.1103/PhysRevPhysEducRes.17.010119 DOI: https://doi.org/10.1103/PhysRevPhysEducRes.17.010119

White, P. (2013). Who’s afraid of research questions? The neglect of research questions in the methods literature and a call for question-led methods teaching. International Journal of Research & Method in Education, 36(3), 213-227. https://doi.org/10.1080/1743727X.2013.809413 DOI: https://doi.org/10.1080/1743727X.2013.809413

Zhu, J. y Liu, W. (2020). A tale of two databases: The use of Web of Science and Scopus in academic papers. Scientometrics, 123(1), 321-335. https://doi.org/10.1007/s11192-020-03387-8 DOI: https://doi.org/10.1007/s11192-020-03387-8

Zyphur, M. J. y Pierides, D. C. (2020). Making quantitative research work: From positivist dogma to actual social scientific inquiry. Journal of Business Ethics, 167(1), 49-62. https://doi.org/10.1007/s10551-019-04189-6 DOI: https://doi.org/10.1007/s10551-019-04189-6