Métricas alternativas
Palabras clave
Higher education
Gender
Educational technology
Digital literacy. Inteligencia artificial
Educación superior
Género
Tecnología educativa
Alfabetización digital
Cómo citar
Resumen
La creciente integración de la inteligencia artificial en la educación superior requiere comprender las actitudes del estudiantado hacia esta tecnología, especialmente en contextos latinoamericanos donde la investigación es escasa y los instrumentos de medición carecen de validación cultural. Este estudio analizó las diferencias de género en las actitudes hacia la IA en 1,912 estudiantes universitarios de Arequipa, Perú, utilizando la escala AIAS-4 adaptada al español. Se aplicaron análisis factorial exploratorio (n=1,165) y confirmatorio (n=776), confirmando una estructura unidimensional con propiedades psicométricas adecuadas (α=.884; CFI=.991; RMSEA=.076). Los resultados revelaron que las estudiantes mujeres presentan actitudes significativamente más positivas hacia la IA que los hombres (M=3.15 vs. M=2.97; p<.001; d=0.204), contradiciendo patrones reportados en contextos europeos y asiáticos. Se observó además una tendencia descendente en actitudes positivas conforme avanza el año académico. Estos hallazgos evidencian una paradoja crítica: actitudes favorables coexisten con subrepresentación estructural femenina en el campo tecnológico. Se discuten las implicaciones para el diseño de políticas educativas con perspectiva de género que promuevan participación equitativa en inteligencia artificial.
Citas
Aiken, L. R. (1980). Content Validity and Reliability of Single Items or Questionnaires. Educational and Psychological Measurement, 40(4), 955-959. https://doi.org/10.1177/001316448004000419
Alam, A. (2022). Psychological, Sociocultural, and Biological Elucidations for Gender Gap in STEM Education: A Call for Translation of Research into Evidence-Based Interventions. 95-107. https://doi.org/10.2991/ahsseh.k.220105.012
Ancheta-Arrabal, A., Pulido-Montes, C., & Carvajal-Mardones, V. (2021). Gender Digital Divide and Education in Latin America: A Literature Review. Education Sciences, 11(12). https://doi.org/10.3390/educsci11120804
Ato, M., López-García, J. J., & Benavente, A. (2013). Un sistema de clasificación de los diseños de investigación en psicología. Anales de Psicología / Annals of Psychology, 29(3), 1038-1059. https://doi.org/10.6018/analesps.29.3.178511
Aviles-Valenzuela, A., Acosta-Barreno, K., Espinel-Obregoso, F. P., & Carrasco, A. S. E. (2025). Trends and Analysis of Artificial Intelligence Research in Latin America (2013–2023). Open Information Science, 9(1). https://doi.org/10.1515/opis-2025-0023
Beig, S., & Qasim, S. H. (2023). Assessing Students’ Attitude Towards Artificial Intelligence with Respect to Gender and Use of Computer and Mobile Devices. IJFMR - International Journal For Multidisciplinary Research, 5(3). https://doi.org/10.36948/ijfmr.2023.v05i03.4130
Bittle, K., & El-Gayar, O. (2025). Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda. Information, 16(4). https://doi.org/10.3390/info16040296
Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., Chong, S. W., & Siemens, G. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21(1), 4. https://doi.org/10.1186/s41239-023-00436-z
Browne, M. W., & Cudeck, R. (1992). Alternative ways of assessing model fit. Sociological Methods & Research, 21(2), 230–258. https://doi.org/10.1177/0049124192021002005
Cai, Z., Fan, X., & Du, J. (2017). Gender and attitudes toward technology use: A meta-analysis. Computers & Education, 105, 1-13. https://doi.org/10.1016/j.compedu.2016.11.003
Carvajal, D., Franco, C., & Isaksson, S. (2025). Will Artificial Intelligence Get in the Way of Achieving Gender Equality? Discussion Paper Series in Economics, Discussion Paper Series in Economics, Article 3/2024. https://ideas.repec.org//p/hhs/nhheco/2024_003.html
Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 100197. https://doi.org/10.1016/j.caeai.2023.100197
Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences (2.a ed.). Routledge. https://doi.org/10.4324/9780203771587
Contreras, F. C., & Olaya, J. (2025). La inteligencia artificial en la educación superior peruana: Tendencias y desafíos. Revista Tribunal, 5(13), 39-61. https://doi.org/10.59659/revistatribunal.v5i13.254
Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge University Press. https://doi.org/10.1017/CBO9780511761676
Fernández-Miranda, M., Román-Acosta, D., Jurado-Rosas, A. A., Limón-Dominguez, D., Torres-Fernández, C., Fernández-Miranda, M., Román-Acosta, D., Jurado-Rosas, A. A., Limón-Dominguez, D., & Torres-Fernández, C. (2024). Artificial Intelligence in Latin American Universities: Emerging Challenges. Computación y Sistemas, 28(2), 435-450. https://doi.org/10.13053/cys-28-2-4822
Ferguson, C. J. (2009). An effect size primer: A guide for clinicians and researchers. Professional Psychology: Research and Practice, 40(5), 532–538. https://doi.org/10.1037/a0015808
Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE.
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
Frumin, I., Vorochkov, A., Kiryushina, M., Platonova, D., & Terentiev, E. (2026). Mapping the Generative AI Research in Higher Education: 2022–2024 Insights. Higher Education Quarterly, 80(1), e70075. https://doi.org/10.1111/hequ.70075
García-López, I. M., & Trujillo-Liñán, L. (2025). Ethical and regulatory challenges of Generative AI in education: A systematic review. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1565938
Grassini, S. (2023). Development and validation of the AI attitude scale (AIAS-4): A brief measure of general attitude toward artificial intelligence. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1191628
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
Hajam, K. B., & Gahir, S. (2024). Unveiling the Attitudes of University Students Toward Artificial Intelligence. Journal of Educational Technology Systems, 52(3), 335-345. https://doi.org/10.1177/00472395231225920
Heredia Pérez, G., Benavides Galvez, J. B., Rojas Campos, E., & Sanchez Bustamante, E. (2025). Inteligencia artificial y pedagogía: Retos para la educación superior en el Perú. https://doi.org/10.5281/zenodo.16997148
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
Jovanović, M., & Campbell, M. (2022). Generative Artificial Intelligence: Trends and Prospects. Computer, 55(10), 107-112. https://doi.org/10.1109/MC.2022.3192720
Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31–36. https://doi.org/10.1007/BF02291575
Katsantonis, A., & Katsantonis, I. G. (2024). University Students’ Attitudes toward Artificial Intelligence: An Exploratory Study of the Cognitive, Emotional, and Behavioural Dimensions of AI Attitudes. Education Sciences, 14(9). https://doi.org/10.3390/educsci14090988
Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The Performance of RMSEA in Models With Small Degrees of Freedom. Sociological Methods & Research, 44(3), 486-507. https://doi.org/10.1177/0049124114543236
Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Kofinas, A. K., Tsay, C. H.-H., & Pike, D. (2025). The impact of generative AI on academic integrity of authentic assessments within a higher education context. British Journal of Educational Technology, 56(6), 2522-2549. https://doi.org/10.1111/bjet.13585
Leavy, S. (2018). Gender bias in artificial intelligence: The need for diversity and gender theory in machine learning. Proceedings of the 1st International Workshop on Gender Equality in Software Engineering, GE ’18, 14-16. https://doi.org/10.1145/3195570.3195580
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023). Trustworthy AI: From Principles to Practices. ACM Comput. Surv., 55(9), 177:1-177:46. https://doi.org/10.1145/3555803
Li, B., Tan, Y. L., Wang, C., & Lowell, V. (2025). Two years of innovation: A systematic review of empirical generative AI research in language learning and teaching. Computers and Education: Artificial Intelligence, 9, 100445. https://doi.org/10.1016/j.caeai.2025.100445
Lloret-Segura, S., Ferreres-Traver, A., Hernández-Baeza, A., & Tomás-Marco, I. (2014). El Análisis Factorial Exploratorio de los Ítems: Una guía práctica, revisada y actualizada. Anales de Psicología, 30(3), 1151-1169. https://doi.org/10.6018/analesps.30.3.199361
Merino-Soto, C. (2023). Coeficientes V de Aiken: Diferencias en los juicios de validez de contenido. MHSalud, 20(1), 1-14.
Møgelvang, A., Bjelland, C., Grassini, S., & Ludvigsen, K. (2024). Gender Differences in the Use of Generative Artificial Intelligence Chatbots in Higher Education: Characteristics and Consequences. Education Sciences, 14(12). https://doi.org/10.3390/educsci14121363
Ofosu-Ampong, K. (2024). Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions. Telematics and Informatics Reports, 14, 100127. https://doi.org/10.1016/j.teler.2024.100127
Pallant, J. (2020). SPSS survival manual (7th ed.). McGraw-Hill.
Peláez-Sánchez, I. C., George Reyes, C. E., & Glasserman-Morales, L. D. (2023). Gender digital divide in education 4.0: A systematic literature review of factors and strategies for inclusion. Future in Educational Research, 1(2), 129-146. https://doi.org/10.1002/fer3.16
Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269-275. https://doi.org/10.1016/j.ijresmar.2023.03.001
Purificato, E., Lorenzo, F., Fallucchi, F., & De Luca, E. W. (2023). The Use of Responsible Artificial Intelligence Techniques in the Context of Loan Approval Processes. International Journal of Human–Computer Interaction, 39(7), 1543-1562. https://doi.org/10.1080/10447318.2022.2081284
Russo, C., Romano, L., Clemente, D., Iacovone, L., Gladwin, T. E., & Panno, A. (2025). Gender differences in artificial intelligence: The role of artificial intelligence anxiety. Frontiers in Psychology, 16. https://doi.org/10.3389/fpsyg.2025.1559457
Sáinz, M., Meneses, J., López, B.-S., & Fàbregues, S. (2016). Gender Stereotypes and Attitudes Towards Information and Communication Technology Professionals in a Sample of Spanish Secondary Students. Sex Roles, 74(3), 154-168. https://doi.org/10.1007/s11199-014-0424-2
Salas-Pilco, S. Z., & Yang, Y. (2022). Artificial intelligence applications in Latin American higher education: A systematic review. International Journal of Educational Technology in Higher Education, 19(1), 21. https://doi.org/10.1186/s41239-022-00326-w
Singh, S., Rahul, K., Paliwal, M., Wani, I. A., & Suri, S. (2025). Gendering the digital divide: A systematic review of women’s digital inclusion challenges and emerging research directions. Digital Transformation and Society, 4(4), 503-531. https://doi.org/10.1108/DTS-04-2025-0083
Stöhr, C., Ou, A. W., & Malmström, H. (2024). Perceptions and usage of AI chatbots among students in higher education across genders, academic levels and fields of study. Computers and Education: Artificial Intelligence, 7, 100259. https://doi.org/10.1016/j.caeai.2024.100259
Sultana, A., Abdul Latheef, N., Siby, N., & Ahmad, Z. (2025). Exploring Students’ Attitudes Toward Artificial Intelligence (AI): Psychometric Validation of AI-Attitude Scale. Sage Open, 15(4), 21582440251378375. https://doi.org/10.1177/21582440251378375
Tapullima Mori, C., Mamani Benito, O. J., Turpo Chaparro, J., Olivas Ugarte, L. O., & Carranza Esteban, R. F. (2024). Inteligencia artificial en la educación universitaria: Revisión bibliométrica en Scopus y Web of Science. Revista Electrónica Educare, 28(Extra 1), 1.
Tellhed, U., Björklund, F., & Kallio Strand, K. (2023). Tech-Savvy Men and Caring Women: Middle School Students’ Gender Stereotypes Predict Interest in Tech-Education. Sex Roles, 88(7), 307-325. https://doi.org/10.1007/s11199-023-01353-1
The jamovi project. (2024). jamovi (Version 2.3) [Computer software]. https://www.jamovi.org
UNESCO Biblioteca Digital. (2024). UNESCO Call to Action: Closing the gender gap in science. bit.ly/3PoZ2zy
Valdivieso, T., & González, O. (2025). Generative AI Tools in Salvadoran Higher Education: Balancing Equity, Ethics, and Knowledge Management in the Global South. Education Sciences, 15(2). https://doi.org/10.3390/educsci15020214
World Economic Forum. (2023). The global risks report 2023 (18th ed). World Economic Forum. bit.ly/4rg9Ux2
Yau, H. K., & Cheng, A. L. F. (2012). Gender Difference of Confidence in Using Technology for Learning. Journal of Technology Studies, 38(2). https://doi.org/10.21061/jots.v38i2.a.2
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Derechos de autor 2026 Pixel-Bit. Revista de Medios y Educación