Inteligencia artificial generativa y calidad percibida del servicio educativo en instituciones de educación superior
Abstract
La inteligencia artificial generativa se ha incorporado aceleradamente a la educación superior, generando desafíos no solo pedagógicos, sino también gerenciales, asociados a la gobernanza, la integridad académica, la gestión de riesgos y el aseguramiento de la calidad del servicio educativo. El objetivo de este estudio fue analizar la relación entre la gestión institucional percibida del uso de inteligencia artificial generativa y la calidad percibida del servicio educativo en una universidad del Perú. Para ello, se empleó un enfoque cuantitativo, con diseño no experimental y transversal, mediante la aplicación de un cuestionario estructurado a estudiantes de pregrado (n = 312). La calidad percibida se midió a partir de una adaptación de escalas consolidadas de calidad de servicio en educación superior, mientras que la gestión institucional percibida se operacionalizó en las dimensiones de políticas y expectativas, soporte y capacitación, integridad y controles, y gestión de riesgos. Los datos fueron analizados mediante modelamiento de ecuaciones estructurales por mínimos cuadrados parciales, evaluándose la fiabilidad, la validez y las relaciones estructurales del modelo. Los resultados evidenciaron una asociación positiva entre la gestión institucional percibida y la calidad percibida del servicio educativo, con efectos significativos de las dimensiones de políticas y expectativas, integridad y gestión de riesgos, mientras que soporte y capacitación no presentó una asociación significativa. En conjunto, los hallazgos sugieren que la institucionalización visible de reglas, controles y mecanismos de gestión del riesgo en el contexto analizado contribuye a percepciones más favorables sobre la calidad del servicio educativo.
References
Ally, M., & Mishra, S. (2025). Policies for artificial intelligence in higher education: A call for action. Canadian Journal of Learning and Technology, 50(3), 1–12. https://doi.org/10.21432/cjlt28869
Ali, F., Zhou, Y., Hussain, K., Nair, P. K., & Ragavan, N. A. (2016). Does higher education service quality effect student satisfaction, image and loyalty? A study of international students in Malaysian public universities. Quality Assurance in Education, 24(1), 70–94. https://doi.org/10.1108/QAE-02-2014-0008
Baek, C., Tate, T., & Warschauer, M. (2024). “ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI. Computers and Education: Artificial Intelligence, 7, 100294. https://doi.org/10.1016/j.caeai.2024.100294
Bin-Nashwan, S. A., Sadallah, M., & Bouteraa, M. (2023). Use of ChatGPT in academia: Academic integrity hangs in the balance. Technology in Society, 75, 102370. https://doi.org/10.1016/j.techsoc.2023.102370
Bittle, K., & El-Gayar, O. (2025). Generative AI and academic integrity in higher education: A systematic review and research agenda. Information, 16(4), 296. https://doi.org/10.3390/info16040296
Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20, 38. https://doi.org/10.1186/s41239-023-00408-3
Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, 43. https://doi.org/10.1186/s41239-023-00411-8
Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023). Generative AI and ChatGPT: Applications, challenges, and AI-human collaboration. Journal of Information Technology Case and Application Research, 25(3), 277-304. https://doi.org/10.1080/15228053.2023.2233814
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook. Springer International Publishing. https://doi.org/10.1007/978-3-030-80519-7
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Huang, L., Yu, W., Ma, W., Zhong, W., Feng, Z., Wang, H., Chen, Q., Peng, W., Feng, X., Qin, B., & Liu, T. (2025). A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions. ACM Transactions on Information Systems, 43(2), 1-55. https://doi.org/10.1145/3703155
International Organization for Standardization- ISO (2023). Information technology—Artificial intelligence—Management system (International Standard ISO/IEC ٤٢٠٠١:٢٠٢٣; ١.a ed.). ISO. https://www.iso.org/standard/42001
Jin, Y., Yan, L., Echeverria, V., Gašević, D., & Martinez-Maldonado, R. (2025). Generative AI in higher education: A global perspective of institutional adoption policies and guidelines. Computers and Education: Artificial Intelligence, 8, 100348. https://doi.org/10.1016/j.caeai.2024.100348
Johnston, H., Wells, R. F., Shanks, E. M., Boey, T., & Parsons, B. N. (2024). Student perspectives on the use of generative artificial intelligence technologies in higher education. International Journal for Educational Integrity, 20, 2. https://doi.org/10.1007/s40979-024-00149-4
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kock, N. (2017). Common Method Bias: A Full Collinearity Assessment Method for PLS-SEM. En H. Latan & R. Noonan (Eds.), Partial Least Squares Path Modeling (pp. ٢٤٥-٢٥٧). Springer International Publishing. https://doi.org/10.1007/978-3-319-64069-3_11
Lawshe, C. H. (1975). A quantitative approach to content validity. Personnel Psychology, 28(4), 563-575. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x
McDonald, N., Johri, A., Ali, A., & Hingle, A. (2024). Generative Artificial Intelligence in Higher Education: Evidence from an Analysis of Institutional Policies and Guidelines (Versión ١). arXiv. https://doi.org/10.48550/ARXIV.2402.01659
National Institute of Standards and Technology (US). (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI ١٠٠-١; p. NIST AI ١٠٠-١). National Institute of Standards and Technology (U.S.). https://doi.org/10.6028/NIST.AI.100-1
National Institute of Standards and Technology (US). (2024). Artificial intelligence risk management framework: Generative artificial intelligence profile (Error: 600-1; p. error: 600-1). National Institute of Standards and Technology (U.S.). https://doi.org/10.6028/NIST.AI.600-1
OECD. (2023). OECD Digital Education Outlook 2023: Towards an Effective Digital Education Ecosystem. OECD Publishing. https://doi.org/10.1787/c74f03de-en
Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), 101885. https://doi.org/10.1016/j.jsis.2024.101885
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879
Rahman, M. M., & Nasrin, S. (2024). Perceived service quality at higher education institutions: A study on the success factors of total quality management practices in Bangladesh. Social Sciences & Humanities Open, 10, 100997. https://doi.org/10.1016/j.ssaho.2024.100997
Scarfe, P., Watcham, K., Clarke, A., & Roesch, E. (2024). A real-world test of artificial intelligence infiltration of a university examinations system: A “Turing test” case study. PLOS ONE, 19(6), e0305354. https://doi.org/10.1371/journal.pone.0305354
Seitova, M., Temirbekova, Z., Kazykhankyzy, L., Khalmatova, Z., & Çelik, H. E. (2024). Perceived service quality and student satisfaction: A case study at Khoja Akhmet Yassawi University, Kazakhstan. Frontiers in Education, 9, 1492432. https://doi.org/10.3389/feduc.2024.1492432
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
Superintendencia Nacional de Educación Superior Universitaria. (2021). Aprueban el Modelo de Renovación de Licencia Institucional y modifican la Resolución del Consejo Directivo N° 008-2017-SUNEDU/CD que aprobó las “Medidas de simplificación administrativa para el licenciamiento institucional” y el “Reglamento del procedimiento de licenciamiento institucional” (Resolución del Consejo Directivo N° ٠٩١-٢٠٢١-SUNEDU-CD; p. ٤٩). Superintendencia Nacional de Educación Superior Universitaria (SUNEDU). https://www.gob.pe/institucion/sunedu/normas-legales/٢١٣١٧١٤-٠٩١-٢٠٢١-sunedu-cd
Sweeney, S. (2023). Who wrote this? Essay mills and assessment – Considerations regarding contract cheating and AI in higher education. The International Journal of Management Education, 21(2), 100818. https://doi.org/10.1016/j.ijme.2023.100818
Tan, P. S. H., Choong, Y. O., & Chen, I.-C. (2022). The effect of service quality on behavioural intention: The mediating role of student satisfaction and switching barriers in private universities. Journal of Applied Research in Higher Education, 14(4), 1394–1413. https://doi.org/10.1108/JARHE-03-2021-0122
Teeroovengadum, V., Nunkoo, R., Grönroos, C., Kamalanabhan, T. J., & Seebaluck, A. K. (2019). Higher education service quality, student satisfaction and loyalty: Validating the HESQUAL scale and testing an improved structural model. Quality Assurance in Education, 27(4), 427–445. https://doi.org/10.1108/QAE-01-2019-0003
Toscano-Hernández, A. E., Álvarez-González, L. I., Sanzo-Pérez, M. J., & Esparza Rodríguez, S. A. (2024). Calidad del servicio en la educación superior: Una revisión sistemática de la literatura 2007–2023. Estudios Gerenciales, 40(170), 13–30. https://doi.org/10.18046/j.estger.2024.170.6244
United Nations Educational, Scientific and Cultural Organization. (2023). Guidance for generative AI in education and research. UNESCO Publishing.
United Nations Educational, Scientific and Cultural Organization. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084
Wang, H., Dang, A., Wu, Z., & Mac, S. (2024). Generative AI in higher education: Seeing ChatGPT through universities’ policies, resources, and guidelines. Computers and Education: Artificial Intelligence, 7, 100326. https://doi.org/10.1016/j.caeai.2024.100326
Wider, W., Tan, F. P., Tan, Y. P., Lin, J., Fauzi, M. A., Wong, L. S., Tanucan, J. C. M., & Hossain, S. F. A. (2024). Service quality (SERVQUAL) model in private higher education institutions: A bibliometric analysis of past, present, and future prospects. Social Sciences & Humanities Open, 9, 100805. https://doi.org/10.1016/j.ssaho.2024.100805
Xiao, P., Chen, Y., & Bao, W. (2023). Waiting, Banning, and Embracing: An Empirical Analysis of Adapting Policies for Generative AI in Higher Education (Versión ١). arXiv. https://doi.org/١٠.٤٨٥٥٠/ARXIV.٢٣٠٥.١٨٦١٧
Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21, 21. https://doi.org/10.1186/s41239-024-00453-6

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