Inteligencia Artificial en la Educación Superior: Oportunidades, Riesgos y Desafíos Éticos
Palabras clave:
inteligencia artificial, educación superior, desafíos éticosResumen
Este artículo de revisión sistemática analiza el impacto de la inteligencia artificial (IA) en la educación superior, enfocándose en sus oportunidades, riesgos y desafíos éticos. El objetivo principal es sintetizar la evidencia científica sobre el uso de IA en contextos universitarios, identificando tendencias, beneficios y limitaciones para la enseñanza, el aprendizaje y la gestión institucional. La metodología empleada se basa en el enfoque PRISMA 2020, incluyendo la búsqueda de estudios en bases de datos académicas, la selección de artículos bajo criterios de inclusión y exclusión, y la extracción y análisis de datos mediante categorías analíticas. Los hallazgos revelan que la IA ofrece oportunidades significativas para la personalización del aprendizaje, la retroalimentación automatizada, la analítica educativa y la mejora de la gestión académica, contribuyendo a una educación más flexible e inclusiva. Sin embargo, también se identifican riesgos importantes, como sesgos algorítmicos, opacidad de los modelos, dependencia tecnológica y deshumanización de la enseñanza. Se destacan desafíos éticos relacionados con la privacidad, la equidad, la transparencia y la responsabilidad institucional, lo que demanda políticas de gobernanza y formación docente crítica para una integración responsable de la IA en la educación superior.
Referencias
Ausubel, D. P. (2000/2002). The Acquisition and Retention of Knowledge: A Cognitive View. Kluwer Academic Publishers. https://doi.org/10.1007/978-94-015-9454-7
Bates, A. W., & Sangrà, A. (2020). Teaching in a Digital Age: Guidelines for Designing Teaching and Learning. Tony Bates Associates Ltd.
Benjamin, R. (2019). Race after Technology: Abolitionist Tools for the New Jim Code. Polity Press.
Biesta, G. (2015). Beyond Learning: Democratic Education for a Human Future. Routledge.
Black, P., & Wiliam, D. (2009). Developing the theory of formative assessment. Educational Assessment, Evaluation and Accountability, 21(1), 5–31. https://doi.org/10.1007/s11092-008-9068-5
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Burgstahler, S. (2015). Universal Design in Higher Education: From Principles to Practice. Harvard Education Press.
Cabero-Almenara, J., Barroso-Osuna, J., & Palacios-Rodríguez, A. (2021). Competencias digitales docentes y formación en entornos universitarios. Revista de Educación a Distancia, 65, 1–20.
Cordón García, O. (2023). Inteligencia Artificial en Educación Superior: Oportunidades y Riesgos. RiiTE. Revista Interuniversitaria de Investigación en Tecnología Educativa, (15), 16–27. https://doi.org/10.6018/riite.591581
Del Valle, E. (2025). La inteligencia artificial en la educación: potencial transformador, riesgos de sesgo y desafíos éticos. Revista Iberoamericana de Educación, 99(1), 79–93. https://doi.org/10.35362/rie9916838
Diakopoulos, N. (2016). Accountability in algorithmic decision-making. Communications of the ACM, 59(2), 56–62.
Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press.
Floridi, L. (2019). The Ethics of Artificial Intelligence. Oxford University Press.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707.
Galindo-Domínguez, H., Campo, L., Delgado, N., & Sainz de la Maza, M. (2025). Relationship between the use of ChatGPT for academic purposes and plagiarism: the influence of student-related variables on cheating behavior. Interactive Learning Environments, 33(6), 4047–4061. https://doi.org/10.1080/10494820.2025.2457351
Gallo Macías, G. G., Pacheco Pérez, X. O., & Sánchez Macías, R. E. (2024). La inteligencia artificial en la educación superior: oportunidades y desafíos. Una revisión sistemática. RECIMUNDO, 8(4), 28–39. https://doi.org/10.26820/recimundo/8.(4).diciembre.2024.28-39
Gašević, D., Dawson, S., & Siemens, G. (2016). Let's not forget: Learning analytics are about learning. TechTrends, 60(1), 64–71.
Giroux, H. A. (2020). The Terror of the Unthinkable: Reimagining the Future of Education. Routledge.
Gusenbauer, M., & Haddaway, N. R. (2020). Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research Synthesis Methods, 11(2), 181–217. https://doi.org/10.1002/jrsm.1378
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.
Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., Rodrigo, M. T., Cukurova, M., Bittencourt, I. I., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(3), 504–526. https://doi.org/10.1007/s40593-021-00239-1
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
Lelescu, A., Sava, S., Grosseck, G., & Malița, L. (2025). Exploring trust in generative AI for higher education institutions: a systematic literature review focused on educators. Humanities and Social Sciences Communications, 12, 1961. https://doi.org/10.1057/s41599-025-06253-1
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson.
Miao, F., & Holmes, W. (2023). Guidance for Generative AI in Education and Research. UNESCO. https://www.unesco.org/en/articles/guidance-generative-ai-education-and-research
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
Nicol, D. J. (2010). From monologue to dialogue: Improving written feedback in mass higher education. Assessment & Evaluation in Higher Education, 35(5), 501–517.
Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
OECD. (2021). OECD Digital Education Outlook 2021: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. OECD Publishing.
O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Pardo, A., & Siemens, G. (2014). Ethical and privacy principles for learning analytics. British Journal of Educational Technology, 45(3), 438–450.
Pasquale, F. (2015). The Black Box Society: The Secret Algorithms that Control Money and Information. Harvard University Press.
Redecker, C. (2017). European Framework for the Digital Competence of Educators: DigCompEdu (JRC107466). Publications Office of the European Union.
Reina Marín, Y., Cruz Caro, O., Carrasco Rituay, A. M., Guimac Llanos, K. A., Tarrillo Perez, D., Sánchez Bardales, E., Alva Tuesta, J. N., & Chávez Santos, R. (2025). Ethical challenges associated with the use of artificial intelligence in university education. Journal of Academic Ethics, 23(4), 2443–2467. https://doi.org/10.1007/s10805-025-09660-w
Romero Alonso, R., Araya Carvajal, K., & Reyes Acevedo, N. (2025). Rol de la Inteligencia Artificial en la personalización de la educación a distancia: una revisión sistemática. RIED. Revista Iberoamericana de Educación a Distancia, 28(1), 9–36. https://doi.org/10.5944/ried.28.1.41538
Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed., Global ed.). Pearson.
Selwyn, N. (2019). Should Robots Replace Teachers? AI and the Future of Education. Polity Press.
Siemens, G., & Baker, R. S. (2012). Learning analytics and educational data mining: Towards communication and collaboration. Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, 252–254.
Slade, S., & Prinsloo, P. (2013). Learning analytics: ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221. https://doi.org/10.1080/00461520.2011.611369
Viberg, O., Hatakka, M., Bälter, O., & Mavroudi, A. (2018). The current landscape of learning analytics in higher education. Computers in Human Behavior, 89, 98–110. https://doi.org/10.1016/j.chb.2018.07.027
Williamson, B. (2017). Big Data in Education: The Digital Future of Learning, Policy and Practice. Sage. https://doi.org/10.4135/9781529714920
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995
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. https://doi.org/10.1186/s41239-019-0171-0
Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2
Publicado
Número
Sección
Licencia
Derechos de autor 2026 Carlos Eduardo Mamani Flores (Autor/a)
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Todo el contenido de Global Net se publica bajo la Licencia
Creative Commons Atribución 4.0 Internacional (CC BY 4.0).
Los autores conservan los derechos de autor y otorgan a la
revista el derecho de primera publicación. Se permite la libre
copia, distribución, adaptación y reutilización del contenido
para cualquier fin legal, siempre que se otorgue la atribución
adecuada a los autores y a la fuente original.
Más información en: https://creativecommons.org/licenses/by/4.0/
