Explorar la Inteligencia Artificial en educación superior mediante Procesamiento del Lenguaje Natural Ligero
un estudio metodológico de prueba de concepto
DOI:
https://doi.org/10.46661/ijeri.12995Palabras clave:
Inteligencia artificial en la educación (AIEd), Respuestas abiertas a encuestas, Procesamiento ligero del lenguaje natural (Light NLP), Análisis semántico, Estudio metodológico exploratorioResumen
La integración de la Inteligencia Artificial en la educación ha generado una amplia gama de perspectivas entre los actores educativos, a menudo capturadas a través de respuestas de encuestas abiertas. Sin embargo, los enfoques para analizar dichos datos bajo condiciones no ideales siguen siendo limitados. Este estudio examina la viabilidad de combinar respuestas abiertas con técnicas de "Procesamiento de Lenguaje Natural (PLN) ligero" para explorar el discurso sobre la IA en la educación superior. Con un diseño exploratorio de prueba de concepto, se contó con dos conjuntos de datos independientes de 31 estudiantes de Pedagogía de España y 35 docentes de Perú que respondieron a cuestiones abiertas similares sobre Inteligencia Artificial. Se recurrió a representaciones semánticas basadas en embeddings, derivadas de la ponderación de términos y la descomposición en valores singulares (SVD) truncada, junto con anclas semánticas y visualización exploratoria para analizar textos heterogéneos. Los resultados muestran que los flujos de trabajo semánticos ligeros pueden generar representaciones interpretables, apoyar la inspección basada en proximidad y permitir la exploración guiada por anclas incluso con muestras pequeñas. En concreto, los datos analizados revelan que, mientras el discurso estudiantil gravita hacia la utilidad pragmática y el aprendizaje autorregulado, la narrativa docente está dominada por la ética y la ansiedad regulatoria. Se concluye reflexionando sobre la capacidad de este enfoque como herramienta escalable para monitorizar el clima educativo en tiempo real.
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Almazán-López, O., Hasbún, H., & Osuna-Acedo, S. (2025). Inteligencia Artificial Generativa e identidad (pos) digital docente. IJERI: International Journal of Educational Research and Innovation, (24), 1-17. https://doi.org/10.46661/ijeri.11160 DOI: https://doi.org/10.46661/ijeri.11160
Álvarez-Herrero, J.-F. (2024). Opinion of Spanish Teachers About Artificial Intelligence and Its Use in Education. En S. Papadakis (Ed.), IoT, AI, and ICT for Educational Applications (pp. 163-172). Springer Nature. https://doi.org/10.1007/978-3-031-50139-5_8 DOI: https://doi.org/10.1007/978-3-031-50139-5_8
Antonenko, P. & Abramowitz, B. (2023) In-service teachers’ (mis)conceptions of artificial intelligence in K-12 science education, Journal of Research on Technology in Education, 55(1), 64-78. https://doi.org/10.1080/15391523.2022.2119450 DOI: https://doi.org/10.1080/15391523.2022.2119450
Antunes dos Santos, R., y Reategui, E. B. (2025). Uso de inteligencia artificial generativa y análisis de palabras clave para apoyar la planificación de proyectos de investigación en la educación superior. RELATEC. Revista Latinoamericana de Tecnología Educativa, 24(2), 87-104. https://doi.org/10.17398/1695-288X.24.2.87 DOI: https://doi.org/10.17398/1695-288X.24.2.87
Arranz-García, O., Romero García, M. del C., & Alonso-Secades, V. (2025). Perceptions, strategies, and challenges of teachers in the integration of artificial intelligence in primary education: A systematic review. Journal of Information Technology Education: Research, 24, Article 6. https://doi.org/10.28945/5458 DOI: https://doi.org/10.28945/5458
Benoit, K., Watanabe, K., Wang, H., Nulty, P., Obeng, A., Müller, S., & Matsuo, A. (2018). Quanteda: An R package for the quantitative analysis of textual data. Journal of Open Source Software, 3(30), 774. https://doi.org/10.21105/joss.00774 DOI: https://doi.org/10.21105/joss.00774
Bewersdorff, A., Zhai, X., Roberts, J., & Nerdel, C. (2023). Myths, mis-and preconceptions of artificial intelligence: A review of the literature. Computers and Education: Artificial Intelligence, 4, 100143. https://doi.org/10.1016/j.caeai.2023.100143 DOI: https://doi.org/10.1016/j.caeai.2023.100143
Bover, A. (2013). Herramientas de reflexividad y posicionalidad para promover la coherencia teórico-metodológica al inicio de una investigación cualitativa. Enfermería Clínica, 23(1), 33-36. https://doi.org/10.1016/j.enfcli.2012.11.007 DOI: https://doi.org/10.1016/j.enfcli.2012.11.007
Cabero-Almenara, J., Palacios-Rodríguez, A., Llorente-Cejudo, C., y Barroso-Osuna, J. (2026). Aceptación de ChatGPT en Educación Superior: Actitudes y Percepciones del modelo UTAUT2. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 24(1), 1-17. https://doi.org/10.15366/reice2026.24.1.001 DOI: https://doi.org/10.15366/reice2026.24.1.001
Chen, C., & Shu, K. (2023). Combating Misinformation in the Age of LLMs: Opportunities and Challenges. arXiv Computers and Society, arXiv:2311.05656. https://doi.org/10.48550/arXiv.2311.05656
Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., & Cheng, M. (2023). Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence, 4, 100118. https://doi.org/10.1016/j.caeai.2022.100118 DOI: https://doi.org/10.1016/j.caeai.2022.100118
Cordón, 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 DOI: https://doi.org/10.6018/riite.591581
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., & Harshman, R. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-407. https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 DOI: https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9
García Peñalvo, F. J., Alier, M., Pereira, J., & Casany, M. J. (2024). Safe, Transparent, and Ethical Artificial Intelligence: Keys to Quality Sustainable Education (SDG4). IJERI: International Journal of Educational Research and Innovation, (22), 1-21. https://doi.org/10.46661/ijeri.11036 DOI: https://doi.org/10.46661/ijeri.11036
García-López, I. M., & Trujillo-Liñán, L. (2025). Ethical and regulatory challenges of Generative AI in education: A systematic review. Frontiers in Education, (10), 1565938. https://doi.org/10.3389/feduc.2025.1565938 DOI: https://doi.org/10.3389/feduc.2025.1565938
Gavira Durón, N., & Jiménez-Preciado, A. L. (2025). Exploring the role of AI in higher education: A natural language processing analysis of emerging trends and discourses. The TQM Journal, 37(19). https://doi.org/10.1108/TQM-10-2024-0376 DOI: https://doi.org/10.1108/TQM-10-2024-0376
Grimmer, J., Roberts, M. E., & Stewart, B. M. (2022). Text as data: A new framework for machine learning and the social sciences. Princeton University Press.
Horban, O., Stadnyk, M., Vintoniv-Bakharieva, S., Panasiuk, L., & Yatyshchuk, O. (2025). Artificial Intelligence as an Integral Component of the Digital Culture within Contemporary Higher Education. Journal of Educational Technology and Learning Creativity. 3(2), 451-467. https://doi.org/10.37251/jetlc.v3i2.2333 DOI: https://doi.org/10.37251/jetlc.v3i2.2333
Huang, C., Zhang, Z., Mao, B., & Yao, X. (2023). An Overview of Artificial Intelligence Ethics. IEEE Transactions on Artificial Intelligence, 4(4), 799-819. https://doi.org/10.1109/TAI.2022.3194503 DOI: https://doi.org/10.1109/TAI.2022.3194503
Ilieva, G., Yankova, T., Ruseva, M., & Kabaivanov, S. (2025). A Framework for Generative AI-Driven Assessment in Higher Education. Information, 16(6), 472. https://doi.org/10.3390/info16060472 DOI: https://doi.org/10.3390/info16060472
Kangwa, D., Msafiri, M. M., & Fute, A. 2025. Exploring the Factors That Promote a Balance Between Academic Integrity and the Effective Use of GenAI Tools in Higher Education: A Systematic Review. Journal of Computer Assisted Learning, 41(5), e70109. https://doi.org/10.1111/jcal.70109 DOI: https://doi.org/10.1111/jcal.70109
Khlaif, Z., Sanmugam, M., Joma, A., Odeh, A., & Barham, K. (2022). Factors Influencing Teacher’s Technostress Experienced in Using Emerging Technology: A Qualitative Study. Technology, Knowledge and Learning, 28, 865-899. https://doi.org/10.1007/s10758-022-09607-9 DOI: https://doi.org/10.1007/s10758-022-09607-9
Li, L., Li, L., Zhong, B., & Yang, Y. (2024). A scientometric analysis of technostress in education from 1991 to 2022. Education and Information Technologies, 29, 23155-23183. https://doi.org/10.1007/s10639-024-12781-1 DOI: https://doi.org/10.1007/s10639-024-12781-1
Lițan, D. E. (2025). The impact of technostress generated by Artificial Intelligence on the quality of life: The mediating role of positive and negative affect. Behavioral Sciences, 15(4), 552. https://doi.org/10.3390/bs15040552 DOI: https://doi.org/10.3390/bs15040552
López-Chila, R., Llerena-Izquierdo, J., Sumba-Nacipucha, N., & Cueva-Estrada, J. (2024). Artificial Intelligence in Higher Education: An Analysis of Existing Bibliometrics. Education Sciences, 14(1), 47. https://doi.org/10.3390/educsci14010047 DOI: https://doi.org/10.3390/educsci14010047
Mateus, J., Lugo, N., Cappello, G., & Guerrero-Pico, M. (2024). Communication educators facing the arrival of generative artificial intelligence: Exploration in Mexico, Peru, and Spain. Digital Education Review, (45), 38-55. https://doi.org/10.1344/der.2024.45.106-114 DOI: https://doi.org/10.1344/der.2024.45.106-114
McInnes, L., Healy, J., & Melville, J. (2018). UMAP: Uniform manifold approximation and projection for dimension reduction. arXiv. https://doi.org/10.21105/joss.00861 DOI: https://doi.org/10.21105/joss.00861
Nelson, L. K. (2020). Computational grounded theory: A methodological framework. Sociological Methods & Research, 49(1), 3-42. https://doi.org/10.1177/0049124117729703 DOI: https://doi.org/10.1177/0049124117729703
Ooms, J. (2024). irlba: Fast truncated singular value decomposition and principal components analysis for large sparse matrices (R package version 2.3.5.1) [Software de ordenador]. CRAN. https://CRAN.R-project.org/package=irlba
R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
Sahin, C. (2024). Artificial intelligence technologiesand ethics in educational processes: solution suggestions and results. Innoeduca. International Journal of Technology and Educational Innovation, 10(2), 201-216. https://doi.org/10.24310/ijtei.102.2024.19806 DOI: https://doi.org/10.24310/ijtei.102.2024.19806
Sergeeva, O. V., Zheltukhina, M. R., Shoustikova, T., Tukhvatullina, L. R., Dobrokhotov, D. A., & Kondrashev, S. V. (2025). Understanding higher education students’ adoption of generative AI technologies: An empirical investigation using UTAUT2. Contemporary Educational Technology, 17(2), ep571. https://doi.org/10.30935/cedtech/16039 DOI: https://doi.org/10.30935/cedtech/16039
Suso-Vega, J. A., Meneses-La-Riva, M. E., & Fernández-Bedoya, V. H. (2024). Adapting to a New Normal: Peruvian University Faculty’s Experiences with Techno-Stress Post-Covid-19. F1000Research, (12), 1381. https://doi.org/10.12688/f1000research.141432.3 DOI: https://doi.org/10.12688/f1000research.141432.3
Trusz, S., & Demeshkant, N. (2025). Teachers’ technological, pedagogical, and content knowledge related to artificial intelligence as a protective factor against technostress and techno-anxiety. Studia z Teorii Wychowania, 16(4), 303-327. https://doi.org/10.5604/01.3001.0055.5084 DOI: https://doi.org/10.5604/01.3001.0055.5084
Villarrubia Zúñiga, M. S., Ortiz Jiménez, M., y González García, P. (2025). Artificial intelligence chatbots in language learning for disadvantaged populations (migrants and aboriginal): State of the art and challenge. EDMETIC, Revista de Educación Mediática y TIC, 14(2), 1-21. https://doi.org/10.21071/edmetic.v14i2.17263 DOI: https://doi.org/10.21071/edmetic.v14i2.17263
Villegas-José, V., y Delgado-García, M. (2024). Inteligencia artificial: revolución educativa innovadora en la Educación Superior. Pixel-Bit. Revista de Medios y Educación, 71, 159-177. https://doi.org/10.12795/pixelbit.107760 DOI: https://doi.org/10.12795/pixelbit.107760
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., … & Yutani, H. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686 DOI: https://doi.org/10.21105/joss.01686
Wu, R., & Yu, Z. (2023). Do AI chatbots improve students learning outcomes? Evidence from a meta-analysis. British Journal of Educational Technology, 1–24. https://doi.org/10.1111/bjet.13334 DOI: https://doi.org/10.1111/bjet.13334
Xu, X., Qiao, L., Cheng, N., Liu, H., & Zhao, W. (2025). Enhancing self-regulated learning and learning experience in generative AI environments: The critical role of metacognitive support. Computers and Education: Artificial Intelligence, 8, 100384. https://doi.org/10.1016/j.caeai.2025.100384 DOI: https://doi.org/10.1016/j.caeai.2025.100384
Zhang, H., & Cao, J. (2025). From digital disruption to mental health: The impact of AI-induced educational anxiety on teacher well-being in the era of smart education. BMC Public Health, (25), 4010. https://doi.org/10.1186/s12889-025-25372-7 DOI: https://doi.org/10.1186/s12889-025-25372-7
Zhang, S., Xu, J., & Alvero, A. J. (2025). Generative AI meets open-ended survey responses: Research participant use of AI and homogenization. Sociological Methods & Research, 54(3), 1197-1242. https://doi.org/10.1177/00491241251327130 DOI: https://doi.org/10.1177/00491241251327130
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Derechos de autor 2026 Antonio Matas-Terron, Jose M. Ríos Ariza, Antonio Luque de la Rosa, José J. Sánchez Amate

Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.




