Student perceptions on the use of AI and biometric analysis in university neuroeducation processes
DOI:
https://doi.org/10.5281/zenodo.21781945Keywords:
neuroeducation, artificial intelligence, educational biometrics, higher educationAbstract
University neuroeducation, strengthened by artificial intelligence and biometric analysis of students, has become an innovative strategy for enhancing teaching and learning processes in higher education. The general objective of this research was to analyze students' perceptions of the use of artificial intelligence (AI) and biometric analysis in university neuroeducation processes. The study was conducted using a quantitative approach, with a non-experimental, cross-sectional, and descriptive-correlational design. The sample consisted of 120 university students who responded to a structured survey based on a Likert-type scale. The instrument was validated by a panel of 10 specialists in education, technology, and scientific research. The results showed that the majority of students perceived the use of AI and educational biometrics favorably, highlighting benefits associated with personalized learning, increased motivation, and improved academic performance. However, concerns related to privacy and the ethical management of biometric data were also identified. It is concluded that these technologies represent an innovative alternative to strengthen the quality of university teaching through more adaptive, humanized processes supported by scientific evidence.
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Agualsaca Calle, D. A., Martínez Hidalgo, F. A., Veintimilla Rizzo, L. M., & Moreno Villalva, E. P. (2025). Neurociencia afectiva y tecnología educativa: el impacto de las emociones en el aprendizaje y el desarrollo socioemocional. Sapiens in Education, 2(4), 1–16. https://doi.org/10.71068/1zb2pj75
Area-Moreira, M., & Adell-Segura, J. (2021). Tecnologías digitales y cambio educativo. Una aproximación crítica. REICE: Revista Iberoamericana de Educación a Distancia, 24(2), 9–21. https://dialnet.unirioja.es/servlet/articulo?codigo=8086687
Baker, T., Smith, L., & Anissa, N. (2021). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta. https://www.nesta.org.uk/report/education-rebooted/
Bastidas González, L. D. (2024). Inteligencia artificial y educación superior: un análisis prospectivo de los desafíos y oportunidades. Sapiens in Higher Education, 1(1), 1-17. https://sapiensjournal.ec/index.php/sihe/article/view/42
Bond, M., Buntins, K., Bedenlier, S., Zawacki-Richter, O., & Kerres, M. (2020). Mapping research in student engagement and educational technology in higher education: a systematic evidence map. International Journal of Educational Technology in Higher Education, 17, 2. https://link.springer.com/article/10.1186/s41239-019-0176-8#citeas
Cabero-Almenara, J., Palacios, A., Llorente-Cejudo, C., & Barroso-Osuna, J. (2025). Aceptación de ChatGPT en educación superior: Actitudes y percepciones del modelo UTAUT2. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 24(1). https://doi.org/10.15366/reice2026.24.1.001
Chen, L., Chen, P., & Lin, Z. (2022). Artificial intelligence in education: A review. IEEE Access, 10, 75264–75278. https://ieeexplore.ieee.org/document/9069875
Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, 22. https://doi.org/10.1186/s41239-023-00392-8
Cuéllar Gaitan, R. A., Betancourt Perdomo, J. D., & Rosales Vidal, E. (2025). Neuroeducación e inteligencia artificial en contexto educativo: estrategias emergentes para la mejora del rendimiento cognitivo. Imperium Académico. Revista Científica, 2(4), 1-13. https://www.researchgate.net/publication/394573636_Neuroeducacion_e_inteligencia_artificial_en_contexto_educativo_estrategias_emergentes_para_la_mejora_del_rendimiento_cognitivoNeuroeducation_and_Artificial_Intelligence_in_Educational_Contexts_Emergin
D’Mello, S., & Graesser, A. C. (2014). Feeling, thinking, and computing with affect-aware learning technologies. In R. A. Calvo, S. K. D’Mello, J. Gratch, & A. Kappas (Eds.), The Oxford handbook of affective computing. Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199942237.013.032
Downes, S. (2019). Recent work in connectivism. European Journal of Open, Distance and E-Learning, 22(2), 113–132. https://eurodljournal.com/articles/10.2478/eurodl-2019-0014
Durand Goyzueta, E. A., Condori Mamani, B., Ramos Chura, E., Avila Inquilla, V. E., Salinas del Carpio, A., Ito Díaz, R. R., & Hilasaca Zea, C. Y. (2026). Cognitive privacy and the use of artificial intelligence in digital platforms for higher education. NeuroData, 3, 160. https://doi.org/10.63688/neurodata2026160
Estrella Romero, V. A. (2025). Impacto de la inteligencia artificial generativa en la evaluación formativa universitaria personalizada. Sapiens in Higher Education, 2(10), 1–14. https://doi.org/10.71068/fw0q7v55
Freire Palacios, V. A., Torres Vizuete, R. E., Alcivar Cuenca, P. E., Cedeño Casquete, G. C., & Ordóñez Ruiz, M. M. (2026). Uso de inteligencia artificial y dependencia cognitiva en estudiantes universitarios. Sage Sphere of Technology, Sciences, Discoveries and Society, 4(1), 1-23. https://doi.org/10.63688/znqmqg17
Giannakos, M., Sharma, K., Pappas, I., Kostakos, V., & Velloso, E. (2019). Multimodal data as a means to understand the learning experience. International Journal of Information Management, 48, 108-119. https://www.sciencedirect.com/science/article/pii/S0268401218312751?via%3Dihub
Hernández-Sampieri, R. & Mendoza, C (2018). Metodología de la investigación. Las rutas cuantitativa, cualitativa y mixta. Mc Graw Hill Education.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign. https://www.consorciosthem.com/wp-content/uploads/2025/02/sthem-ia-07-holmes-fadel-bialik-artificial-intelligence-in-education-promise-and-implications-for-teaching-and-learning-2019.pdf
Immordino-Yang, M., & Damasio, A. (2021). We feel, therefore we learn: The relevance of affective and social neuroscience to education. Mind, Brain, and Education, 1, 3-10. https://doi.org/10.1111/j.1751-228X.2007.00004.x
Knox, J., Wang, Y., & Gallagher, M. (2019). Artificial intelligence and inclusive education speculative futures and emerging practices: Speculative futures and emerging practices. Springer Singapore. https://link.springer.com/book/10.1007/978-981-13-8161-4#accessibility-information
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. (2016). Intelligence unleashed: An argument for AI in education. Pearson. https://www.pearson.com/content/dam/one-dot-com/one-dot-com/global/Files/about-pearson/innovation/open-ideas/IntelligenceUnleashedSPANISH.pdf
Noriega Medina, M. B., Romero Berrones, W. J., Manssur Ponce, I. M., Hidalgo Solórzano, J. L., & Contento Castillo, R. A. (2026). Uso de herramientas de inteligencia artificial y competencias pedagógicas en docentes de educación superior. Sage Sphere Multidisciplinary Studies, 3(1), 1-23. https://doi.org/10.63688/6b6qwk94
Núñez Cayetano, A. M., Lopez Torres, M. F., Strasser López, K. L., & Herrera Vera, D. E. (2026). Inteligencia artificial en la educación: beneficios, desafíos éticos y su impacto en el aprendizaje autónomo. Sapiens in Artificial Intelligence, 3(2), 1-16. https://doi.org/10.71068/jgk10c67
Pazmiño Arcos, A. F. (2024). Revolucionando la educación superior: inteligencia artificial para el aprendizaje personalizado y la transformación docente. Sage Sphere of Technology, Sciences, Discoveries and Society, 2(2), 1-13. https://doi.org/10.63688/ftdznh26
Prinsloo, P., & Slade, S. (2017). Ethics and learning analytics: Charting the (un)charted. In C. Lang, G. Siemens, A. F. Wise, & D. Gašević (Eds.), Handbook of learning analytics (pp. 49–57). Society for Learning Analytics Research. https://doi.org/10.18608/hla17.004
Ramírez-Montoya, M.S., González, C. S. G., Montoya, D. H., Lopez-Caudana, E. O., & Rodríguez-Abitia, G. (Eds.) (2023). Open education for sustainable development: Contributions from emerging technologies and educational innovation. Frontiers Media S. A. https://hdl.handle.net/11285/651091
Redecker, C. (2017). European Framework for the Digital Competence of Educators: DigCompEdu. Publications Office of the European Union. https://publications.jrc.ec.europa.eu/repository/handle/JRC107466
Salas-Pilco, S. Z., Xiao, K., & Oshima, J. (2022). Artificial intelligence and new technologies in inclusive education for minority students: A systematic review. Sustainability, 14(20), 13572. https://www.mdpi.com/2071-1050/14/20/13572
Zaragoza Alvarado, G. A. (2026). Influencia de la Inteligencia Artificial en los procesos de personalización del aprendizaje en la educación superior. Sapiens in Artificial Intelligence, 3(1), 1-15. https://doi.org/10.71068/asq6b765
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