Artificial Intelligence as a Support Tool for University Teaching: Opportunities, Challenges and Educational Implications
DOI:
https://doi.org/10.55204/trc.v6i1.e582Palabras clave:
Artificial Intelligence; Higher Education; University Teaching; Educational Technology; Learning Analytics; Digital Transformation; Intelligent Tutoring Systems.Resumen
Artificial Intelligence (AI) has emerged as a key support tool for university teaching, transforming instructional processes, learning environments, and academic decision-making. This article presents a bibliographic review analyzing the role of AI in higher education, focusing on its pedagogical applications, benefits, and challenges. The findings show that AI contributes to personalized learning, adaptive instruction, automated assessment, and data-driven teaching strategies that enhance student engagement and academic performance. AI systems also support teachers by reducing administrative workload, improving feedback processes, and strengthening instructional planning. However, ethical concerns, data privacy issues, digital competency gaps, and institutional readiness remain significant challenges for its effective implementation. The study concludes that AI represents a strategic resource for educational innovation and digital transformation in universities, reinforcing teaching practices while complementing, not replacing, the human role of educators in fostering critical thinking, ethical awareness, and meaningful learning experiences for future educational development.
Descargas
Referencias
Agaoglu, M. (2016). Predicting instructor performance using data mining techniques in higher education. IEEE Access, 4, 2379–2387.
Alexander, A., Arnedo-Moreno, J., Daradoumis, T., & Guerrero-Roldán, A.-E. (2017). Using learning analytics to support academic integrity. International Review of Research in Open and Distributed Learning, 18(5), 192–210.
Babić, I. Ð. (2017). Machine learning methods for predicting students’ academic motivation. Croatian Operational Research Review, 8, 443–461.
Bhattacharya, P., & Nakhare, S. (2019). Exploring AI-enabled intelligent tutoring systems in professional education. In Proceedings of the Sixth International Conference on Information Technology Trends (pp. 230–233). IEEE.
Bond, M., Marín, V. I., Dolch, C., Bedenlier, S., & Zawacki-Richter, O. (2018). Digital transformation in German higher education: Student and teacher perceptions and use of digital media. International Journal of Educational Technology in Higher Education, 15(1), 1–20.
Castañeda, L., & Selwyn, N. (2018). More than tools? Making sense of the ongoing digitalization of higher education. International Journal of Educational Technology in Higher Education, 15, 1–10.
Dašić, P., Dašić, J., Crvenković, B., & Šerifi, V. (2016). A review of intelligent tutoring systems in e-learning. Annals of the University of Oradea, 3, 85–89.
Duzhin, F., & Gustafsson, A. (2018). Machine learning-based applications for self-evaluation of teaching styles and instructional tools. Education Sciences, 8(1), 7.
Ferretti, F., Santi, G. R. P., Del Zozzo, A., Garzetti, M., & Bolondi, G. (2021). Assessment practices and teachers’ beliefs during distance learning. Education Sciences, 11(6), 264.
García-Gorrostieta, J. M., López-López, A., & González-Elopez, S. (2018). Automated assessment of final project reports in computer engineering education. Computer Applications in Engineering Education, 26(5), 1217–1226.
Hasan, M. A., Noor, N. F. M., Rahman, S. S. B. A., & Rahman, M. M. (2020). From intelligent to affective tutoring systems: A review and open issues. IEEE Access, 8, 204612–204638.
Hew, K. F., Lan, M., Tang, Y., Jia, C., & Lo, C. K. (2019). Where is the “theory” within the field of educational technology research? British Journal of Educational Technology, 50(3), 956–971.
Huang, J., & Chen, Z. (2016). Research and design of web-based intelligent tutoring systems. International Journal of Multimedia and Ubiquitous Engineering, 11(6), 337–348.
Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25.
Lodhi, P., Mishra, O., Jain, S., & Bajaj, V. (2018). STUA: An intelligent student assistant. International Journal of Interactive Multimedia and Artificial Intelligence, 5(2), 17–25.
McLaren, B. M., DeLeeuw, K. E., & Mayer, R. E. (2011). Social intelligent tutoring systems and classroom learning. Computers & Education, 56(3), 574–584.
Pise, A. A., Vadapalli, H., & Sanders, I. (2020). Facial emotion recognition for cognitive assessment in e-learning environments. Multimedia Tools and Applications, 12, 1–21.
Rivers, D. J. (2022). Stress as a mediator in socially distanced online education environments. Human Behavior and Emerging Technologies, 2022, Article 9719729.
Sebbani, M., Adarmouch, L., Adil, M., et al. (2021). Implementation of online teaching in medical education: Lessons learned from student perspectives during a health crisis. Education Research International, 2021, Article 5547821.
Kose, U., & Arslan, A. (2016). Intelligent e-learning systems to improve students’ academic achievement in programming courses. International Journal of Engineering Education, 32(1), 185–198.
Leer, R., & Ivanov, S. (2013). Rethinking the future of learning: The possibilities and limitations of technology in education in the 21st century. International Journal of Oral Implantology, 5(4).
Descargas
Publicado
Número
Sección
Licencia
Derechos de autor 2026 Paola Vanessa Santamaría Velásquez, José Luis Pomavilla Patiño, Mauro David Villacrés Benalcázar, Martha Jacqueline González Blanco
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Los autores conservan los derechos morales y patrimoniales de sus obras. Puesto que Tesla Revista Científica es una publicación de acceso abierto, los lectores pueden reproducir total o parcialmente su contenido siempre y cuando proporcionen adecuadamente el crédito a los autores correspondientes y a la revista misma. Tesla Revista Científica se compromete a no hacer uso comercial de los textos que recibe y/o publica.
Nuestra revista se rige por las politicas internacionales SHERPA/RoMEO: Revista verde: Permiten el autoarchivo tanto del pre-print (borrador de un trabajo) como del post-print (la versión corregida y revisada por pares) y hasta de la versión final (maquetada tal como saldrá publicada en la revista).
Véase también "Derechos de autor y licencias".
