Benefits and Limitations for Salvadoran University Teachers and Students on the Use of AI in Teaching-Learning Processes

Authors

DOI:

https://doi.org/10.31637/epsir-2024-368

Keywords:

Artificial Intelligence, education, teaching, processes, learning, benefits, limitation, ethics

Abstract

Introduction: The study examines the benefits and limitations perceived by Salvadoran university teachers and students on the use of artificial intelligence (AI) in teaching-learning processes. Methodology: A mixed methodology was used with interviews to 5 teachers and questionnaires to 673 students from 20 Salvadoran universities. Results: The results indicate that most of them have a basic knowledge of AI tools such as ChatGPT and Copilot. Perceptions are predominantly positive, although there are concerns about ethical-academic integrity and the need for training. Discussion: The need for a balanced approach that maximises the benefits of AI and mitigates its risks is highlighted, suggesting future research to explore improvements in higher education. Conclusions: AI has great potential, but it is critical to address current limitations and promote thoughtful and careful implementation in university education.

Downloads

Download data is not yet available.

Author Biography

Armando Briñis Zambrano, Universidad Evangelica de El Salvador

Doctor in Historical Sciences. Scientific degree awarded by the University of Havana and registered by the Council of Scientific Degrees of the Ministry of Science and Technology of the Republic of Cuba. Researcher and Professor of the Master's Degree in Research at the Universidad Evangélica de El Salvador. Director of Research. Researcher and Professor at the Universidad Luterana Salvadoreña. Director of the Scientific Research Centre of CONARES (CIC-CONARES). Member of the staff of Thesis Directors of the Universidad Internacional Iberoamericana de México. Professor of the Doctorate in Theology at the Don Bosco University. Professor at the Technological University of El Salvador (UTEC). Founding member of the Multidisciplinary Chair of Africa and the Middle East at the University of Havana.

References

Arroyo, M. y Merlino, I. (2008). Metodología de la investigación social. https://bit.ly/3XTfb2t

Creswell, J. (2013). Introducción y Enfoque del Estudio. https://bit.ly/4cly2Yx

Fraga-Varela, F. y Rodríguez-Groba, A. (2020). Realidad virtual y aumentada e inteligencia artificial aplicadas a la formación universitaria. Education in the Knowledge Society, 21, 15. https://doi.org/10.14201/eks.23537

Göschlberger, B. (2021). Acceptance Models for Intelligent Tutoring Systems. In D. Ifenthaler, y J. Y. K. Yau (Eds.), Utilizing Learning Analytics to Support Study Success (pp. 153-169). https://bit.ly/3W9ROR4

Hernández-Sampieri, R. y Mendoza Torres, C. P. (2018). Metodología de la Investigación. La rutas cualitativa, cuantitativa y mixta. Mc Graw Hill Education

Hernández Sampieri et al. (2014). Metodología de la investigación (6a ed.). McGraw-Hill.

Lavicza, Z., Baranyi, P., Hohenwarter, M., Jones, K. y Kortenkamp, U. (2022). Teachers’ perceptions and acceptance of automated technology in education. British Journal of Educational Technology, 53(1), 149-163. https://doi.org/10.1111/bjet.13162

Liu, D., Geertshuis, S. y Grainger, R. (2022). Understanding academics' adoption of learning technologies: A systematic review. Computers & Education, 172, 104259. https://doi.org/10.1016/j.compedu.2021.104259

Luckin, R., Holmes, W., Griffiths, M. y Forcier, L. B. (2016). Intelligence Unleashed: An argument for AI in Education. Pearson. https://bit.ly/3KbhGW4

Martínez, F., Hinojo, M. A., y Aznar, I. (2020). Percepción de los docentes sobre el impacto de la Inteligencia Artificial en educación superior. Revista Espacios, 41(3), 11-20. https://acortar.link/SC1sdS

MINED (2023). Estadísticas educativas. https://bit.ly/4crtP5N

Moreno-Guerrero, A. J., Lopez-Belmonte, J., Marín-Marín, J. A., y Soler-Costa, R. (2020). E-Learning in Higher Educational Institutions: A Bibliometric Analysis. Social Sciences, 9(12), 231. https://doi.org/10.3390/socsci9120231

Nwana, H. S. (1990). Intelligent tutoring systems: an overview. Artificial Intelligence Review, 4, 251–277. https://doi.org/10.1007/BF00155578

Papamitsiou y Economides. (2014). Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Educational Technology & Society 17(4), 49-64.

Portillo Peñuelas, A. B. (2020). La inteligencia artificial en la educación superior, la nueva materia pendiente. RIED. Revista Iberoamericana de Educación a Distancia, 24(1), 331-350. https://doi.org/10.5944/ried.24.1.26455

Rivas M., Suárez-Alemán, A. y Serebrisky, T. (2010) Hechos estilizados de transporte urbano en América Latina y el Caribe. IDB. http://dx.doi.org/10.18235/0001606

Roll, I. y Wylie, R. (2016). Evolution and Revolution in Artificial Intelligence in Education. International Journal of Artificial Intelligence in Education, 26(2), 582-599. https://doi.org/10.1007/s40593-016-0110-3

Sáez López, J. M. (2020). Posibilidades educativas de la Inteligencia Artificial en la educación superior. Revista Iberoamericana de Educación a Distancia, 24(1), 195-211. https://doi.org/10.5944/ried.24.1.26566

Taylor, S. J. y Bogdan, R. (1986). Introducción a los métodos cualitativos: La búsqueda de significados. Paidós

Ugalde Binda, N. y Balbastre-Benavent, F. (2013). Investigación cuantitativa e investigación cualitativa: Buscando las ventajas de las diferentes metodologías de investigación. Revista de Ciencias Económicas, 31(2), 179-187. Universidad de Costa Rica. https://acortar.link/HQazk0

Volungevičienė, A., Brown, M., Greenspon, R., Gaebel, M. y Morrisroe, A. (2021). Developing a high-performance digital education ecosystem: Institutional self-assessment instruments. European University Association. https://shre.ink/DLrP

Zawacki-Richter, O., Marín, V. I., Bond, M. y Gouverneur, F. (2019). Education of the future? Blended learning with artificial intelligence. Digital Transformations, University Publication. https://shre.ink/DLrz

Downloads

Published

2024-07-23

How to Cite

Briñis Zambrano, A. (2024). Benefits and Limitations for Salvadoran University Teachers and Students on the Use of AI in Teaching-Learning Processes. European Public & Social Innovation Review, 9, 1–19. https://doi.org/10.31637/epsir-2024-368

Issue

Vol. 9 (2024)

Section

INNOVATING IN THE GALAXY OF ARTIFICIAL INTELLIGENCE

License

Copyright (c) 2024 Armando Briñis Zambrano, David Alberto Quintana Pérez

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish with this journal agree to the following terms:
  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Non Commercial, No Derivatives Attribution 4.0. International (CC BY-NC-ND 4.0.), that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).