Analysis of the repercussions of Artificial Intelligence in the Personalization of the Virtual Educational Process in Higher Education Programs
DOI:
https://doi.org/10.56294/dm2024386Keywords:
Artificial Intelligence, Learning Personalization, Virtual Education, Learning Analytics, Tutoring Systems, Recommendation SystemsAbstract
This study examined how artificial intelligence (AI) has transformed the personalization of the virtual educational process in higher education programs. A systematic review of literature published between 2012 and 2023 was carried out, evaluating empirical studies, reports and review articles available in academic databases such as IEEE Xplore, SpringerLink and Google Scholar. Methods discussed include intelligent tutoring systems, learning analytics, and recommendation systems. The results showed that AI significantly improved the personalization of learning. Intelligent tutoring systems provide real-time adaptive feedback, adjusting content and pacing based on students' individual needs, improving their understanding and retention. Learning analytics helps identify student behavior patterns and predict academic issues, thereby facilitating timely interventions that help improve performance. Additionally, recommender systems personalize study materials based on student preferences and progress, thereby optimizing the educational experience. However, significant challenges have been identified, such as the need to protect data privacy and mitigate algorithmic biases that can affect the fairness and efficiency of these systems. In conclusion, the integration of AI into virtual higher education has enhanced the personalization of learning, improving both student satisfaction and academic performance. However, there is a need to continue to focus on developing ethical and equitable AI systems to address identified issues and maximize educational benefits
References
1. Baker, R. S. (2019). Challenges for the Future of Educational Data Mining: The Baker Learning Analytics Prizes. Journal of Educational Data Mining, 11(1), 1-9. https://jedm.educationaldatamining.org/index.php/JEDM/article/view/333/303
2. Baker, R. S., P. S. (2014). Educational Data Mining and Learning Analytics. In C. Romero, S. Ventura, M. Pechenizkiy, & R. S. J. D. Baker (Eds.), Handbook of educational data mining (pp. 61-74). Springer. https://doi.org/10.1007/978-1-4614-3305-7_4
3. Carillo Rodríguez, L. M., & Montenegro Cobeña, A. M. (2024). Sistemas de recomendación basados en la inteligencia artificial para evaluación educativa en la EEB Mercedes Moreno Irigoyen y la EEB Presidente Tamayo (Bachelor's thesis, La Libertad: Universidad Estatal Península de Santa Elena. 2024).
4. Cedeño, E. I. B., Quintero, A. R. T., Quiñónez, O. G. A., Zamora, M. E. P., & Prado, N. G. V. (2024). Análisis de tendencias y futuro de la Inteligencia Artificial en la Educación Superior: perspectivas y desafíos. Ciencia Latina Revista Científica Multidisciplinar, 8(1), 3061-3076.
5. Chen, L., De, R. K., & Kantor, P. B. (2013). Recommender systems handbook. Springer. https://link.springer.com/book/10.1007/978-1-4899-7637-6
6. Chen, Y., & Wu, Z. (2021). Artificial Intelligence in Education: A Review. IEEE Access, 9, 33992-34002. https://doi.org/10.1109/ACCESS.2021.3067977
7. Dziuban, C. D., Graham, C. R., Moskal, P. D., Norberg, A., & Sicilia, N. (2018). Blended learning: The new normal and emerging technologies. International Journal of Educational Technology in Higher Education, 15(1), Article 3. https://doi.org/10.1186/s41239-018-0106-3
8. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press. https://us.macmillan.com/books/9781250074317
9. Fischer, G., & Draxler, S. (2019). Personalization and Learning: Designing Systems for the Preferences of Students. Educational Technology Research and Development, 67(6), 1611-1631. https://link.springer.com/article/10.1007/s11423-018-9634-2
10. Gómez, W. O. A. (2023). La inteligencia artificial y su incidencia en la educación: Transformando el aprendizaje para el siglo XXI. Revista internacional de pedagogía e innovación educativa, 3(2), 217-229.
11. Graesser, A. C., Conley, M., & Olney, A. (2012). Intelligent Tutoring Systems. American Psychologist, 67(6), 455-467. https://psycnet.apa.org/doi/10.1037/a0029462
12. Luckin, R., & Cukurova, M. (2019). Designing Educational Technologies in the Age of AI: A Learning Sciences Perspective. British Journal of Educational Technology, 50(2), 631-641. https://doi.org/10.1111/bjet.12861
13. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (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/Intelligence-Unleashed-Publication.pdf
14. Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press. https://nyupress.org/9781479837243/algorithms-of-oppression/
15. Peláez, C., Solano, A., López, J., de la Rosa, E. A., Ospina, J. A., & Parra Valencia, J. A. (2024). Experiencias interactivas multimedia bajo un enfoque del diseño sensible al valor utilizando analítica de aprendizaje para educación básica.
16. Pistilli, M. D., Arnold, K. E., & Bethune, M. (2012). Signals: Using academic analytics to promote student success. EDUCAUSE Review, 47(5), 30-39. https://er.educause.edu/articles/2012/10/signals-using-academic-analytics-to-promote-student-success
17. Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach. Pearson Education. https://www.pearson.com/store/p/artificial-intelligence-a-modern-approach/P100000752682
18. Siemens, G. (2013). Learning Analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213498851
19. Vera, F. (2023). Integración de la Inteligencia Artificial en la Educación superior: Desafíos y oportunidades. Transformar, 4(1), 17-34.
20. Williamson, B. (2017). Big data in education: The digital future of learning, policy, and practice. Bristol University Press. https://policy.bristoluniversitypress.co.uk/big-data-in-education
Published
Issue
Section
License
Copyright (c) 2024 Elizabeth Magdalena Recalde Drouet, David Mauricio Tello Salazar, Tatiana Lizbeth Charro Domínguez, Pablo Jordán Catota Pinthsa (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.