Artificial intelligence as a tool for analysing student performance: a review from the perspective of learning analytics
DOI:
https://doi.org/10.63969/nsw2xb85Keywords:
Artificial intelligence, Learning analytics, Student performance, Educational personalisationAbstract
Artificial intelligence (AI) emerges as an innovative tool transforming education by offering new possibilities to analyse and enhance student performance. Its application in learning analytics enables the processing of large volumes of data and the detection of complex patterns that facilitate personalised learning and evidence-based decision-making. This allows for timely educational interventions tailored to the specific needs of each student, promoting more effective and motivating learning experiences. However, despite its benefits, the integration of AI in education faces significant challenges, such as the efficient management of academic information, data quality and availability, teacher training, and ethical concerns related to privacy and data security. A systematic literature review conducted through a qualitative approach and documentary methodology has identified the most commonly used techniques, such as neural networks and decision trees, as well as current applications, benefits, and limitations. Furthermore, the importance of establishing regulatory and pedagogical frameworks that ensure responsible and equitable use of AI is emphasised, guaranteeing that its integration contributes to the continuous improvement of educational quality and the holistic development of students. In summary, AI represents a significant opportunity to revolutionise education, provided that its challenges are adequately addressed and its potential fully harnessed.
References
Bonami, P., Kaklauskas, A., & León, R. (2020). Automatización y eficiencia en la gestión educativa mediante inteligencia artificial. Journal of Educational Technology, 15(3), 45-60.
Chen, Y., Zhang, X., & Liu, H. (2021). Predicting academic performance using machine learning techniques: A systematic review. Journal of Educational Data Mining, 13(1), 1-20.
García-Peñalvo, F. J. (2023). Learning analytics and educational data mining in higher education: A review. Education Sciences, 13(2), 123. https://doi.org/10.3390/educsci13020123
García-Peñalvo, F. J., Conde, M. Á., & Alier, M. (2025). Inteligencia artificial para analizar el rendimiento académico en instituciones de educación superior: una revisión sistemática de la literatura. Revista Cátedra, 6(2), 30-50.
Lee, S., Kim, J., & Park, H. (2024). Impacto de la inteligencia artificial en el rendimiento académico de estudiantes de secundaria. International Journal of Educational Research, 112, 101-115.
Li, J., & Wang, S. (2020). Application of artificial neural networks in educational data mining: A review. Computers & Education, 150, 103-109. https://doi.org/10.1016/j.compedu.2020.103849
Martínez, R., Sánchez, A., & Pérez, L. (2019). Decision trees for educational data mining: Interpretability and accuracy. International Journal of Artificial Intelligence in Education, 29(4), 456-475. https://doi.org/10.1007/s40593-019-00187-6
Pérez, M. (2022). Constructivism and personalized learning: The role of AI in education. Journal of Educational Technology & Society, 25(3), 45-56.
Prendes-Espinosa, M. P. (2021). Retroalimentación personalizada en entornos educativos mediante inteligencia artificial. Revista Iberoamericana de Tecnología Educativa, 28(1), 75-89.
Rodríguez, L. (2021). Real-time feedback systems in education: Enhancing student engagement and learning outcomes. Computers in Human Behavior, 115, 106-121. https://doi.org/10.1016/j.chb.2020.106621
Sikström, S., Andersson, S., & Nilsson, L. (2024). Motivación y uso de tecnologías de inteligencia artificial en educación secundaria. Computers & Education, 180, 104-115.
Torres, F., & Gómez, R. (2023). Ethical considerations in the application of artificial intelligence in education. AI & Society, 38(1), 89-102. https://doi.org/10.1007/s00146-022-01402-7
Vega-Lebrún, R., Bonilla, C., & Sánchez, M. (2021). Innovaciones en la educación universitaria a través de la inteligencia artificial. Revista de Innovación Educativa, 22(4), 112-130.
Zhang, W. (2022). Adaptive learning systems based on machine learning: A review. Educational Technology Research and Development, 70(1), 123-140. https://doi.org/10.1007/s11423-021-10001-8
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Ricardo Zambrana Copaja (Autor/a)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Los artículos publicados en la revista se distribuyen bajo la licencia Creative Commons Atribución 4.0 Internacional (CC BY 4.0). Esta licencia permite a terceros descargar, copiar, distribuir, adaptar y reutilizar una obra, incluso con fines comerciales, siempre que se otorgue el crédito adecuado al autor original.
