Sistemas inteligentes y su aplicación en la evaluación del desempeño académico universitario: una revisión de la literatura en el contexto sudamericano

Magdalena Del-Águila-Castro

doi:10.51252/rcsi.v4i2.671

Authors

Magdalena Del-Águila-Castro Universidad César Vallejo

DOI:

https://doi.org/10.51252/rcsi.v4i2.671

Keywords:

artificial intelligence, educational equity, feedback, machine learning

Abstract

The study aimed to analyze the impact of intelligent systems on improving academic performance and personalized learning, through a review of 29 articles published between 2016 and 2024. It focused on the use of artificial intelligence, machine learning, data mining, and intelligent tutoring systems in education. The results showed that these technologies optimize educational assessment and improve academic performance. Predictive models help identify students at risk of dropping out, enabling early interventions. Adaptive architectures proved effective across various disciplines, and intelligent tutoring systems enhanced interaction and feedback. Despite these advances, challenges remain in accessibility in resource-limited environments and ethical concerns related to data privacy and algorithmic bias. The study highlights the need for an inclusive and ethical approach to ensure these technologies transform education and benefit all students.

References

Abulibdeh, A., Zaidan, E., & Abulibdeh, R. (2024). Navigating the confluence of artificial intelligence and education for sustainable development in the era of industry 4.0: Challenges, opportunities, and ethical dimensions. Journal of Cleaner Production, 437, 140527. https://doi.org/10.1016/j.jclepro.2023.140527

Akinwalere, S. N., & Ivanov, V. (2022). Artificial Intelligence in Higher Education: Challenges and Opportunities. Border Crossing, 12(1), 1–15. https://doi.org/10.33182/bc.v12i1.2015

Alfaro, L., Rivera, C., & Luna-Urquizo, J. (2019). Using Project-based learning in a Hybrid e-Learning system model. International Journal of Advanced Computer Science and Applications, 10(10), 426–436. https://doi.org/10.14569/ijacsa.2019.0101059

Ali, O., Murray, P. A., Momin, M., Dwivedi, Y. K., & Malik, T. (2024). The effects of artificial intelligence applications in educational settings: Challenges and strategies. Technological Forecasting and Social Change, 199, 123076. https://doi.org/10.1016/j.techfore.2023.123076

Álvarez, D., Melillán, A., Cravero, A., & Sepúlveda, S. (2023). Proposed Model for the Alignment between Curriculum Design and IT | Propuesta de Modelo para el Alineamiento entre el Diseño Curricular y las TI. RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao, 2023(E59), 531–543.

Balasubramaniam, N., Kauppinen, M., Rannisto, A., Hiekkanen, K., & Kujala, S. (2023). Transparency and explainability of AI systems: From ethical guidelines to requirements. Information and Software Technology, 159, 107197. https://doi.org/10.1016/j.infsof.2023.107197

Carlotto, T., & Jaques, P. A. (2016). The effects of animated pedagogical agents in an English-as-a-foreign-language learning environment. International Journal of Human Computer Studies, 95, 15–26. https://doi.org/10.1016/j.ijhcs.2016.06.001

Cronin, P., Ryan, F., & Coughlan, M. (2008). Undertaking a literature review: a step-by-step approach. British Journal of Nursing, 17(1), 38–43. https://doi.org/10.12968/bjon.2008.17.1.28059

de Brito Lima, F., Lautert, S. L., & Gomes, A. S. (2022). Learner behaviors associated with uses of resources and learning pathways in blended learning scenarios. Computers and Education, 191. https://doi.org/10.1016/j.compedu.2022.104625

Delerna Rios, G. E., & Levano Rodriguez, D. (2021). Importancia de las tecnologías de información en el fortalecimiento de competencias pedagógicas en tiempos de pandemia. Revista Científica de Sistemas e Informática, 1(1), 69–78. https://doi.org/10.51252/RCSI.V1I1.104

Díaz, B., & Nussbaum, M. (2024). Artificial intelligence for teaching and learning in schools: The need for pedagogical intelligence. Computers and Education, 217. https://doi.org/10.1016/j.compedu.2024.105071

Durães, D., Toala, R., Gonçalves, F., & Novais, P. (2019). Intelligent tutoring system to improve learning outcomes. AI Communications, 32(3), 161–174. https://doi.org/10.3233/AIC-190624

Flores, V., Heras, S., & Julián, V. (2022). A New Methodological Framework for Project Design to Analyse and Prevent Students from Dropping Out of Higher Education. Electronics (Switzerland), 11(18). https://doi.org/10.3390/electronics11182902

Freitas, F. A. D. S., Vasconcelos, F. F. X., Peixoto, S. A., Hassan, M. M., Ali Akber Dewan, M., de Albuquerque, V. H. C., & Rebouças Filho, P. P. (2020). IoT system for school dropout prediction using machine learning techniques based on socioeconomic data. Electronics (Switzerland), 9(10), 1–14. https://doi.org/10.3390/electronics9101613

García-Martínez, I., Fernández-Batanero, J. M., Fernández-Cerero, J., & León, S. P. (2023). Analysing the Impact of Artificial Intelligence and Computational Sciences on Student Performance: Systematic Review and Meta-analysis. Journal of New Approaches in Educational Research, 12(1), 171–197. https://doi.org/10.7821/naer.2023.1.1240

Garcia, C., & Lemos, N. (2023). The Gamification of E-learning Environments for Learning Programming. International Journal on Informatics Visualization, 7(2), 455–462. https://doi.org/10.30630/joiv.7.2.1602

Gligorea, I., Cioca, M., Oancea, R., Gorski, A.-T., Gorski, H., & Tudorache, P. (2023). Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review. Education Sciences, 13(12), 1216. https://doi.org/10.3390/educsci13121216

Gomede, E., de Barros, R. M., & de Souza Mendes, L. (2020). Use of deep multi-target prediction to identify learning styles. Applied Sciences (Switzerland), 10(5). https://doi.org/10.3390/app10051756

Gomez, A., Chamba Eras, L. A., & Aguilar, J. (2021). Multi-agent systems for the management of resources and activities in a smart classroom. IEEE Latin America Transactions, 19(9), 1511–1519. https://doi.org/10.1109/TLA.2021.9468444

Guanin-Fajardo, J. H., Guaña-Moya, J., & Casillas, J. (2024). Predicting Academic Success of College Students Using Machine Learning Techniques. Data, 9(4). https://doi.org/10.3390/data9040060

Huaman Llanos, A. A., Huatangari, L. Q., Yalta Meza, J. R., & Monteza, A. H. (2023). Leveraging Text Mining for Analyzing Students’ Preferences in Computer Science and Language Courses. Ingenierie Des Systemes d’Information, 28(5), 1265–1273. https://doi.org/10.18280/isi.280515

Jaramillo-Morillo, D., Ruipérez-Valiente, J. A., Burbano Astaiza, C. P., Solarte, M., Ramirez-Gonzalez, G., & Alexandron, G. (2022). Evaluating a learning analytics dashboard to detect dishonest behaviours: A case study in small private online courses with academic recognition. Journal of Computer Assisted Learning, 38(6), 1574–1588. https://doi.org/10.1111/jcal.12734

Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution. Sustainability, 15(16), 12451. https://doi.org/10.3390/su151612451

Lim, T., Gottipati, S., & Cheong, M. L. F. (2023). Ethical Considerations for Artificial Intelligence in Educational Assessments. In Creative AI Tools and Ethical Implications in Teaching and Learning (pp. 32–79). https://doi.org/10.4018/979-8-3693-0205-7.ch003

Luna-Urquizo, J. (2019). Learning management system personalization based on multi-attribute decision making techniques and intuitionistic fuzzy numbers. International Journal of Advanced Computer Science and Applications, 10(11), 669–676. https://doi.org/10.14569/IJACSA.2019.0101188

Malik, A. R., Pratiwi, Y., Andajani, K., Numertayasa, I. W., Suharti, S., Darwis, A., & Marzuki. (2023). Exploring Artificial Intelligence in Academic Essay: Higher Education Student’s Perspective. International Journal of Educational Research Open, 5, 100296. https://doi.org/10.1016/j.ijedro.2023.100296

Martinez-Carrascal, J. A., Munoz-Gama, J., & Sancho-Vinuesa, T. (2024). Evaluation of Recommended Learning Paths Using Process Mining and Log Skeletons: Conceptualization and Insight into an Online Mathematics Course. IEEE Transactions on Learning Technologies, 17, 555–568. https://doi.org/10.1109/TLT.2023.3298035

Melillán, A., & Cravero, A. (2022). Software engineering in the development of technologies to support curriculum design: A systematic mapping | Ingeniería de Software en el desarrollo de tecnologías para el apoyo al diseño curricular: Un mapeo sistemático. RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao, 2022(E50), 322–334.

Mellado, R., Cubillos, C., Vicari, R. M., & Gasca-Hurtado, G. (2024). Leveraging Gamification in ICT Education: Examining Gender Differences and Learning Outcomes in Programming Courses. Applied Sciences (Switzerland), 14(17). https://doi.org/10.3390/app14177933

Mendoza, W., Ramírez, G. M., González, C., & Moreira, F. (2022). Assessment of Curriculum Design by Learning Outcomes (LO). Education Sciences, 12(8). https://doi.org/10.3390/educsci12080541

Mhlongo, S., Mbatha, K., Ramatsetse, B., & Dlamini, R. (2023). Challenges, opportunities, and prospects of adopting and using smart digital technologies in learning environments: An iterative review. Heliyon, 9(6), e16348. https://doi.org/10.1016/j.heliyon.2023.e16348

Nieto, Y., García-Díaz, V., Montenegro, C., & Crespo, R. G. (2019). Supporting academic decision making at higher educational institutions using machine learning-based algorithms. Soft Computing, 23(12), 4145–4153. https://doi.org/10.1007/s00500-018-3064-6

Pincay-Ponce, J., Herrera-Tapia, J., Terranova-Ruiz, J., Cruz-Felipe, M., Sendón-Varela, J., & Fernández-Capestany, L. (2022). Educational data mining: Incidence of socioeconomic factors on school achievement | Minería de datos educativos: Incidencia de factores socioeconómicos en el aprovechamiento escolar. RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao, 2022(E49), 654–667.

Rodríguez, M. F., Nussbaum, M., Yunis, L., Reyes, T., Alvares, D., Joublan, J., & Navarrete, P. (2022). Using scaffolded feedforward and peer feedback to improve problem-based learning in large classes. Computers and Education, 182. https://doi.org/10.1016/j.compedu.2022.104446

Salazar, C., Montoya-Múnera, E., & Aguilar, J. (2023). Sentiment analysis in learning resources. Journal of Computers in Education, 10(4), 637–662. https://doi.org/10.1007/s40692-022-00237-9

Souza, A. S. C. de, & Debs, L. (2024). Concepts, innovative technologies, learning approaches and trend topics in education 4.0: A scoping literature review. Social Sciences & Humanities Open, 9, 100902. https://doi.org/10.1016/j.ssaho.2024.100902

Theophilou, E., Sanchez-Reina, R., Hernandez-Leo, D., Odakura, V., Amarasinghe, I., & Lobo-Quintero, R. (2024). The effect of a group awareness tool in synchronous online discussions: studying participation, quality and balance. Behaviour and Information Technology, 43(6), 1149–1163. https://doi.org/10.1080/0144929X.2023.2200543

Vázquez-Cano, E., Mengual-Andrés, S., & López-Meneses, E. (2021). Chatbot to improve learning punctuation in Spanish and to enhance open and flexible learning environments. International Journal of Educational Technology in Higher Education, 18(1). https://doi.org/10.1186/s41239-021-00269-8

Villegas-Ch, W., Palacios-Pacheco, X., & Luján-Mora, S. (2020). A business intelligence framework for analyzing educational data. Sustainability (Switzerland), 12(14), 1–21. https://doi.org/10.3390/su12145745

Villegas-Ch, W., Roman-Cañizares, M., Sánchez-Viteri, S., García-Ortiz, J., & Gaibor-Naranjo, W. (2021). Analysis of the state of learning in university students with the use of a hadoop framework. Future Internet, 13(6). https://doi.org/10.3390/fi13060140

Vives, L., Cabezas, I., Vives, J. C., Reyes, N. G., Aquino, J., Condor, J. B., & Altamirano, S. F. S. (2024). Prediction of Students’ Academic Performance in the Programming Fundamentals Course Using Long Short-Term Memory Neural Networks. IEEE Access, 12, 5882–5898. https://doi.org/10.1109/ACCESS.2024.3350169

Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252, 124167. https://doi.org/10.1016/j.eswa.2024.124167

Zapata-Medina, D., Espinosa-Bedoya, A., & Jiménez-Builes, J. A. (2024). Improving the Automatic Detection of Dropout Risk in Middle and High School Students: A Comparative Study of Feature Selection Techniques. Mathematics, 12(12). https://doi.org/10.3390/math12121776

Zhai, X., Chu, X., Chai, C. S., Jong, M. S. Y., Istenic, A., Spector, M., Liu, J.-B., Yuan, J., & Li, Y. (2021). A Review of Artificial Intelligence (AI) in Education from 2010 to 2020. Complexity, 2021(1). https://doi.org/10.1155/2021/8812542