The Future of Education
AI and School Dropout: Prediction and Consequences for Frequency and Students Performance
Leogildo Freires, Federal University of Alagoas (Brazil)
Heitor Araújo, Federal University of Bahia (Brazil)
Júlio Costa, Federal University of Minas Gerais (Brazil)
Luan Filipy Freire Torres, Universidade Federal de Alagoas (Brazil)
Gabriel Macedo, Federal University of Alagoas (Brazil)
Ane Mayra Melo Silva, Federal University of Alagoas Center for Excellence in Social Technologies (NEES) (Brazil)
Abstract
The study investigates the use of artificial intelligence tools in the analysis of large databases made available in partnership with education departments of Brazilian states in order to understand how student scores in the five dimensions of the Relational Factors for the Risk of School Dropout Scale [1] would be able to predict student attendance and engagement in the classroom, In addition, analyses were carried out crossing the students' social markers in an intersectional way, in order to understand, for example, if race-ethnicity is also determinant for the purposes of the research [2]. The data were analyzed from RStudio following rigorous and methodologically innovative protocols in the face of the technological innovation that artificial intelligence already presents in the field of Psychology and Education. The results reveal a diversity of determining factors based on where schools are located in the city, namely: whether they are located in rural, urban or peripheral areas, to predict the attendance of students in school environments and how this reverberates even in psychological factors of well-being [3]. Finally, we propose the incorporation of computerized systems based on scientific evidence for the design of public policies [4] in the field of education that are intersectionally sensitive and politically committed to the continuing education of civil servants, the qualification of infrastructure and the promotion of cutting-edge school environments at the municipal, state and federal levels.
Keywords: school dropout, large scale analysis, school dropout prevention
REFERENCES
[1] Vasconcelos AN, Freires LA, Loureto GDL, Fortes G, Costa JCA, Torres LFF, Bittencourt II, Cordeiro TD and Isotani S (2023) Advancing school dropout early warning systems: the IAFREE relational model for identifying at-risk students. Front. Psychol. https://doi.org/10.3389/fpsyg.2023.1189283
[2] Fortes, G., Nunes , A., Albuquerque , J. C., Lopes , G. D., Freires , L. F., Soares , L., Alves , L., Cordeiro, T., Santana, I. B., & Isotani, S. (2024). Exploring the Unexpected: the Relationship Between Higher Family Income and Dropout Risk. Praxis & Saber, 15(41), 1-16. https://doi.org/10.19053/uptc.22160159.v15.n41.2024.16848
[3] Madalena, A. P., Loureto, G. D. L., Santos, J. A. G., Santos, L. C. de O., Fortes, G., & Freires, L. A. (2024). Psychological Well-Being Among Adolescents: The Role of Parenting Styles, Causal Attributions of Academic Success/Failure, and Perceived School Performance. Journal of Psychoeducational Assessment, 42(5), 498-511. https://doi.org/10.1177/07342829241245462
[4] Rosa, J. G. L. da, Lima, L. L., & Aguiar, R. B. de. (2025). Desenho de Políticas Públicas: Guia prático. Jacarta.
