Volume 4, 2026 – Issue 1
Attention and academic performance: AI-driven prediction and intervention
Elias Souza Ribeiro 
1 * and Carlos Eduardo Batista de Sousa 2
Laboratory of Cognition and Language (LCL), State University of Northern Rio de Janeiro (UENF), Campos dos Goytacazes, Brazil
Corresponding author: elias@ribeiroelias.com.br
DOI: 10.5281/zenodo.18839684
Abstract
Generational changes in cognitive performance and recent results from large-scale educational assessments have renewed interest in attention as a key factor in learning outcomes, particularly in the Brazilian context. This study investigates the role of focused, sustained, alternating, and divided attention in learning and presents an exploratory, performance-based digital assessment tool integrated with an artificial intelligence (AI)–assisted interpretative system to support educational research and decision-making. The system was developed using .NET MAUI and applied to a sample of 144 undergraduate students from Universidade Estadual do Norte Fluminense Darcy Ribeiro (UENF). Teachers provided qualitative ratings of student engagement and academic performance. The AI component, named Samantha, was designed to interpret attentional profiles and generate context-sensitive feedback for educators based on predefined psychometric and neuroscientific principles. Results showed weak positive associations between student engagement and both focused and sustained attention. An inverse association was observed between sustained attention and teacher-rated academic performance. This unexpected pattern should be interpreted cautiously and is treated as exploratory, given the study design and analytical constraints. Overall, the iGnosi® application demonstrated operational stability and practical usability during data collection. While not intended as a validated diagnostic instrument, the system represents a feasible framework for exploratory investigation of attentional patterns in educational settings and provides clear directions for future psychometric validation and controlled empirical testing.
Keywords: artificial intelligence, attention, education, academic performance, cognitive skills, ai intervention, predictive analytics
Published
2026/02/28
—
Section
Research Papers
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Ribeiro, E. S., & de Sousa, C. E. B. (2026). Attention and academic performance: AI-driven prediction and intervention. The International Journal of Technology, Innovation, and Education, 4(1), 165–184. https://doi.org/10.5281/zenodo.18839684
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Ribeiro, Elias Souza, and Carlos Eduardo Batista de Sousa. 2026. “Attention and Academic Performance: AI-Driven Prediction and Intervention.” The International Journal of Technology, Innovation, and Education 4 (1): 165–84. https://doi.org/10.5281/zenodo.18839684.
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Ribeiro, Elias Souza, and Carlos Eduardo Batista de Sousa. “Attention and Academic Performance: AI-Driven Prediction and Intervention.” The International Journal of Technology, Innovation, and Education, vol. 4, no. 1, Feb. 2026, pp. 165–84, https://doi.org/10.5281/zenodo.18839684.
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[1]
E. S. Ribeiro and C. E. B. de Sousa, “Attention and academic performance: AI-driven prediction and intervention,” The International Journal of Technology, Innovation, and Education, vol. 4, no. 1, pp. 165–184, Feb. 2026, doi: 10.5281/zenodo.18839684.
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Ribeiro, E.S. and de Sousa, C.E.B. (2026) “Attention and academic performance: AI-driven prediction and intervention,” The International Journal of Technology, Innovation, and Education, 4(1), pp. 165–184. Available at: https://doi.org/10.5281/zenodo.18839684.
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Ribeiro ES, de Sousa CEB. Attention and academic performance: AI-driven prediction and intervention. The International Journal of Technology, Innovation, and Education [Internet]. 2026 Feb 28;4(1):165–84. Available from: https://ijtie.com/wp-content/uploads/2026/02/N40116.pdf
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Ribeiro, E. S., & de Sousa, C. E. B. (2026). Attention and academic performance: AI-driven prediction and intervention. The International Journal of Technology, Innovation, and Education, 4(1), 165–184. https://doi.org/10.5281/zenodo.18839684
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About the authors
- Elias Souza Ribeiro is a researcher in neuroeducation and cognitive assessment. He holds an M.Sc. in Natural Sciences from Universidade Estadual do Norte Fluminense (UENF), where he investigated attentional processes and learning outcomes using iGnosi®, a cross-platform psychometric instrument he developed and integrated with a custom-trained artificial intelligence model. His research focuses on attention, executive functions, computational psychometrics, and the use of AI-driven tools to support educational evaluation and evidence-based decision-making in learning environments.
elias@ribeiroelias.com.br
↩︎ - Carlos Eduardo Batista de Sousa is an Associate Professor of Philosophy, Laboratory of Cognition and Language, UENF. PhD in Philosophy (Epistemology) from the University of Konstanz, Germany, Master’s and Bachelor’s degrees in Philosophy from UFRJ. Research areas: General Epistemology, Philosophy and Theory of Science, Neurophilosophy and Philosophy of Neuroscience, Philosophy of Physics and Psychophysics. Research focuses on the brain/consciousness relationship, the nature of matter, and the foundations of the Natural Sciences. Additional interests include Theoretical Rationality, Philosophy of Language and Mind, Logic, and Metaphysics.
cdesousa@uenf.br
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