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.
Ribeiro et al. (Sat,) studied this question.