Conference Agenda

In the current landscape of educational digital transformation, integrating new technological tools to enhance student learning poses significant challenges. Notably, following the COVID-19 pandemic, potential cognitive and attentional delays have been observed in children due to disruptions in traditional learning processes. Therefore, developing mechanisms to accurately measure the impact of these educational tools is crucial since existing evaluations mainly rely on subjective behavioral observations. A notable gap exists in the literature regarding their effectiveness and neurological validation. As the validation phase of a broader research project, this study aims to develop a tool for objectively assessing cognitive states. The methodology involves conducting validated tests with children, recording Electroencephalogram (EEG) signals, and applying predictive machine learning models such as Random Forest to classify cognitive states during interactions with the NAO robot, a Socially Assistive Robotics (SAR) platform. Our preliminary results demonstrate the feasibility of classifying students' attention states—engaged versus distracted—with high accuracy (79%). This approach provides a robust quantitative metric that complements behavioral observations, offering educators and researchers a data-driven tool to evaluate and personalize teaching strategies, thereby helping to close learning gaps. Ultimately, this study advances the integration of educational neuroscience, artificial intelligence, and SAR in educational settings.