Research how emotional intelligence training affects students EEG response patterns and improves classroom behavior

This study investigates the impact of emotional intelligence (EI) training on students’ EEG response patterns and its role in improving classroom behavior, aligning with the thematic scope of enhancing learning and development through educational psychology. Emotional intelligence is increasingly recognized as a pivotal factor in educational settings, yet traditional pedagogical approaches often lack methodologies for integrating EI development into classroom instruction. Existing studies predominantly rely on static assessments or behavioral observations, which do not adequately capture the dynamic interplay between emotional states and cognitive performance. To address these limitations, we propose the Cohort-Driven Learning Optimization Model (CDLOM), an advanced framework integrating EEG-based emotional and cognitive state monitoring with personalized EI interventions. CDLOM leverages recurrent architectures and interaction networks to model and optimize student learning states in real-time. Experimental evaluations demonstrate that EI training not only modulates EEG response patterns associated with emotional regulation but also significantly enhances classroom behavior metrics, including engagement and collaboration. These findings highlight the transformative potential of integrating emotional intelligence frameworks with computational modeling for fostering inclusive and effective educational environments.