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

Research how emotional intelligence training affects students EEG response patterns and improves classroom behavior | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Research how emotional intelligence training affects students EEG response patterns and improves classroom behavior Jiang Hui Li, Rui Zhang, RuiJuan Han This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7873963/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract 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. Physical sciences/Mathematics and computing Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Emotional Intelligence EEG Classroom Behavior Educational Psychology Computational Modeling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Nov, 2025 Reviews received at journal 26 Nov, 2025 Reviews received at journal 15 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers invited by journal 07 Nov, 2025 Editor assigned by journal 04 Nov, 2025 Editor invited by journal 28 Oct, 2025 Submission checks completed at journal 28 Oct, 2025 First submitted to journal 28 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7873963","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":546163185,"identity":"63615d88-a790-4e93-95aa-6f36b9295ad8","order_by":0,"name":"Jiang Hui Li","email":"","orcid":"","institution":"Shandong Xiehe University","correspondingAuthor":false,"prefix":"","firstName":"Jiang","middleName":"Hui","lastName":"Li","suffix":""},{"id":546163186,"identity":"47107d79-89ec-4593-9206-a80fdb25027e","order_by":1,"name":"Rui 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