Publicación:
Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learning

Vista sencilla de ítem
dc.contributor.authorBarros Camargo, Claudia de
dc.contributor.authorHernández Fernández, Antonio
dc.date.accessioned2024-10-31T10:25:54Z
dc.date.available2024-10-31T10:25:54Z
dc.date.issued2024-10-21
dc.description.abstractBackground/Purpose. This study investigates the integration of neuropedagogy, neuroimaging, artificial intelligence (AI), and deep learning in educational systems. The research aims to elucidate how these technologies can be synergistically applied to optimize learning processes based on individual neurocognitive profiles, thereby enhancing educational effectiveness. Materials/Methods. A mixed-methods approach was employed, incorporating both quantitative and qualitative analyses. The study involved 297 students and 59 teachers. Quantitative methods included exploratory factor analysis (EFA) to validate the Neuropedagogy, Neuroimaging, Artificial Intelligence, and Deep Learning Scale, and Spearman correlations to examine inter-variable relationships. Qualitative data were collected through focus groups and analyzed using selective coding. Additionally, a comparative case study using portable electroencephalography (EEG) was conducted to observe direct neurological effects of different learning approaches. Results. EFA confirmed the construct validity of the scale (KMO = .89, p < .001). Spearman correlations revealed significant positive relationships between all dimensions (.65-.72, p < .01). Multiple regression analysis indicated that AI was the strongest predictor of deep learning (β = 0.39, p < .001). The neuroimaging case study demonstrated increased frontal and prefrontal lobe activation and enhanced theta-gamma wave synchronization in AI-supported learning tasks, suggesting more integrated information processing. Conclusion. The findings provide empirical evidence for the transformative potential of integrating neuropedagogy, neuroimaging, AI, and deep learning in education. The strong predictive relationship between AI and deep learning, coupled with the neuroimaging results, suggests that this technological convergence can significantly enhance learning processes. However, the study also highlighted the need for careful ethical considerations in its implementation. These results contribute to the growing body of knowledge on technology-enhanced learning and offer a foundation for developing more personalized and effective educational strategies.en
dc.description.versionversión publicada
dc.identifier.citationde Barros Camargo, C., & Fernández, A.H. (2024). Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learning. Educational Process: International Journal, 13(3): 97-115. https://doi.org/10.22521/edupij.2024.133.6
dc.identifier.doihttps://doi.org/10.22521/edupij.2024.133.6
dc.identifier.issn2147-0901; e-ISSN: 2564-8020
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24219
dc.journal.issue3
dc.journal.titleEducational Process: International Journal
dc.journal.volume13
dc.language.isoen
dc.page.final115
dc.page.initial97
dc.publisherUniversitepark Publishing
dc.relation.centerFacultades y escuelas::Facultad de Educación
dc.relation.departmentMétodos de Investigación y Diagnóstico en Educación I
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject53 Ciencias Económicas::5312 Economía sectorial::5312.04 Educación
dc.subject.keywordsneuropedagogyen
dc.subject.keywordsneuroimagingen
dc.subject.keywordsartificial intelligenceen
dc.subject.keywordsdeep learningen
dc.subject.keywordseducational personalizationen
dc.titleNeuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learningen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
person.familyNameBarros Camargo
person.givenNameClaudia de
person.identifier.orcid0000-0002-2286-8674
relation.isAuthorOfPublication8bad378a-ff27-4ec6-93fb-5ea4f6cd09f4
relation.isAuthorOfPublication.latestForDiscovery8bad378a-ff27-4ec6-93fb-5ea4f6cd09f4
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
De_Barros_Hernandez_Neuropedagogy_Neuroimagin.pdf
Tamaño:
1.25 MB
Formato:
Adobe Portable Document Format
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
3.62 KB
Formato:
Item-specific license agreed to upon submission
Descripción:
Descargar
Colecciones
Artículos y papers