Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learning

Background/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...

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Autores: Barros Camargo, Claudia de, Hernández Fernández, Antonio
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Repositorio:e-spacio (DSpace). Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:dnet:e-spacio(ds_::9c0927c45f5d69d5e52361eb6f619ed8
Acceso en línea:https://hdl.handle.net/20.500.14468/24219
Access Level:acceso abierto
Palabra clave:53 Ciencias Económicas::5312 Economía sectorial::5312.04 Educación
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spelling Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep LearningBarros Camargo, Claudia deHernández Fernández, Antonio53 Ciencias Económicas::5312 Economía sectorial::5312.04 EducaciónBackground/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.Universitepark Publishinge-Spacio UNED20242024-10-3120242024-10-2120242024-10-21journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/24219reponame:e-spacio (DSpace). Repositorio Institucional de la UNEDinstname:Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/deed.esoai:dnet:e-spacio(ds_::9c0927c45f5d69d5e52361eb6f619ed82025-06-20T09:27:21Z
dc.title.none.fl_str_mv Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learning
title Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learning
spellingShingle Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learning
Barros Camargo, Claudia de
53 Ciencias Económicas::5312 Economía sectorial::5312.04 Educación
title_short Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learning
title_full Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learning
title_fullStr Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learning
title_full_unstemmed Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learning
title_sort Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learning
dc.creator.none.fl_str_mv Barros Camargo, Claudia de
Hernández Fernández, Antonio
author Barros Camargo, Claudia de
author_facet Barros Camargo, Claudia de
Hernández Fernández, Antonio
author_role author
author2 Hernández Fernández, Antonio
author2_role author
dc.contributor.none.fl_str_mv e-Spacio UNED
dc.subject.none.fl_str_mv 53 Ciencias Económicas::5312 Economía sectorial::5312.04 Educación
topic 53 Ciencias Económicas::5312 Economía sectorial::5312.04 Educación
description Background/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.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-10-31
2024
2024-10-21
2024
2024-10-21
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/24219
url https://hdl.handle.net/20.500.14468/24219
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
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info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/deed.es
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by/4.0/deed.es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitepark Publishing
publisher.none.fl_str_mv Universitepark Publishing
dc.source.none.fl_str_mv reponame:e-spacio (DSpace). Repositorio Institucional de la UNED
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