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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| Format: | Article |
| Language: | English |
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ÜNİVERSİTEPARK Limited
2024-10-01
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| Series: | Educational Process: International Journal |
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| Online Access: | https://www.edupij.com/files/1/articles/article_352/EDUPIJ_352_article_6720c1e39c44e.pdf |
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| _version_ | 1832543343485124608 |
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| author | Claudia De Barros Camargo , Antonio Hernández Fernández |
| author_facet | Claudia De Barros Camargo , Antonio Hernández Fernández |
| author_sort | Claudia De Barros Camargo , Antonio Hernández Fernández |
| collection | DOAJ |
| 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.
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| format | Article |
| id | doaj-art-0ed8c0d5ee2c4ecb8f9e139ca56452b1 |
| institution | Kabale University |
| issn | 2147-0901 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | ÜNİVERSİTEPARK Limited |
| record_format | Article |
| series | Educational Process: International Journal |
| spelling | doaj-art-0ed8c0d5ee2c4ecb8f9e139ca56452b12025-02-03T11:47:17ZengÜNİVERSİTEPARK LimitedEducational Process: International Journal2147-09012024-10-0113310.22521/edupij.2024.133.6Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep LearningClaudia De Barros Camargo , Antonio Hernández Fernándezhttps://orcid.org/0000-0002-2286-8674Background/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. https://www.edupij.com/files/1/articles/article_352/EDUPIJ_352_article_6720c1e39c44e.pdfneuropedagogyneuroimagingartificial intelligencedeep learning |
| spellingShingle | Claudia De Barros Camargo , Antonio Hernández Fernández Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learning Educational Process: International Journal neuropedagogy neuroimaging artificial intelligence deep learning |
| title | 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_short | Neuropedagogy and Neuroimaging of Artificial Intelligence and Deep Learning |
| title_sort | neuropedagogy and neuroimaging of artificial intelligence and deep learning |
| topic | neuropedagogy neuroimaging artificial intelligence deep learning |
| url | https://www.edupij.com/files/1/articles/article_352/EDUPIJ_352_article_6720c1e39c44e.pdf |
| work_keys_str_mv | AT claudiadebarroscamargoantoniohernandezfernandez neuropedagogyandneuroimagingofartificialintelligenceanddeeplearning |