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                            [identifier] => oai:ojs2.ijsmc.pro-metrics.org:article/353
                            [datestamp] => 2026-04-02T15:13:04Z
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                                    [title] => Array
                                        (
                                            [0] => Artificial intelligence in education: How has research evolved over time? A longitudinal bibliometric analysis
                                            [1] => La inteligencia artificial en la educación: ¿Cómo ha evolucionado la investigación a lo largo del tiempo? Un análisis bibliométrico longitudinal
                                        )

                                    [creator] => Array
                                        (
                                            [0] => Caro-Soto, Félix
                                            [1] => Vellon-Flores, Viviana
                                            [2] => Gavedia-Garcia, Gladys
                                            [3] => Bravo-Montoya, Julia
                                            [4] => Huaman-Fritas, Juan
                                        )

                                    [subject] => Array
                                        (
                                            [0] => artificial intelligence in education
                                            [1] => generative artificial intelligence
                                            [2] => large-scale language models
                                            [3] => bibliometric mapping
                                            [4] => co-word analysis
                                            [5] => higher education
                                            [6] => academic integrity
                                            [7] => artificial intelligence literacy
                                            [8] => inteligencia artificial en educación
                                            [9] => inteligencia artificial generativa
                                            [10] => modelos de lenguaje de gran escala
                                            [11] => mapeo bibliométrico
                                            [12] => análisis de copalabras
                                            [13] => educación superior
                                            [14] => integridad académica
                                            [15] => alfabetización en inteligencia artificial
                                        )

                                    [description] => Array
                                        (
                                            [0] => Objective. A longitudinal approach based on author keywords was used to analyze the thematic evolution of research on artificial intelligence (AI) in education. This approach enabled the identification of changes in the field’s vocabulary, established and emerging themes, and shifts in their relationships, as well as their behavior across five analytical dimensions (AI technologies/techniques, algorithms/models, platforms/systems, educational level, and methodological approach).
Design/Methodology/Approach. A comprehensive search was conducted on Scopus (1973–2025) using an advanced title search algorithm that combined terms from the field of AI with educational terms. The analysis was based exclusively on the authors’ keywords. A global co-occurrence network and a temporal visualization (overlay) were constructed in VOSviewer. The minimum threshold for terms within each cluster was set at 15, resulting in a final map comprising 1,197 keywords, which were subsequently organized into 20 clusters.
Results/Discussion. The field of AI in education exhibited a robust technical foundation, with a focus on machine learning, deep learning, and neural networks. This foundation was complemented by a more nascent trend that was predominantly characterized by generative AI. During the 2024–2025 period, there was an escalation in the use of vocabulary related to ChatGPT, language models, and applied practices. This escalation was accompanied by a notable increase in terms related to ethics, integrity, and governance. The cluster-based structure reflected a broad thematic organization, with connections across technological, pedagogical, and institutional fronts.
Conclusions. The field of AI research in education has undergone a significant shift in focus, transitioning from a more “traditional” approach, which was dominated by techniques such as machine learning and deep learning, toward a new phase marked by the emergence of generative AI and language models, such as ChatGPT. This transition has been most evident in the domain of higher education. Concurrently, subjects such as AI literacy, academic integrity, bias, and governance have emerged as the most pressing issues in the current discourse.
Originality/Value. The study provided a longitudinal perspective on AI research in education and a thematic analysis by dimension, which facilitated the identification of topics that have persisted, topics that have emerged, and the evolution of the field over time.
                                            [1] => Objetivo. Se analizó la evolución temática de la investigación sobre inteligencia artificial (IA) en educación mediante un enfoque longitudinal basado en palabras clave de autor, identificando cambios en el vocabulario del campo, temas consolidados y emergentes y reconfiguraciones de sus relaciones, así como su comportamiento a través de cinco dimensiones analíticas (tecnologías/técnicas, algoritmos/modelos, plataformas/sistemas, nivel educativo y enfoque metodológico).
Diseño/Metodología/Enfoque. Se recuperaron documentos en Scopus (1973–2025) mediante una búsqueda avanzada por título que combinaba términos del ámbito de la IA con términos educativos. El análisis se basó exclusivamente en las palabras clave de los autores. Se construyó una red global de coocurrencias y una visualización temporal (overlay) en VOSviewer. Con un umbral mínimo de 15 términos por clúster, el mapa final quedó compuesto por 1197 palabras clave agrupadas en 20 clústeres.
Resultados/Discusión. El campo de la IA en educación mostró una base técnica consolidada, asociada al aprendizaje automático, al aprendizaje profundo y a las redes neuronales, sobre la cual se superpone una temática más emergente dominada por la IA generativa. En 2024–2025 se intensificó el vocabulario ligado a ChatGPT, a los modelos de lenguaje y a las prácticas aplicadas, acompañado de un crecimiento visible de términos relacionados con la ética, la integridad y la gobernanza. La estructura por clústeres reflejó una organización temática amplia, con conexiones entre los frentes tecnológicos, pedagógicos e institucionales.
Conclusiones. La investigación en IA en educación ha transitado desde un foco más “clásico”, dominado por técnicas como machine learning y deep learning, hacia una etapa claramente marcada por la IA generativa y los modelos de lenguaje, como ChatGPT. Este giro se ha observado principalmente en el ámbito de la educación superior. Al mismo tiempo, temas como la alfabetización en IA, la integridad académica, los sesgos y la gobernanza se han convertido en los temas de debate más actuales.
Originalidad/Valor. El estudio aportó una mirada longitudinal sobre la investigación en IA en educación y una lectura instrumental por dimensiones, lo que facilitó interpretar qué temáticas se mantienen, cuáles emergen y cómo se ha reorganizado el campo a lo largo del tiempo.
                                        )

                                    [publisher] => Pro-Metrics
                                    [date] => 2026-04-02
                                    [type] => Array
                                        (
                                            [0] => info:eu-repo/semantics/article
                                            [1] => info:eu-repo/semantics/publishedVersion
                                            [2] => Peer-reviewed article
                                        )

                                    [format] => Array
                                        (
                                            [0] => application/pdf
                                            [1] => application/pdf
                                        )

                                    [identifier] => Array
                                        (
                                            [0] => https://ijsmc.pro-metrics.org/index.php/i/article/view/353
                                            [1] => 10.47909/ijsmc.353
                                        )

                                    [source] => Array
                                        (
                                            [0] => Iberoamerican Journal of Science Measurement and Communication; Vol. 6 (2026): Forthcoming articles; 1-18
                                            [1] => Iberoamerican Journal of Science Measurement and Communication; Vol. 6 (2026): Próximos artículos; 1-18
                                            [2] => Iberoamerican Journal of Science Measurement and Communication; Vol. 6 (2026): Artigos futuros; 1-18
                                            [3] => 2709-3158
                                            [4] => 2709-7595
                                        )

                                    [language] => Array
                                        (
                                            [0] => eng
                                            [1] => spa
                                        )

                                    [relation] => Array
                                        (
                                            [0] => https://ijsmc.pro-metrics.org/index.php/i/article/view/353/210
                                            [1] => https://ijsmc.pro-metrics.org/index.php/i/article/view/353/211
                                        )

                                    [rights] => Array
                                        (
                                            [0] => Copyright (c) 2026 Félix Caro-Soto, Viviana Vellon-Flores, Gladys Gavedia-Garcia, Julia Bravo-Montoya, Juan Huaman-Fritas
                                            [1] => https://creativecommons.org/licenses/by-nc/4.0
                                        )

                                )

                        )

                )

        )

)