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                            [identifier] => oai:ojs2.ijsmc.pro-metrics.org:article/372
                            [datestamp] => 2026-05-13T07:11:36Z
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                                    [title] => Array
                                        (
                                            [0] => Generative artificial intelligence and the transformation of the scientific research process through a critical review of the research cycle 
                                            [1] => Inteligencia artificial generativa y transformación del proceso de investigación científica mediante una revisión crítica del ciclo investigativo 
                                        )

                                    [creator] => Array
                                        (
                                            [0] => Marin-Rodriguez, William
                                            [1] => Garivay-Torres, Flor
                                            [2] => Susanibar-Ramírez, Edgar
                                            [3] => Andrade-Giron, Elia
                                        )

                                    [subject] => Array
                                        (
                                            [0] => large language models
                                            [1] => research methodology
                                            [2] => academic writing
                                            [3] => AI ethics
                                            [4] => knowledge production
                                            [5] => modelos de lenguaje a gran escala
                                            [6] => metodología de investigación
                                            [7] => redacción académica
                                            [8] => ética de la IA
                                            [9] => producción de conocimiento
                                        )

                                    [description] => Array
                                        (
                                            [0] => Objective. The objective of this study was to critically examine the role of generative artificial intelligence (AI) throughout the entire scientific research lifecycle. This analysis identified the applications, benefits, limitations, and emerging tensions of the aforementioned technology. The proposed integrative perspective combined the use of the aforementioned technology with human judgment and critical validation.
Design/Methodology/Approach. A narrative review with critical analysis was conducted based on an intentional selection of recent scientific literature (2020–2026) from databases such as Scopus, Web of Science, and Google Scholar. The analysis was structured according to the main phases of the research process: ideation, literature review, methodological design, data analysis, and scientific writing. A systematization matrix was employed to support the analysis and facilitate the identification of patterns, convergences, and gaps in the literature. Furthermore, generative AI tools were employed for the synthesis of documents and the organization of knowledge.
Results/Discussion. The findings indicated the pervasive integration of generative AI across all phases of the research process, resulting in substantial enhancements in operational efficiency, particularly in literature reviews and scientific writing. However, limitations were also identified, including the generation of superficial content, biases derived from training data, and risks that affect methodological and analytical validity. A critical analysis of these results highlighted structural tensions between automation and human control, as well as between productivity and scientific quality. This analysis underscored the imperative for uninterrupted expert oversight.
Conclusions. The application of generative AI does not result in the substitution of researchers; rather, it leads to a redefinition of their roles, which now encompass critical validation and epistemological control. Within the context of a university, the implementation of generative AI cannot be restricted; rather, it must be integrated into research training as a fundamental competence. The primary challenge confronting researchers is not technological but rather epistemological in nature. That is, the imperative lies in ascertaining that the integration of these tools does not serve to undermine but rather fortify the foundational tenets of scientific inquiry.
                                            [1] => Objetivo. Analizar críticamente el papel de la inteligencia artificial (IA) generativa a lo largo de todo el ciclo de la investigación científica, identificando sus aplicaciones, beneficios, limitaciones y tensiones emergentes, y discutir un enfoque integrador basado en la interacción entre IA, juicio humano y validación crítica.
Diseño/Metodología/Enfoque. Se desarrolló una revisión narrativa con análisis crítico basada en la selección intencional de literatura científica reciente (2020–2026) proveniente de bases de datos como Scopus, Web of Science y Google Scholar. El análisis se estructuró en función de las principales fases del proceso investigativo —ideación, revisión de la literatura, diseño metodológico, análisis de datos y redacción científica—, y se apoyó en una matriz de sistematización que permitió identificar patrones, convergencias y vacíos en la literatura. Asimismo, se incorporó el uso de herramientas de IA generativa para la síntesis documental y la organización del conocimiento.
Resultados/Discusión. Los resultados evidencian una incorporación transversal de la IA generativa en todas las fases del proceso de investigación, con mejoras significativas en la eficiencia operativa, especialmente en la revisión de la literatura y la redacción científica. No obstante, se identifican limitaciones relevantes asociadas a la generación de contenido superficial, a sesgos derivados de los datos de entrenamiento y a riesgos para la validez metodológica y analítica. El análisis crítico revela tensiones estructurales entre la automatización y el control humano, así como entre la productividad y la calidad científica, y destaca la necesidad de una supervisión experta constante.
Conclusiones. La IA generativa no sustituye al investigador, sino que redefine su rol hacia funciones de validación crítica y de control epistemológico. En el ámbito universitario, su uso no puede restringirse, sino que debe integrarse como una competencia clave en la formación investigativa. El enfoque integrador identificado permite comprender la producción científica como un proceso de interacción entre la IA, el juicio humano y la validación crítica, y ofrece un marco conceptual para su uso responsable. En este contexto, el principal desafío no es tecnológico, sino epistemológico: garantizar que la incorporación de estas herramientas fortalezca, y no comprometa, los principios fundamentales de la investigación científica.
                                        )

                                    [publisher] => Pro-Metrics
                                    [date] => 2026-05-13
                                    [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/372
                                            [1] => 10.47909/ijsmc.372
                                        )

                                    [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/372/214
                                            [1] => https://ijsmc.pro-metrics.org/index.php/i/article/view/372/215
                                        )

                                    [rights] => Array
                                        (
                                            [0] => Copyright (c) 2026 William Marin-Rodriguez, Flor Garivay-Torres, Edgar Susanibar-Ramírez, Elia Andrade-Giron
                                            [1] => https://creativecommons.org/licenses/by-nc/4.0
                                        )

                                )

                        )

                )

        )

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