Methodological archetype based on Artificial Intelligence, r language and bibliographic management to strengthen research skills in university students
Keywords:
inteligencia artificial, lenguaje de programación R, habilidades investigativas, guía metodológica, educación superiorAbstract
Objective: Despite the recognition of research as a training axis in higher education, structural weaknesses persist in the development of research skills, especially in methodological design, so the proposal is to design a methodological archetype based on artificial intelligence, R language and bibliographic management to strengthen research skills in university students. Methodology: A qualitative study with a phenomenological design was developed at the San Ignacio de Loyola University, Lima, Peru, as part of the EDUSIL projects. 55 university students and 4 experts selected by intentional sampling participated. Semi-structured interviews and focus groups were applied, analyzed through open coding and phenomenological synthesis. The archetype was structured in six phases: questioning, information search, management with Zotero, R–IA–ChatGPT integration, bibliometric management and scientific writing. Results: The findings show substantive improvements in five dimensions: epistemological knowledge, interpretive skills, methodological knowledge, information management and scientific communication. Seven empirical categories emerged that reflect a transition towards higher-order skills, highlighting the strengthening of critical analysis, interpretation of results and bibliometric literacy. Conclusions: The methodological archetype constitutes an innovative and replicable proposal that integrates technology, data analysis and critical thinking, promoting an epistemological transformation of the student towards an active, reflective and meta-investigative role in the production of scientific knowledge. Contribution: the study supports the importance of designing university policies aimed at strengthening the research culture, promoting training ecosystems where technology is not an end in itself, but rather an epistemological mediator.
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Copyright (c) 2026 Omar Bellido Valdiviezo, Angel Deroncele-Acosta , Liliana Morales-Yanayaco, karla Bolo-Romero , Luis Alberto Calderón-Coello

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