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¿Cómo viven y narran su experiencia los egresados de las profesiones de la salud su interacción con la inteligencia artificial generativa durante la transición de la formación universitaria a la práctica profesional?

dc.contributor.advisorVergel Guerrero, John Alexander
dc.creatorArdila Trujillo, Edison Gilberto
dc.creatorGonzález Díaz, Edison Andrés
dc.creatorPérez Rodríguez, Sandy Johanna
dc.creator.degreeMagíster en Educación para Profesionales de la Salud
dc.date.accessioned2026-07-09T21:22:03Z
dc.date.available2026-07-09T21:22:03Z
dc.date.created2026-07-04
dc.descriptionIntroducción: La inteligencia artificial generativa (IAG) se incorpora rápidamente a la educación profesional en salud, pero aún se comprende poco cómo los egresados viven el tránsito desde la formación en pregrado y posgrado hacia el ejercicio profesional. Metodología: Mediante un diseño de indagación narrativa, participaron cuatro egresados de profesiones de la salud vinculados a escenarios clínicos, docentes, administrativos y de la industria farmacéutica. A partir de conversaciones narrativas, autorrelatos y diarios reflexivos, se compusieron textos de campo y relatos de investigación co-construidos atendiendo las dimensiones de temporalidad, socialidad y lugaridad. Hallazgos: Las historias de Ana, Carlos, Pepito y Dani muestran que el sentido atribuido a la IAG no dependió únicamente del mundo figurado donde se utilizó, sino de cómo se transformó en el tránsito entre la formación universitaria y la práctica profesional. En el mundo figurado de la formación, apareció asociada a sospecha, facilidad y temor a la sanción, porque podía poner en duda la autoría, el esfuerzo y el mérito académico; todo enmarcado en experiencias narradas más desde la penalización o la vigilancia que desde el acompañamiento. En el mundo figurado profesional, fue re-narrada como un recurso situado y aceptado respondiendo a necesidades de escritura, gestión administrativa, investigación y práctica clínica. La IAG siguió generando tensiones, pero cambiaron de forma al pasar del mundo formativo al profesional. Discusión: Estos hallazgos cuestionan que el principal problema educativo sea solo la falta de alfabetización en IAG o su regulación institucional. El aporte del estudio muestra que el sentido que se le atribuye a la IAG no depende únicamente de la herramienta vista como amenaza o recurso, sino del mundo figurado en que fue vivida y narrada. En el tránsito entre la formación y la práctica profesional, los egresados reconfiguran las formas de comprender, valorar y legitimar su uso. Conclusiones: Se propone desplazar la formación desde la lógica de aceptar o prohibir la IAG hacia una pedagogía de la transición, orientada a preparar a futuros profesionales o especialistas en salud para usarla de manera crítica, situada y responsable, sin delegar autoría, juicio clínico ni responsabilidad ética.
dc.description.abstractIntroduction: Generative artificial intelligence (GAI) is rapidly being incorporated into professional health education; however, limited understanding exists regarding how graduates experience the transition from undergraduate and postgraduate training to professional practice. Methods: This study adopted a narrative inquiry design involving four health professionals working in clinical, educational, administrative, and pharmaceutical industry settings. Data was generated through narrative conversations, self-narratives, and reflective journals. Field texts and research accounts were co-constructed, guided by the dimensions of temporality, sociality, and place. Findings: The stories of the participants revealed that the meaning attributed to GAI was not solely determined by the figurative world in which it was used, but rather by how it was transformed across the transition from academic training to professional practice. Within the figurative world of education, GAI was associated with suspicion, ease, and fear of sanction, as it challenged notions of authorship, effort, and academic merit, often framed through narratives of surveillance and penalization rather than guidance. In contrast, in the professional figurative world, GAI was re-narrated as a situated and accepted resource supporting writing, administrative management, research, and clinical practice. Although tensions persisted, their nature shifted during this transition. Discussion: These findings challenge the assumption that the primary educational issue lies solely in GAI literacy or institutional regulation. Instead, the study highlights that the meaning attributed to GAI depends on the figurative world in which it is experienced and narrated. As graduates’ transition into professional practice, they reconfigure how they understand, value, and legitimize its use. Conclusions: The study proposes moving beyond binary approaches of acceptance or prohibition toward a pedagogy of transition, aimed at preparing future health professionals to engage with GAI in a critical, situated, and responsible manner, without delegating authorship, clinical judgment, or ethical responsibility.
dc.format.extent91 pp
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.48713/10336_48030
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/48030
dc.language.isospa
dc.publisherUniversidad del Rosario
dc.publisherPontificia Universidad Javeriana. Facultad de Medicina
dc.publisher.departmentEscuela de Medicina y Ciencias de la Salud
dc.publisher.programMaestría en Educación para Profesionales de la Salud
dc.rightsAttribution 4.0 International*
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.source.bibliographicCitationAssociation of American Medical Colleges. (2024). Responsible use of AI in and for medical education: Key principles. https://www.aamc.org/about-us/mission-areas/medical-education/principles-ai-use
dc.source.bibliographicCitationAuerbach, C. F., & Silverstein, L. B. (2003). Convincing other people: The issues formerly known as reliability, validity, and generalizability. In Qualitative data: An introduction to coding and analysis (pp. 77–88). New York University Press.
dc.source.bibliographicCitationBassey, M. O. (2010). Educating for the real world: An illustration of John Dewey’s principles of continuity and interaction. Educational Studies, 36(1), 45–55. https://doi.org/10.1080/03055690903148480
dc.source.bibliographicCitationBerg, D.E. Zooming in on Dewey, Democracy, and Subjectivity in Post digital Education. (2023). Post digital Science and Education, 5(3), 456–470. https://doi.org/10.1007/s42438-023-00422-8
dc.source.bibliographicCitationBlease, C. R., Locher, C., Gaab, J., Hägglund, M., & Mandl, K. D. (2024). Generative artificial intelligence in primary care: an online survey of UK general practitioners. BMJ Health & Care Informatics, 31(1), e101102. https://doi.org/10.1136/bmjhci-2024-101102
dc.source.bibliographicCitationBlix, B. H., Steeves, P., Caine, V., & Clandinin, J. (2025). Beginning in multiple midst: living alongside in narrative inquiry. Qualitative Inquiry, 32(3–4), 291 297.
dc.source.bibliographicCitationChan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20, Article 38. https://doi.org/10.1186/s41239-023-00408-3
dc.source.bibliographicCitationChua, W. L., Cheong, C. W. Y., & Sim, J. (2023). ChatGPT and Generative Artificial Intelligence for Medical Education: Potential Impact and Opportunity. Medical Education, 57(8), 1015–1017. https://pubmed.ncbi.nlm.nih.gov/37651677
dc.source.bibliographicCitationClandinin, D. J. (2007). Handbook of Narrative Inquiry: Mapping a Methodology. Thousand Oaks, CA: Sage.
dc.source.bibliographicCitationClandinin, D. J. (2013). Engaging in Narrative Inquiry. Walnut Creek, CA: Left Coast Press.
dc.source.bibliographicCitationClandinin, D. J., & Connelly, F. M. (2000). Narrative inquiry: Experience and story in qualitative research. San Francisco, CA: Jossey-Bass.
dc.source.bibliographicCitationClandinin, D. J., Caine, V., & Lessard, S. (2018). The relational ethics of narrative inquiry. New York, NY: Routledge.
dc.source.bibliographicCitationClandinin, D. J., Cave, M. T., & Berendonk, C. (2016). Narrative inquiry: a relational research methodology for medical education. Medical Education, 51(1), 89-96. https://doi.org/10.1111/medu.13136
dc.source.bibliographicCitationDepartamento nacional de planeación (DPN). Departamento nacional de planeación. (2020). Documento CONPES 3975: política nacional para la transformación digital e inteligencia artificial. Gobierno de Colombia. Documentos CONPES
dc.source.bibliographicCitationDewey, J. (2022). Experience and Education. En Routledge eBooks (pp. 141-145). https://doi.org/10.4324/9781003340362-13
dc.source.bibliographicCitationFeng, Y., Chen, X., & Li, H. (2023). Behavioral Intentions to Adopt Artificial Intelligence in Healthcare: Exploring the Perception of Healthcare Professionals. Journal of Medical Internet Research, 25, e47629. https://pubmed.ncbi.nlm.nih.gov/40386172/
dc.source.bibliographicCitationGrillia, N., & Daskolia, M. (2025). Reimagining environmental education research through narrative inquiry as a relational ethical practice of care and reciprocity with teachers. Qualitative Research, 26(2), 393 415. https://doi.org/10.1177/14687941251350885
dc.source.bibliographicCitationÍndice Latam. (2024). Índice Latinoamericano de Inteligencia Artificial (ILIA) 2024. Índice latam. https://indicelatam.cl/wpcontent/uploads/2025/01/ILIA_2024_Ingles_020125_compressed.pdf?utm_source=chatgpt.com
dc.source.bibliographicCitationKaufman, D.M. (2018). Teaching and Learning in Medical Education: How theory can inform practice. In Understanding Medical Education (eds T. Swanwick, K. Forrest and B.C. O'Brien)
dc.source.bibliographicCitationKing, R. B., Wang, H., & Sun, Y. (2023). Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior. Educational Technology Research and Development, 71(4), 1835–1854. https://educationaltechnologyjournal.springeropen.com
dc.source.bibliographicCitationKung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education. PLOS Digital Health, 2(2), e0000198.https://doi.org/10.1371/journal.pdig.0000198
dc.source.bibliographicCitationKuribayashi, T., Chinen, T., Nagai, S., Shigeta, K., Fujirai, Y., & Uda, H. (2025). Delayed Use of ChatGPT by Japanese Doctors Due to Limited English Language Proficiency: A Survey-Based Study in Japan. Cureus. https://doi.org/10.7759/cureus.91443
dc.source.bibliographicCitationLee, P., Bubeck, S., & Petro, J. (2023). Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. New England Journal of Medicine, 388(13), 1233–1239. https://doi.org/10.1056/NEJMsr2214184
dc.source.bibliographicCitationLópez-García, M., & Martínez, D. (2022). Barreras en la capacitación tecnológica del profesorado de ciencias de la salud. Revista Iberoamericana de Educación, 89(2), 45–60.
dc.source.bibliographicCitationMinisterio de tecnologías de la información y las comunicaciones (MinTIC). Ministerio de tecnologías de la información y las comunicaciones. (2023). Política nacional de inteligencia artificial. Gobierno de Colombia. CONPES de transformación digital promoverá la competitividad del país y la eficiencia del sector público
dc.source.bibliographicCitationMorley, J., Machado, C. C. V., Burr, C., Cowls, J., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172. https://doi.org/10.1016/j.socscimed.2020.113172
dc.source.bibliographicCitationMorley, J., Machado, C. C. V., Burr, C., Cowls, J., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172. https://doi.org/10.1016/j.socscimed.2020.113172
dc.source.bibliographicCitationO'Connor, S., Chatfield, S. L., & Wood, D. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Frontiers in Artificial Intelligence, 6, 1192857. https://doi.org/10.3389/frai.2023.1192857
dc.source.bibliographicCitationOkamoto, S., Kataoka, M., Itano, M., & Sawai, T. (2025). AI-based medical ethics education: examining the potential of large language models as a tool for virtue cultivation. BMC Medical Education, 25(1). https://doi.org/10.1186/s12909-025-06801-y
dc.source.bibliographicCitationPérez, A., & Sánchez, J. (2022). IAG en la universidad: una propuesta de formación docente en el campo de la salud. Educación Médica, 23(3), 211–218.
dc.source.bibliographicCitationReuters. (2025, Junio 17). Latin American countries launch own AI model in September. https://www.reuters.com/world/americas/latin-american-countries-launch-own-ai-model-september-2025-06-17/
dc.source.bibliographicCitationRiessman, C. K. (2008). Narrative Methods for the Human Sciences. Thousand Oaks, CA: Sage.
dc.source.bibliographicCitationRincon, E. H. H., Jimenez, D., Aguilar, L. A. C., Flórez, J. M. P., Tapia, Á. E. R., & Peñuela, C. L. J. (2025). Mapping the use of artificial intelligence in medical education: a scoping review. BMC Medical Education, 25(1). https://doi.org/10.1186/s12909-025-07089-8
dc.source.bibliographicCitationRouzrokh, P., Khosravi, B., Faghani, S., Moassefi, M., Shariatnia, M. M. & Erickson, B. (2025). A Current Review of Generative AI in Medicine: Core Concepts, Applications, and Current Limitations. Current Reviews in Musculoskeletal Medicine. https://doi.org/10.1007/s12178-025-09961-y
dc.source.bibliographicCitationRuiz-Rodríguez, J. E., Morales, M., & Pérez, C. (2023). Nursing Educators' Perspectives on the Integration of Artificial Intelligence into Academic Settings. Journal of Nursing Education, 62(11), 611–617. https://pubmed.ncbi.nlm.nih.gov/40386172/
dc.source.bibliographicCitationSanad, A. H., Alsaegh, A. S., Abdulla, H. M., Mohamed, A. J., Alqassab, A., Sharaf, S. M. A., Abdulla, M. H., & Khadem, S. A. (2024). Perceptions of artificial intelligence in medicine among newly graduated interns: A cross-sectional study. Cureus, 16(10), e71216. https://doi.org/10.7759/cureus.71216
dc.source.bibliographicCitationShishehgar, S., Murray‐Parahi, P., Alsharaydeh, E., Mills, S., & Liu, X. (2025). Artificial Intelligence in Health Education and Practice: A Systematic Review of Health Students’ and Academics’ Knowledge, Perceptions and Experiences.
dc.source.bibliographicCitationTang, O. Y., Li, C., & Patel, R. (2023). Integrating artificial intelligence into medical education: a roadmap informed by a survey of faculty and students. Advances in Medical Education and Practice, 14, 487–496. https://pubmed.ncbi.nlm.nih.gov/40660466/
dc.source.bibliographicCitationWang, Y., Li, J., & Zhang, P. (2023). Opportunities, Challenges, and Future Directions of Generative Artificial Intelligence in Medical Education: Scoping Review. JMIR Medical Education, 9, e45138. Disponible en: https://pubmed.ncbi.nlm.nih.gov/37862079/
dc.source.bibliographicCitationWasyliw, D., McGregor, T. A., Schaefer, L., & Clandinin, D. J. (2025). Skennenko:wa - A Narrative Inquiry Into Experiences of Collaboration. International Journal of Qualitative Methods, 24. https://doi.org/10.1177/16094069251392414
dc.source.bibliographicCitationWei, L. (2023). Narrative Inquiry: A Research Method in the Education Field. World Journal of Education, 13(6), 35. https://doi.org/10.5430/wje.v13n6p35
dc.source.bibliographicCitationWeidener, L., & Fischer, M. (2024). Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education. JMIR Medical Education, 10, e55368. https://doi.org/10.2196/55368
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subjectInteligencia artificial
dc.subjectProfesiones de la salud
dc.subjectEgresados
dc.subjectIndagación narrativa.
dc.subject.keywordGenerative artificial intelligence
dc.subject.keywordHealth professions education
dc.subject.keywordGraduates
dc.subject.keywordNarrative inquiry
dc.title¿Cómo viven y narran su experiencia los egresados de las profesiones de la salud su interacción con la inteligencia artificial generativa durante la transición de la formación universitaria a la práctica profesional?
dc.title.TranslatedTitle¿How Do Health Professions Graduates Experience and Narrate Their Interaction with Generative Artificial Intelligence During the Transition from University Education to Professional Practice?
dc.title.alternativeCómo viven y narran su experiencia los egresados de las profesiones de la salud su interacción con la inteligencia artificial generativa durante la transición de la formación universitaria a la práctica profesional
dc.typemasterThesis
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersion
dc.type.spaTrabajo de grado
local.department.reportEscuela de Medicina y Ciencias de la Salud
local.regionesVirtual
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