Beyond the Tool: Ethical and Pedagogical Challenges of Using Generative AI in Medical Research Training

Parada Romero - Universidad Cooperativa de Colombia
DIAZ,L - UNIVERSIDAD COOPERATIVA DE COLOMBIA
Alonso R, O - Universidad Cooperativa de Colombia

Resumen

Este estudio tuvo como objetivo analizar las percepciones de estudiantes de medicina y docentes sobre el uso de la inteligencia artificial generativa en la investigación formativa, con especial atención a los desafíos éticos y pedagógicos asociados con su incorporación en la formación universitaria en investigación. Se empleó un diseño cualitativo de alcance exploratorio, que combinó encuestas estructuradas aplicadas a estudiantes de medicina y docentes con una revisión sistemática de literatura académica reciente sobre inteligencia artificial generativa en educación superior y ciencias de la salud. Los hallazgos evidencian una percepción generalmente favorable, aunque cautelosa, frente al uso de la inteligencia artificial generativa. Los participantes reconocieron su potencial para apoyar la organización de ideas, la escritura académica y el desarrollo de tareas investigativas; sin embargo, también manifestaron reservas sobre la calidad, profundidad, pertinencia e implicaciones éticas del contenido generado por estas herramientas. Los resultados sugieren que la integración de la inteligencia artificial generativa en la formación investigativa médica no debe entenderse únicamente como un asunto tecnológico, sino como un desafío ético y pedagógico que exige alfabetización digital crítica, lineamientos institucionales claros y mediación docente activa. El estudio concluye que la inteligencia artificial generativa puede contribuir a la formación en investigación médica cuando se emplea como recurso complementario en entornos pedagógicamente orientados y éticamente regulados. Su integración efectiva depende del fortalecimiento del pensamiento crítico, la autonomía investigativa y las políticas institucionales que prevengan la dependencia tecnológica y promuevan prácticas académicas responsables.Palabras clave: saberes locales, educación decolonial, identidad cultural, currículo contextualizado, aprendizaje significativo.

Abstract

This study aimed to analyze the perceptions of medical students and faculty regarding the use of generative artificial intelligence in formative research, with particular attention to the ethical and pedagogical challenges associated with its incorporation into university-based research training. An exploratory qualitative design was employed, combining structured surveys administered to medical students and faculty with a systematic review of recent academic literature on generative artificial intelligence in higher education and health sciences. Results: The findings reveal a generally favorable but cautious perception of generative artificial intelligence. Participants recognized its potential to support idea organization, academic writing, and research-related tasks; however, they also expressed reservations regarding the quality, depth, relevance, and ethical implications of AI-generated content. The results suggest that the integration of generative artificial intelligence in medical research training should not be understood merely as a technological issue, but as an ethical and pedagogical challenge that requires critical digital literacy, clear institutional guidelines, and active teacher mediation. The study concludes that generative artificial intelligence can contribute to research training in medical education when used as a complementary resource within pedagogically guided and ethically regulated environments. Its effective integration depends on strengthening critical thinking, research autonomy, and institutional policies that prevent technological dependency and promote responsible academic practice.

Palabras clave

tecnologías de la información y la comunicación, investigación educativa, Innovación pedagógica, practica pedagógica, tecnología de la educación

Referencias

Almasre, M. (2024). Development and evaluation of a custom GPT for the assessment of students' designs in a typography course. <i>Education Sciences, 14</i>(2), 1–19. https://doi.org/10.3390/educsci14020148

Bates, T. (2015). <i>Teaching in a digital age</i> (2nd ed.). University of British Columbia.

Buckingham, D. (2019). Teaching media in a "post-truth" age: Fake news, media bias and the challenge for media/digital literacy education. <i>Culture and Education, 31</i>(2), 1–10. https://doi.org/10.1080/11356405.2019.1603814

Butarbutar, R., & González, R. (2025). Factors influencing AI-assisted thesis writing in university: A pull-push-mooring theory narrative inquiry study. <i>Data and Metadata, 4</i>(203), 1–17. https://doi.org/10.56294/dm2025203

Meo, S. A., Al-Masri, A. A., Alotaibi, M. S., Meo, M. S., & Meo, M. S. (2023). ChatGPT knowledge evaluation in basic and clinical medical sciences: Multiple choice question examination-based performance. <i>Healthcare, 11</i>(14), 1–11. https://doi.org/10.3390/healthcare11142046

Michel-Villarreal, R., Vilalta-Perdomo, E., Salinas-Navarro, D., Thierry-Aguilera, R., & Gerardou, F. (2023). Challenges and opportunities of generative AI for higher education as explained by ChatGPT. <i>Education Sciences, 13</i>(856), 1–18. https://doi.org/10.3390/educsci13090856

Raghuwanshi, S., Hasan, A., Agrawal, R., Shekhar, A., Dubey, N., & Kumar, P. (2025). Thematic analysis: Exploring teacher and student perspectives on utilizing ChatGPT for content generation. <i>Data and Metadata, 4</i>, 1–11. https://doi.org/10.56294/dm2025676

Selwyn, N. (2020). Digital automation: Resisting the digital automation of teaching. <i>Australian TAFE Teacher, 54</i>(2), 17–19.

Selwyn, N., & Jandrić, P. (2020). Postdigital living in the age of COVID-19: Unsettling what we see as possible. <i>Postdigital Science and Education, 2</i>, 989–1005. https://doi.org/10.1007/s42438-020-00166-9

Siemens, G. (2020). Learning analytics and educational data mining: Towards communication and collaboration. In D. Ifenthaler & D. Gibson (Eds.), <i>Foundations of learning analytics and educational data mining</i> (pp. 115–133). Springer. https://doi.org/10.1007/978-3-030-21847-6_7

Wang, L., & Ren, B. (2024). Enhancing academic writing in a linguistics course with generative AI: An empirical study in a higher education institution in Hong Kong. <i>Education Sciences, 14</i>(12), 1–11. https://doi.org/10.3390/educsci14121329

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: Where are the educators? <i>International Journal of Educational Technology in Higher Education, 16</i>(39), 1–27. https://doi.org/10.1186/s41239-019-0171-0