Pedagogical strategy based on generative artificial intelligence for the self-directed learning of nursing students
DOI:
https://doi.org/10.62452/s0j91k94Keywords:
Self-directed learning, generative artificial intelligence, nursing education, nursing students, educational innovationAbstract
Generative artificial intelligence offers new opportunities to strengthen self-directed learning in nursing education when its integration is supported by a structured pedagogical design. This study aimed to evaluate the effect of the E-PAGIA strategy, organized into planning, construction, self-monitoring, and interaction/transfer stages, on self-directed learning among nursing students. A quantitative, quasi-experimental, longitudinal study was conducted with 128 students, distributed between an experimental group and a control group, with 64 participants in each. The intervention lasted eight weeks. Self-directed learning was assessed using the Self-Directed Learning Instrument, considering both the overall score and its dimensions. The groups showed comparable baseline scores. Following the intervention, the experimental group increased its mean overall score from 65.00 to 82.35 points, whereas the control group increased from 65.75 to 69.46. Improvements in the experimental group were observed across all four dimensions and were particularly pronounced in learning motivation and planning and implementation. Analysis adjusted for baseline scores confirmed significant differences between groups and showed a large effect associated with the strategy. The findings indicate that E-PAGIA can foster self-directed learning when generative artificial intelligence is integrated as cognitive support within a pedagogical sequence that preserves students' intellectual responsibility, teacher mediation, and critical verification of information. The study concludes that E-PAGIA represents a relevant pedagogical innovation for nursing education.
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Amankwaa, I., Ekpor, E., Cudjoe, D., Kobiah, E., Fuseini, A.-K. J., Diebieri, M., Gyamfi, S., & Brownie, S. (2025). Patterns, advances, and gaps in using ChatGPT and similar technologies in nursing education: A PAGER scoping review. Nurse Education Today, 153, 106822. https://doi.org/10.1016/j.nedt.2025.106822
Bozkurt, S. A., Aydoğan, S., Dursun Ergezen, F., & Türkoğlu, A. (2025). A systematic review and sequential explanatory synthesis: Artificial intelligence in healthcare education, a case of nursing. International Nursing Review, 72(2), e70018. https://doi.org/10.1111/inr.70018
Cheng, S.-F., Kuo, C.-L., Lin, K.-C., & Lee-Hsieh, J. (2010). Development and preliminary testing of a self-rating instrument to measure self-directed learning ability of nursing students. International Journal of Nursing Studies, 47(9), 1152–1158. https://doi.org/10.1016/j.ijnurstu.2010.02.002
Doston, T., Fontenot, J., Morris, D., & Hebert, M. (2025). The use of artificial intelligence in nursing education: A scoping review. Journal of Nursing Education, 64(8), 479–488. https://doi.org/10.3928/01484834-20250313-03
El Arab, R. A. A., Alshakihs, A. H., Alabdulwahab, S. H., Almubarak, Y. S., Alkhalifah, S. S., Abdrbo, A., Hassanein, S., & Sagbakken, M. (2025). Artificial intelligence in nursing: A systematic review of attitudes, literacy, readiness, and adoption intentions among nursing students and practicing nurses. Frontiers in Digital Health, 7, 1666005. https://doi.org/10.3389/fdgth.2025.1666005
García-Mc Collins, M. del P. (2025). Innovación en la práctica de enfermería: Implementando nuevas tecnologías y enfoques para mejorar la atención al paciente. Sophia Research Review, 2(3), 15–19. https://doi.org/10.64092/388she15
Gunawan, J., Aungsuroch, Y., & Montayre, J. (2024). ChatGPT integration within nursing education and its implications for nursing students: A systematic review and text network analysis. Nurse Education Today, 141, 106323. https://doi.org/10.1016/j.nedt.2024.106323
Hardie, P., Darley, A., Derwin, R., Eustace-Cook, J., Kearns, S., Mc Brien, B., Siddiquee, A., Zheng, D., & Mooney, M. (2026). Applications, attitudes and ethical considerations of generative artificial intelligence (Gen AI) in nursing education: A scoping review. BMC Nursing, 25, 148. https://doi.org/10.1186/s12912-025-04253-9
Kucukkaya, A., Arikan, E., & Goktas, P. (2024). Unlocking ChatGPT's potential and challenges in intensive care nursing education and practice: A systematic review with narrative synthesis. Nursing Outlook, 72(6), 102287. https://doi.org/10.1016/j.outlook.2024.102287
Lifshits, I., & Rosenberg, D. (2024). Artificial intelligence in nursing education: A scoping review. Nurse Education in Practice, 80, 104148. https://doi.org/10.1016/j.nepr.2024.104148
Ma, J., Wen, J., Qiu, Y., Wang, Y., Xiao, Q., Liu, T., Zhang, D., Zhao, Y., Lu, Z., & Sun, Z. (2025). The role of artificial intelligence in shaping nursing education: A comprehensive systematic review. Nurse Education in Practice, 84, 104345. https://doi.org/10.1016/j.nepr.2025.104345
Sánchez-Núñez, K. E. (2026). Educación de enfermería en la era digital: SmartNurse como puente hacia la innovación. Sophia Research Review, 3(1), 5–8. https://doi.org/10.64092/vafchy37
Shen, W.-Q., Chen, H.-L., & Hu, Y. (2014). The validity and reliability of the self-directed learning instrument (SDLI) in mainland Chinese nursing students. BMC Medical Education, 14, 108. https://doi.org/10.1186/1472-6920-14-108
United Nations Educational, Scientific and Cultural Organization. (2023). Guidance for generative AI in education and research. https://unesdoc.unesco.org/ark:/48223/pf0000386693
World Health Organization. (2025). Ethics and governance of artificial intelligence for health: Guidance on large multi-modal models. https://iris.who.int/server/api/core/bitstreams/e9e62c65-6045-481e-bd04-20e206bc5039/content
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Copyright (c) 2026 Alberto Figueroa-Reyes, Irina García-Martínez, Rosangela Caicedo-Quiroz, Jarol Figueroa-García (Autor/a)

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