FICHA TÉCNICA
Fecha de publicación:
06/11/2024
Doi del capítulo:
Título del libro: Artificial Intelligence and Education
URL del libro:
ISBN del libro: 9788410282452
DOI del libro:
Abstract
The integration of artificial intelligence (AI) into learning environments poses a challenge in advancing towards more efficient interactive methodologies. The use of AI-based learning assistants, especially generative language models like OpenAI’s GPT, can expand the scope of methodologies such as clinical simulation by generating interactions where AI assumes the role of a standardized patient. Clinical simulation recreates, substitutes, and/or extends real experiences through guided experiences that evoke or replicate substantial aspects of the real professional context in a fully interactive manner. The standardized patient is an actor trained to perform predefined responses based on the students’ behaviour and performance. With appropriate AI training, focused on instruction and adaptation to different patient profiles based on their health-disease processes, it is possible to design and implement clinical simulation scenarios where students interact with it. The authenticity of AI allows achieving a high degree of fidelity, and its scope surpasses the limits of synchronous in-person demand of a standardized actor, exponentially multiplying the capacity to generate simulated learning environments. This chapter outlines the keys to integrating AI as a standardized patient into simulated learning experiences.
Palabras clave
Autores
Mercedes Lorena Pedrajas López, Ana Sanz Cortés, Eva García Carpintero-Blas, Esther Martínez Miguel, Sara Uceda Gutiérrez
Cómo citar
Pedrajas López, M. L., Sanz Cortés, A., García Carpintero-Blas, E., Martínez Miguel, E., Uceda Gutiérrez, S. (2024). Training GPT as a Standardized Patient. In Díaz-Noguera, M. D., Hervás-Gómez, C., Sánchez-Vera, F. (Coords.), Artificial Intelligence and Education (pp. 189-204). Octaedro. https://doi.org/10.36006/09643-1-12
Referencias bibliográficas
“Abdellatif, H., Al Mushaiqri, M., Albalushi, H., Al-Zaabi, A. A., Roychoudhury, S., & Das, S. (2022). Teaching, learning and assessing anatomy with Artificial Intelligence: The Road to a better future. International Journal of Environmental Research and Public Health, 19(21), 14209. https://doi.org/10.3390/ijerph192114209
Abshire, M. A., Li, X., Basyal, P. S., Teply, M. L., Singh, A. L., Hayes, M. M., & Turnbull, A. E. (2020). Actor feedback and rigorous monitoring: Essential quality assurance tools for testing behavioral interventions with simulation. PloS one, 15(5), e0233538. https://doi.org/10.1371/journal.pone.0233538
Adamson, K., Kardong-Edgren, S. y Willhaus J. (2013). An updated review of published simulation evaluation instruments. Clinical Simulation in Nursing, 9, e393-e400. http://dx.doi.org/10.1016/j.ecns.2012.09.004
Bandura A. (1977). Self-efficacy: Toward a Unifying theory of behavioral change. Psychological Rev, 84(2), 91-215.
Barrios, S., Urrutia, M. y Rubio, M. (2017). Impacto de la simulación en el desarrollo de la autoeficacia y del locus de control en estudiantes de enfermería. Educación Médica Superior, 31(1), 125-136. https://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0864-21412017000100012&lng=es&tlng=es
Cheng, A., Morse, K. J., Rudolph, J., Arab, A. A., Runnacles, J., & Eppich, W. (2016). Learner-centered debriefing for health care simulation education: Lessons for faculty development. Simulation in healthcare. Journal of the Society for Simulation in Healthcare, 11(1), 32-40. https://doi.org/10.1097/SIH.0000000000000136
Díaz, D. A., & Cimadevilla, B. (2019). Educación basada en simulación: debriefing, sus fundamentos, bondades y dificultades. Simulación Clínica, 2 (1), 95-103. https://dx.doi.org/10.35366/RSC192F
Gormley, G. J., Carr, D., Murphy, P., Tallentire, V. R., & Smith, S. E. (2023). Unlocking the learning potential of simulation-based education. British Journal of Hospital Medicine, 84(12), 1-8. https://doi.org/10.12968/hmed.2023.0353
Kononowicz, A. A., Woodham, L. A., Edelbring, S., Stathakarou, N., Davies, D., Saxena, N., Tudor Car, L., Carlstedt-Duke, J., Car, J., & Zary, N. (2019). Virtual patient simulations in health professions education: Systematic review and meta-analysis by the digital health education collaboration. Journal of Medical Internet Research, 21(7), e14676.
Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, et al. (2022). Performance of GPT on USMLE: potential for AI-assisted medical education using large language models. PLoS Digit Health, 2(2), e0000198.
Leng, L. (2024). Challenge, integration, and change: GPT and future anatomical education. Medical Education Online, 29(1), 2304973. https://doi.org/10.1080/10872981.2024.2304973
Lioce L. (Ed.), Lopreiato J. (Founding Ed.), Downing D., Chang T.P., Robertson J.M., Anderson M., Diaz D.A., Spain A.E. (Eds.), &Terminology and Concepts Working Group (2020). Healthcare Simulation Dictionary (2nd edition). Agency for Healthcare Research and Quality. https://doi.org/10.23970/simulationv2
McCoy, L. G., Nagaraj, S., Morgado, F., Harish, V., Das, S., & Celi, L. A. (2020). What do medical students actually need to know about artificial intelligence? NPJ Digital Medicine, 3, 86. https://doi.org/10.1038/s41746-020-0294-7
Merchán-Baeza, J. A., González-Sánchez, M., & Pérez-Cruzado, D. (2021). Simulación con pacientes estandarizados en ciencias de la salud: una revisión sistemática. Revista Chilena de Terapia Ocupacional, 22(2), 25-43. https://doi.org/10.5354/0719-5346.2021.61071
Morcela, Ó. A. (2023). GPT: la IA está aquí y nos desafía. Revista Internacional de Ingeniería Industrial, 6, 3-6. http://www3.fi.mdp.edu.ar/otec/revista/index.php/AACINI-RIII/article/view/67
Motola, I., Devine, L. A., Chung, H. S., Sullivan, J. E., & Issenberg, S.B. (2013). Simulation in healthcare education: A best evidence practical guide. Med. Teach., 82(10) 1511-1530. https://doi.org/10.3109/0142159X.2013.818632
Rouhiainen, L. (2018). Inteligencia artificial. 101 cosas que debes saber hoy sobre nuestro futuro. Alienta.
Rudolph, J. W., Raemer, D. B., & Simon, R. (2014). Establishing a safe container for learning in simulation the role of the presimulation briefing. Simul Healthc, 9(6), 339- 349. https://doi.org/10.1097/SIH.0000000000000047
Ruiz, R., & Caballero, F. (2014). Programa para seleccionar y entrenar pacientes estandarizados en el contexto de un currículo universitario de simulación clínica. Fundación Educación Médica, 17(4), 199-204. https://dx.doi.org/10.4321/S2014-98322014000400005
Schön, D. A. (1992). The theory of inquiry: Dewey’s legacy to education. Curriculum Inquiry, 22(2), 119-139.
Schwarzer, R., & Jerusalem, M. (1993). Measurement of Perceived Self-Efficacy: Psychometric Scales for Cross-Cultural Research. Freie University.
Turner, S., & Harder, N. (2018). Psychological safe environment: A concept analisis. Clinical Simulation in Nursing, 18(5), 47-55. https://doi.org/10.1016/j.ecns.2018.02.0”