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Federación Latinoamericana de Simulación Clínica y Seguridad del Paciente

2025, Number 2

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Simulación Clínica 2025; 7 (2)

Development of an educational debriefing simulator using virtual reality and artificial intelligence technologies

Díaz-Guio DA, Herrera D, Lara-Espinoza C, Acuña O, Infante-Villagrán V

Language: Spanish
References: 32
Page: 76-80
PDF size: 422.67 Kb.


ABSTRACT

Introduction: educational debriefing is one of the most important and valued aspects of simulation-based education. However, it is a skill that requires deliberate practice to develop and maintain, and there are no simulators available for simulation educators to train on. Objective: to describe the process of developing a simulator that integrates virtual reality, artificial intelligence, and immediate feedback for training educators in educational debriefing. Material and methods: we designed a simulator using a game matrix and artificial intelligence based on large language models. Conclusion: this simulator is an innovation that incorporates artificial intelligence for educational debriefing training. This prototype allows educators to practice reflective conversations in real time and receive verbal and written feedback from the simulator.


REFERENCES

  1. Díaz-Guío DA, Cimadevilla-Calvo B. Educación basada en simulación: debriefing, sus fundamentos, bondades y dificultades. Simulación Clínica. 2019; 1 (2): 95-103. doi: 10.35366/RSC192F.

  2. Levett-Jones T, Lapkin S. A systematic review of the effectiveness of simulation debriefing in health professional education. Nurse Educ Today. 2014; 34 (6): e58-e63.

  3. Dinkins CS, Cangelosi PR. Putting Socrates back in Socratic method: theory-based debriefing in the nursing classroom. Nurs Philos. 2019; 20 (2): 1-7.

  4. Rudolph JW, Simon R, Raemer DB, Eppich WJ. Debriefing as formative assessment: closing performance gaps in medical education. Acad Emerg Med. 2008; 15 (11): 1010-1016.

  5. Langan-Fox J, Anglim J, Wilson JR. Mental models, team mental models, and performance: process, development, and future directions. Hum Factors Ergon Manuf. 2004; 14 (4): 331-352.

  6. Klimoski R, Mohammed S. Team Mental Model: construct or metaphor? J Manage. 1994; 20 (2): 403-437. Available from: http://journals.sagepub.com/doi/10.1177/014920639402000206

  7. Johnson-Laird P. Mental models and human reasoning. Proc Natl Acad Sci U S A. 2010; 107 (43): 18243-18250.

  8. Díaz-Guio DA, Ruiz-Ortega FJ. Relationship among mental models, theories of change, and metacognition: structured clinical simulation. Colomb J Anesthesiol. 2019; 47 (14): 113-116. doi: 10.1097/CJ9.0000000000000107

  9. Ericsson KA. Deliberate practice and the acquisition and maintenance of expert performance in medicine and related domains. Acad Med. 2004; 79 (10 Suppl): S70-S81.

  10. Dreyfus SE, Dreyfus HL. A five-stage model of the mental activities involved in directed skill acquisition. Operations Research Center. 1980; 1-18.

  11. Roussin C, Sawyer T, Weinstock P. Assessing competency using simulation: the SimZones approach. BMJ Simul Technol Enhanc Learn. 2020; 6 (5): 262-267.

  12. Fey MK, Roussin CJ, Rudolph JW, Morse KJ, Palaganas JC, Szyld D. Teaching, coaching, or debriefing with good judgment: a roadmap for implementing "With Good Judgment" across the SimZones. Adv Sim. 2022; 7 (1): 1-9. doi: 10.1186/s41077-022-00235-y

  13. Fraser KL, Ayres P, Sweller J. Cognitive load theory for the design of medical simulations. Sim Healthc. 2015; 10 (5): 295-307.

  14. Fraser KL, Meguerdichian MJ, Haws JT, Grant VJ, Bajaj K, Cheng A. Cognitive Load Theory for debriefing simulations: implications for faculty development. Adv Sim (Lond). 2018; 3: 28. doi: 10.1186/s41077-018-0086-1

  15. Moor M, Banerjee O, Abad ZSH, Krumholz HM, Leskovec J, Topol EJ, et al. Foundation models for generalist medical artificial intelligence. Nature. 2023; 616 (7956): 259-265. Available from: http://www.ncbi.nlm.nih.gov/pubmed/37045921

  16. OPS. Inteligencia artificial, ocho principios rectores de la transformación digital del sector salud caja de herramientas de transformación digital. Inteligencia artificial. 2023. Available from: https://iris.paho.org/bitstream/handle/10665.2/57128/OPSEIHIS230003_spa.pdf?sequence=1&isAllowed=y

  17. Lee J, Wu AS, Li D, Kulasegaram KM. Artificial intelligence in undergraduate medical education: a scoping review. Acad Med. 2021; 96 (11): S62-S70.

  18. Charow R, Jeyakumar T, Younus S, Dolatabadi E, Salhia M, Al-Mouaswas D, et al. Artificial intelligence education programs for health care professionals: scoping review. JMIR Med Educ. 2021; 7 (4): 1-22.

  19. Díaz-Guio DA, Henao J, Pantoja A, Arango MA, Díaz-Gómez AS, Gómez AC. Artificial intelligence, applications and challenges in simulation-based education. Colomb J Anesthesiol. 2024; 52 (1).

  20. Song Y, Wu K, Ding J. Developing an immersive game-based learning platform with generative artificial intelligence and virtual reality technologies – "LearningverseVR". Computers & Education: X Reality. 2024; 4 (100069): 100069.

  21. Girvan C. What is a virtual world? Definition and classification. Educ Technol Res Dev. 2018; 66 (5): 1087-1100.

  22. Garrido-Iñigo P, Rodríguez-Moreno F. The reality of virtual worlds: pros and cons of their application to foreign language teaching. Interact Learn Environ. 2015; 23 (4): 453-470.

  23. Smith RC, Iversen OS, Hjorth M. Design thinking for digital fabrication in education. Int J Child Comput Interact. 2015; 5: 20-28.

  24. Deitte LA, Omary RA. The power of design thinking in medical education. Acad Radiol. 2019. 26 (10): 1417-1420.

  25. Dorst K. The core of "design thinking" and its application. Des Stud. 2011; 32 (6): 521-532.

  26. Mariani B, Cantrell MA, Meakim C, Prieto P, Dreifuerst KT. Structured debriefing and students' clinical judgment abilities in simulation. Clin Simul Nurs. 2013; 9 (5): e147-e155. doi: 10.1016/j.ecns.2011.11.009

  27. Johnson-Laird P. Mental Models. Cambridge: Harvard University Press; 1983. 513 p.

  28. Centro de Simulación Médica. Evaluación del debriefing para la Simulación en Salud (EDSS). Debriefing Assessment for Simulation in Healthcare. Manual del Evaluador. 2019.

  29. Díaz-Guio DA, Arias-Botero JH, Álvarez C, Gaitán-Buitrago MH, Ricardo-Zapata A, Cárdenas L, et al. Telesimulación en la formación en medicina perioperatoria desde la perspectiva colombiana. Simulación Clínica. 2021; 3 (3): 110-116.

  30. Sawyer T, Eppich W, Brett-Fleegler M, Grant V, Cheng A. More than one way to debrief: a critical review of healthcare simulation debriefing methods. Simul Healthc. 2016; 11 (3): 209-217.

  31. Díaz-Guio DA, Vasco M, Ferrero F, Ricardo-Zapata A. Educación basada en simulación, una metodología activa de aprendizaje a través de experiencia y reflexión. Simulación Clínica. 2024; 6 (3): 119-126. Available from: https://www.medigraphic.com/cgi-bin/new/resumen.cgi?IDARTICULO=118838

  32. Simon R, Raemer DB RJ. Debriefing Assessment for Simulation in Healthcare (DASH)© - Student versión. Boston, Massachusetts: Center for Medical Simulation; 2010. Available from: https://harvardmedsim.org/wp-content/uploads/2017/01/DASH.SV.Short.2010.Final.pdf




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