Artificial intelligence, applications and challenges in simulation-based education

  • Diego Andrés Díaz-Guio a. Simulation and Innovation, Faculty of Medicine, Universidad San Sebastián. Santiago, Chile. b. Educational Innovation, VitalCare Clinical Simulation Center, Armenia, Colombia.
  • Julián Henao a. Educational Innovation, VitalCare Clinical Simulation Center, Armenia, Colombia. b. School of Computer Engineering, Universidad del Valle. Cali, Colombia.
  • Andy Pantoja a. Educational Innovation, VitalCare Clinical Simulation Center, Armenia, Colombia. b. School of Mechatronic Engineering, Universidad EAM. Armenia, Colombia
  • María Alejandra Arango Educational Innovation, VitalCare Clinical Simulation Center, Armenia, Colombia.
  • Ana Sofía Díaz-Gómez Educational Innovation, VitalCare Clinical Simulation Center, Armenia, Colombia.
  • Aida Camps Gómez CISARC, Universidad de Manresa. Barcelona, España.
Keywords: Simulation-based education, ChatGPT, Artificial intelligence, Machine learning, Educational innovation


The rapid advancement of Artificial Intelligence (AI) has taken the world by “surprise” due to the lack of regulation over this technological innovation which, while promising application opportunities in different fields of knowledge, including education, simultaneously generates concern, rejection and even fear.

In the field of Health Sciences Education, clinical simulation has transformed educational practice; however, its formal insertion is still heterogeneous, and we are now facing a new technological revolution where AI has the potential to transform the way we conceive its application.


Cifuentes-Gaitán MJ, González-Rojas D, Ricardo-Zapata A, Díaz-Guio DA. Transferencia del aprendizaje de emergencias y cuidado crítico desde la simulación de alta fidelidad a la práctica clínica. Acta Colomb Cuid Intensivo. 2020;21(1):17-21. doi:

Cortegiani A, Russotto V, Montalto F, Iozzo P, Palmeri C, Raineri SM, et al. Effect of high-fidelity simulation on medical students’ knowledge about advanced life support: A randomized study. PLoS One. 2015;10(5):e0125685. doi:

Arora S, Hull L, Fitzpatrick M, Sevdalis N, Birnbach DJ. Crisis management on surgical wards. Ann Surg. 2015;261(5):1. doi:

Doumouras AG, Engels PT. Early crisis nontechnical skill teaching in residency leads to long-term skill retention and improved performance during crises: A prospective, nonrandomized controlled study. Surg (United States). 2017;162(1):174-81. doi:

Brydges R, Hatala R, Mylopoulos M. Examining residents’ strategic mindfulness during self-regulated learning of a simulated procedural skill. J Grad Med Educ. 2016;8(3):364-71. doi:

Russell E, Petrosoniak A, Caners K, Mastoras G, Szulewski A, Dakin C, et al. Simulation in the continuing professional development of academic emergency physicians. Simul Heal. 2020;00(00):1-8.

Forristal C, Russell E, McColl T, Petrosoniak A, Thoma B, Caners K, et al. Simulation in the continuing professional development of academic emergency physicians. Simul Healthc J Soc Simul Healthc. 2020;Publish Ah(00):1-8. doi:

Nestel D, Bearman M. Theory and simulation-based education: Definitions, worldviews and applications. Clin Simul Nurs. 2015;11(8):349-54. doi:

Ferrero F, Díaz-Guio DA. Educación basada en simulación: polemizando bases teóricas de la formación docente. Simulación Clínica. 2021;3(1):35-9. doi:

Ferguson J, Astbury J, Willis S, Silverthorne J, Schafheutle E. Implementing, embedding and sustaining simulation-based education: What helps, what hinders. Med Educ. 2020;54(10):915-–24. doi:

Díaz-Guio DA, Ríos-Barrientos E, Santillán-Roldan PA, Díaz-Gómez AS, Ricardo-Zapata A, Mora-Martinez S, et al. Online-synchronized clinical simulation: an efficient teaching-learning option for the COVID-19 pandemic time and beyond. Adv Simul. 2021;6:30. doi:

Sherwood RJ, Francis G. The effect of mannequin fidelity on the achievement of learning outcomes for nursing, midwifery and allied healthcare practitioners: Systematic review and meta-analysis. Nurse Educ Today. 2018;69:81-94. doi:

Ouyang F, Jiao P. Artificial intelligence in education: The three paradigms. Comput Educ Artif Intell. 2021;2(April):100020. doi:

Haenlein M, Kaplan A. A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. Calif Manage Rev. 2019;61(4):5-14. doi:

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):259-65. doi:

Dwivedi YK, Kshetri N, Hughes L, Slade EL, Jeyaraj A, Kar AK, et al. “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int J Inf Manage. 2023;71(March). doi:

Khanna A, Pandey B, Vashishta K, Kalia K, Pradeepkumar B, Das T. A Study of Today’s A.I. through chatbots and rediscovery of machine intelligence. Int J Service, Sci Technol. 2015;8(7):277-84. doi:

Adamopoulou E, Moussiades L. An overview of chatbot technology [Internet]. Vol. 584 IFIP, IFIP Advances in Information and Communication Technology. Springer International Publishing; 2020. Pp. 373-83. Disponible en:

Alser M, Waisberg E. Concerns with the usage of ChatGPT in Academia and Medicine: A viewpoint. Am J Med Open. 2023;100036. doi:

Halaweh M. ChatGPT in education: Strategies for responsible implementation. Contemp Educ Technol. 2023;15(2):ep421. doi:

Moldt JA, Festl-Wietek T, Madany Mamlouk A, Nieselt K, Fuhl W, Herrmann-Werner A. Chatbots for future docs: exploring medical students’ attitudes and knowledge towards artificial intelligence and medical chatbots. Med Educ Online. 2023;28(1). doi:

Lim WM, Gunasekara A, Pallant JL, Pallant JI, Pechenkina E. Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. Int J Manag Educ. 2023;21(2):1-13. doi:

Wang J, Zhu H, Wang SH, Zhang YD. A Review of deep learning on medical image analysis. Mob Networks Appl. 2021;26(1):351-80. doi:

Jayatilake SMDAC, Ganegoda GU. Involvement of machine learning tools in healthcare decision making. J Healthc Eng. 2021;2021. doi:

Time SR, Matching SS. Encyclopedia of the sciences of learning. Encyclopedia of the Sciences of Learning. 2012.

Young T, Hazarika D, Poria S, Cambria E. Recent trends in deep learning based natural language processing [Review Article]. IEEE Comput Intell Mag. 2018;13(3):55-75. doi:

Lee J, Wu AS, Li D, Kulasegaram KM. Artificial intelligence in undergraduate medical education: A scoping review. Acad Med. 2021;96(11):S62-70. doi:

Civaner MM, Uncu Y, Bulut F, Chalil EG, Tatli A. Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Med Educ. 2022;22(1):1-9. doi:

Ossa LA, Rost M, Lorenzini G, Shaw DM, Elger BS. A smarter perspective: Learning with and from AI-cases. Artif Intell Med. 2023;135(October 2021):102458. doi:

Monereo C, Pozo J. En qué siglo vive la escuela? Cuad Pedagog. 2001;298(January 2001):50-5.

Dieckmann P, Gaba D, Rall M. Deepening the theoretical foundations of patient simulation as social practice. Simul Healthc. 2007;2(3):183-93. doi:

Mcgriff SJ. Instructional System Design (ISD): Using the ADDIE Model. Instr Syst Coll Educ Penn State Univ [Internet]. 2000;2. Disponible en:

Díaz-Guio DA, del Moral I, Maestre JM. Do we want intensivists to be competent or excellent? Clinical simulation-based mastery learning. Acta Colomb Cuid Intensivo. 2015;15(3):187-95. doi:

Ericsson KA. Deliberate practice and the acquisition and maintenance of expert performance in medicine and related domains. Acad Med. 2004;79(10 Suppl):S70-81. doi:

Barsuk JH, Cohen ER, Wayne DB, Siddal VJ, McGaghie W. Developing a simulation-based mastery learning curriculum: Lessons from 11 years of advanced cardiac life support. Simul Heal. 2016;11(1):52-9. doi:

Ledwos N, Mirchi N, Yilmaz R, Winkler-schwartz A, Sawni A, Fazlollahi AM, et al. Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence. J Neurosurg. 2022;137:1160-71. doi:

Sawyer T, Eppich W, Brett-Fleegler M, Grant V, Cheng A. More than one way to debrief. Simul Healthc. 2016;11(3):209-17. doi:

Roussin C, Sawyer T, Weinstock P. Assessing competency using simulation: The SimZones approach. BMJ Simul Technol Enhanc Learn. 2020;6(5):262-7. doi:

Díaz-Guio D, Cimadevilla-Calvo B. Educación basada en simulación: Debriefing, sus fundamentos, bondades y dificultades. Revista Latinoamericana de Simulación Clínica. 2019;1:95-103. doi:

Díaz-Guio DA, Ruiz-Ortega FJ. Relationship among mental models , theories of change , and metacognition: structured clinical simulation. Colombian Journal of Anesthesiology. 2019;47(14):113-6. doi:

Fengchun M, Wayne H, Huang R, Zhang H. AI and education Guidance for policymakers [Internet]. 2021. Avaiable at:

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. doi:

OPS. Inteligencia artificial, 8 Principios rectores de la transformación digital del sector salud Caja de herramientas de transformación digital [Internet]. Inteligencia artificial. 2023. Disponible en:

How to Cite
Díaz-Guio DA, Henao J, Pantoja A, Arango MA, Díaz-Gómez AS, Camps Gómez A. Artificial intelligence, applications and challenges in simulation-based education. Colomb. J. Anesthesiol. [Internet]. 2023 Sep. 5 [cited 2024 May 25];52(1). Available from:


Download data is not yet available.
How to Cite
Díaz-Guio DA, Henao J, Pantoja A, Arango MA, Díaz-Gómez AS, Camps Gómez A. Artificial intelligence, applications and challenges in simulation-based education. Colomb. J. Anesthesiol. [Internet]. 2023 Sep. 5 [cited 2024 May 25];52(1). Available from:
Narrative review


Article metrics
Abstract views
Galley vies
PDF Views
HTML views
Other views
QR Code