The future of simulation‐based medical education: Adaptive simulation utilizing a deep multitask neural network
Open accessAbstract
Background: In resuscitation medicine, effectively managing cognitive load in high‐stakes environments has important implications for education and expertise development. There exists the potential to tailor educational experiences to an individual's cognitive processes via real‐time physiologic measurement of cognitive load in simulation environments. Objective: The goal of this research was to test a novel simulation platform that utilized artificial intelligence to deliver a medical simulation that was adaptable to a participant's measured cognitive load. Methods: The research was conducted in 2019. Two board‐certified emergency physicians and two medical students participated in a 10‐minute pilot trial of a novel simulation platform. The system utilized artificial intelligence algorithms to measure cognitive load in real time via electrocardiography and galvanic skin response. In turn, modulation of simulation difficulty, determined by a participant's cognitive load, was facilitated through symptom severity changes of an augmented reality (AR) patient. A postsimulation survey assessed the participants’ experience. Results: Participants completed a simulation that successfully measured cognitive load in real time through physiological signals. The simulation difficulty was adapted to the participant's cognitive load, which was reflected in changes in the AR patient's symptoms. Participants found the novel adaptive simulation platform to be valuable in supporting their learning. Conclusion: Our research team created a simulation platform that adapts to a participant's cognitive load in real‐time. The ability to customize a medical simulation to a participant's cognitive state has potential implications for the development of expertise in resuscitation medicine.
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Ruberto and colleagues tested an adaptive patient simulation utilizing a deep multitask neural network powered by AI; the AR patient's symptoms auto-adjusted to accommodate the student's cognitive load.
“Ruberto and colleagues tested an adaptive patient simulation utilizing a deep multitask neural network powered by AI [129]. The AR patient’s symptoms auto-adjusted to accommodate the student’s cognitive load.”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [129]
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- Authors
- Ruberto, A. J., Rodenburg, D., Ross, K., Sarkar, P., Hungler, P. C., Etemad, A., Howes, D., Clarke, D., McLellan, J., Wilson, D., & Szulewski, A.
- Affiliations
- Queen's University (CA)
- Department of Chemical Engineering Queen's University Kingston Ontario Canada
- Corresponding author
- Szulewski, Adam
- ORCID
- Venue
- AEM Education and Training
- Publisher
- Wiley
- Published
- 2021-05-26
- Volume
- 5
- Issue
- 3
- Pages
- e10605-e10605
- ISSN
- 2472-5390 (ISSN-L)
- PMC
- PMC8155693
- OpenAlex
- W3157420573
- Cited by
- 22
- References
- 27
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 5 · 2023: 4
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- AEM Education and Training (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Simulation-Based Education in Healthcare
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Health Sciences › Medicine › Emergency Medicine › Cardiac Arrest and Resuscitation
- Keywords
- adaptive simulation, cognitive load, resuscitation medicine, electrocardiogram, skin conductance, artificial intelligence in medical education, emergency medicine education, simulation-based medical education
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