The future of simulation-based medical education: Adaptive simulation utilizing a deep multitask neural network.

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.

Toward Dynamically Adaptive Simulation: Multimodal Classification of User Expertise Using Wearable Devices.

Simulation-based training has been proven to be a highly effective pedagogical strategy. However, misalignment between the participant's level of expertise and the difficulty of the simulation has been shown to have significant negative impact on learning outcomes. To ensure that learning outcomes are achieved, we propose a novel framework for adaptive simulation with the goal of identifying the level of expertise of the learner, and dynamically modulating the simulation complexity to match the learner's capability. To facilitate the development of this framework, we investigate the classification of expertise using biological signals monitored through wearable sensors. Trauma simulations were developed in which electrocardiogram (ECG) and galvanic skin response (GSR) signals of both novice and expert trauma responders were collected. These signals were then utilized to classify the responders' expertise, successive to feature extraction and selection, using a number of machine learning methods. The results show the feasibility of utilizing these bio-signals for multimodal expertise classification to be used in adaptive simulation applications.

Maintaining the Characteristics of Effective Clinical Teachers in Computer Assisted Learning Environments.

There has been a growing trend in medical education to integrate the use of computers into the undergraduate medical curriculum. While it seems intuitively obvious that personal computers and the Internet can be useful learning tools, it is not clear that the perceived advantages of Computer Assisted Instruction (CAI) are warranted. One problem is that computers are too often used in CAI simply as presentation devices for predefined material without ample consideration paid to the pedagogical principles that have informed more conventional teaching practices. The creation of an environment that is conducive to effective learning has often been overlooked in favour of the development and use of increasingly more sophisticated technologies. The current paper represents an attempt to delineate ways in which we might better develop instructional multimedia programs by employing some of the strategies believed to characterise effective clinical teaching. To do so, this paper will briefly review the work of Irby and others in an attempt to draw attention to ways in which the characteristics identified by these researchers might be implemented for the use of CAI.