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.

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.