Putting the ghost in the machine: exploring human-machine hybrid virtual patient systems for health professional education

Abstract

Background: Virtual patient authoring tools provide a simple means of creating rich and complex online cases for health professional students to explore. However, the responses available to the learner are usually predefined, which limits the utility of virtual patients, both in terms of replayability and adaptability. Using artificial intelligence or natural language processing is expensive and hard to design. This project description lays out an alternative approach to making virtual patients more adaptable and interactive. Methods: Using OpenLabyrinth, an open-source educational research platform, we modified the interface and functionality to provide a human-computer hybrid interface, where a human facilitator can interact with learners from within the online case scenario. Using a design-based research approach, we have iteratively improved our case designs, workflows and scripts and interface designs. The next step is to robustly test this new functionality in action. This report describes the piloting and background as well as the rationale, objectives, software development implications, learning designs, and educational intervention designs for the planned study. Results: The costs and time required to modify the software were much lower than anticipated. Facilitators have been able to handle text input from multiple concurrent learners. Learners were not discouraged waiting for the facilitator to respond. Discussion: The implementation and use of this new technique seems very promising and there are a great many ways in which it might be used for training and assessment purposes. This report also explores the provisional implications arising from the study so far.

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