Virtual Reality Training for “Safety-Critical” Jobs

Virtual Reality Training for “Safety-Critical” Jobs

Published By: University of Exeter, 3/6/2017

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Summary

A virtual-reality training system currently in development by Exeter-based researchers and experts from the nuclear power industry could help prevent accidents in high-risk jobs. Employees using the system could gain experience with high-stress tasks in a safe environment, while employers could use the eye tracking and physiological sensor data to better understand how workers learn and how they react under pressure.

Flesch-Kincaid Grade Level of Article: 13.8

Extended Discussion Questions

  • Would you feel safer knowing that workers in your power plant were trained in virtual reality first, before they trained on the physical equipment? Why or why not?
  • How does combining virtual reality training with biological monitoring of the trainee benefit workers in high-risk jobs?
    • How does it benefit the employer?
    • How does it benefit the customer, or society in general?
  • Are there ways this combination could backfire and cause problems for the employees?
    • Could it cause problems for the employers?
    • Could it cause problems for anyone else?
  • The article discusses the use of this approach in nuclear power as well as surgery, aviation, and the military. What are some other potentially dangerousĀ fields that you think could benefit from this type of training?

Relating This Story to the CSP Curriculum Framework

Global Impact Learning Objectives:

  • LO 7.1.1 Explain how computing innovations affect communication, interaction, and cognition.
  • LO 7.2.1 Explain how computing has impacted innovations in other fields.

Global Impact Essential Knowledge:

  • EK 7.1.1J Sensor networks facilitate new ways of interacting with the environment and with physical systems.
  • EK 7.2.1G Advances in computing as an enabling technology have generated and increased the creativity in other fields.

Banner Image:Network Visualization – Violet – Offset Crop“, derivative work by ICSI. New license: CC BY-SA 4.0. Based on “Social Network Analysis Visualization” by Martin Grandjean. Original license: CC BY-SA 3.0.

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