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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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 dangerous¬†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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