Task Needs Assessment and Work Allocation Tools for Mission Operations and Procedures (Completed)
Last Published:  07/30/20 02:45:15 PM (Central)
Short Title: Work Allocation Tools
Responsible HRP Element: Space Human Factors and Habitability
Collaborating Org(s):
Funding Status: Completed - Task completed and produced a deliverable
Procurement Mechanism(s):
Directed
Aims:

Increased automation usage will be a prerequisite for long-term spaceflight. Identification of potential risks in complex, novel automation is critical to successful missions. Critical steps in the design of novel automation are: identification of mission needs, and validation that the developed technology meets those needs (i.e. is “fit for purpose”). The state-of-the-art of evaluation processes lack replicable, systematic methods for measuring a “good fit”. This task sets out to develop a method that will be used to represent and measure operator work needs, test input data for method test/validation, and then apply the method to a testbed developed in the first and second steps in an empirical data comparison effort.

 

  1. Develop a method (for use by the designers and evaluators of human-automation integration) to assess fitness-for-purpose of automation. We will propose the automation-to-work (ATW) structural alignment as the method for measuring how work and automation are aligned.
  2. Empirically test a human-automation system that has high ATW structural alignment, will support better performance, and particularly, is more easily learned and can be more flexibly applied in unfamiliar conditions.

 

Mitigation/Partial mitigation of the gap/risk: Application of such methods would reduce the risk of inadequate design of human/automation integration, and reduce the gap in guidelines and tools for appropriate task/needs analysis, and in how these are used to guide design and evaluation. With these tools and methods, NASA will better be able to implement HAI designs that are robust in supporting a variety of tasks across a variety of circumstances (some of which are difficult or impossible to anticipate). Further, the and methods will enable designs that support performance on tasks executed long after training, by operators who must be competent in many types of work rather than specialists in any one.





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