Bracketing human performance to support automation for workload reduction: A case study

Abstract

Semi-automated Forces (SAFs) are commonly used in training simulation. SAFs often require human intervention to ensure that appropriate, individual training opportunities are presented to trainees. We cast this situation as a supervisory control challenge and are developing automation designed to support human operators, reduce workload, and improve training outcomes. This paper summarizes a combined analytic and empirical verification study that identified specific situations in the overall space of possible scenarios where automation may be particularly helpful. By bracketing “high performance” and “low performance” conditions, this method illuminates salient points in the space of operational performance for future human-in-the-loop studies.