A Hybrid Approach to Training Expert Skills in Highly Automated Systems: Lessons from Air Traffic Management

In air traffic management (ATM), as in many other domains, automation is increasingly capable of performing more strategic and “cognitive” aspects of system performance. This paper sets out a potential hybrid approach to automation design, which assumes qualitatively different challenges at the introductory and mature phases of automation implementation. Whereas operator acceptance seems the critical issue at the time of automation introduction, skills development and maintenance seem most significant as expertise accrues. This proposed hybrid marries the notions of strategic conformance and adaptive automation to achieve a design approach in which, over the span of the skill acquisition cycle, automation is fitted to the novice, and the expert is fitted to the automation. Further, this approach assumes that training can (and indeed must) ultimately extend the training criterion from that of (heuristically based) expert operator performance, to that of (algorithmically based) optimized system performance.