Enhancing Decision-Making in Single-Pilot Operations Through Proactive and Explainable Human-Autonomy Teaming

Single-Pilot Operations (SPO) in commercial aviation raise critical safety challenges in complex, time-pressured situations where human expertise is paramount. This study investigates how AI-based Human-Autonomy Teaming (HAT) can support pilot decision-making under high workload, compromised situational awareness, and time pressure. In a high-fidelity Airbus A320 simulator, 22 airline pilots performed a fuel- and time-constrained approach scenario designed to challenge HAT’s operational effectiveness under four conditions: no assistance, on-demand AI, proactive AI, and proactive explainable AI. Workload (NASA-TLX) remained high across conditions, yet decision-making differed markedly. Without assistance, only 20% chose the safest landing option; with on-demand AI, 40%; and with proactive AI (both versions), 100%. Explainable AI yielded the highest scenario comprehension (75%), matching two-crew baseline performance. Proactive systems increased decision and SA related interactions, fostering a collaborative dynamic akin to traditional crew resource management. Thus, the results demonstrate that, in the context of SPO, proactive and explainable AI was associated with crew-level decision performance within the experimental scenario without adding cognitive load. Beyond the aeronautical domain, our results are of interest for the design of cognitive support systems for high-risk domains where teams composed of humans and AI must adapt in real time.