Pilots operating modern cockpits often face high cognitive demands due to complex interfaces and multitasking requirements, which can lead to overload and decreased performance. This study introduces AdaptiveCoPilot, a neuroadaptive guidance system that adapts visual, auditory, and textual cues in real time based on the pilot’s cognitive workload, measured via functional Near-Infrared Spectroscopy (fNIRS). A formative study with expert pilots (N=3) identified adaptive rules for modality switching and information load adjustments during preflight tasks. These insights informed the design of AdaptiveCoPilot, which integrates cognitive state assessments, behavioral data, and adaptive strategies within a context-aware Large Language Model (LLM). The system was evaluated in a virtual reality (VR) simulated cockpit with licensed pilots (N=8), comparing its performance against baseline and random feedback conditions. The results indicate that the pilots using AdaptiveCoPilot exhibited higher rates of optimal cognitive load states on the facets of working memory and perception, along with reduced task completion times. Based on the formative study, experimental findings, qualitative interviews, we propose a set of strategies for future development of neuroadaptive pilot guidance systems and highlight the potential of neuroadaptive systems to enhance pilot performance and safety in aviation environments.

Recently, much work has been done to research personal identifiability of extended reality (XR) users. Many of these prior studies are task-specific and involve identifying users completing a specific XR task. On the other hand, some studies have been domainspecific and focus on identifying users completing different XR tasks from the same domain, such as watching 360° videos or assembling structures. In this paper, we present one of the few studies to investigate cross-domain identification (i.e., identifying users completing XR tasks from different domains). To facilitate our investigation, we used open-source datasets from two different virtual reality (VR) studies-one from an assembly domain and one from a gaming domain-to investigate the feasibility of cross-domain gender identification, as personal identification is not possible between these datasets. The results of our machine learning experiments clearly demonstrate that cross-domain gender identification is more difficult than domain-specific gender identification. Furthermore, our results indicate that head position is important for gender identification and demonstrate that the k-nearest neighbors (kNN) algorithm is not suitable for cross-domain gender identification, which future researchers should be aware of.