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Explainable artificial intelligence in air traffic control: effects of expertise on workload, acceptance, and usage
Giulia Cartocci1,2, Alexandre Veyrié3, Nicola Cavagnetto4
1Departmental Faculty of Medicine and Surgery, Saint Camillus University of Health Sciences, 00131, Rome, Italy. giulia.cartocci@unicamillis.org.
Brain Informatics
|January 24, 2026
Summary
Visual explanations from Explainable AI (XAI) reduce cognitive workload and increase AI adoption for Air Traffic Control Officers (ATCOs). This trust-building approach benefits both expert and student ATCOs in critical Air Traffic Management (ATM) systems.
Area of Science:
- Human-Computer Interaction
- Artificial Intelligence
- Cognitive Science
Background:
- Explainable AI (XAI) is vital for trust in AI, especially in safety-critical domains like Air Traffic Management (ATM).
- Limited empirical data exists on visual explanations' impact on human-AI interaction in Air Traffic Control (ATC).
Purpose of the Study:
- To empirically investigate the effects of HeatMap-based visual explanations (XAI) on cognitive workload, user acceptance, and AI system adoption among Air Traffic Control Officers (ATCOs).
- To compare explainable (HeatMap) versus non-explainable (BlackBox) AI conditions in an ATC context.
Main Methods:
- Experimental comparison between explainable and non-explainable AI conditions.
- Inclusion of both expert and student Air Traffic Control Officers (ATCOs).
- Utilized objective neurophysiological measures (Electroencephalography) and subjective questionnaires.
Main Results:
- Visual explanations significantly reduced cognitive workload and increased willingness to adopt AI systems across all expertise levels.
- Perceived AI impact on work performance was higher among less experienced controllers compared to experts.
- Combined objective and subjective measures advanced evaluation rigor for human-AI interactions.
Conclusions:
- User-centric visual explanations enhance trust and adoption of AI decision-support systems in ATC.
- Tailored explanations and expertise-based training/deployment strategies are crucial for effective AI integration in safety-critical fields.
- Findings provide practical guidelines for designing cognitively compatible and trustworthy AI tools for Air Traffic Control.
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