The use of artificial intelligence (AI) in the flight deck: Enhancing human-AI teamwork in aviation
Artificial intelligence (AI) is becoming increasingly integrated into aviation, transforming numerous operations, including those performed at the flight deck. This paper explores theoretical approaches to optimizing human-AI teamwork in the context of aviation, such as trust and role clarity, to enhance safety and efficiency. It also outlines strategies for effective task allocation in human-AI teams using a previously developed conceptual model. By applying teamwork and cognitive principles, such as situation awareness, it examines the complementary strengths of humans and AI, and addresses how AI can serve both as a tool and as a collaborative teammate in aviation contexts. This paper evaluates human strengths, such as adaptive decision-making and AI capabilities, including real-time data processing, alongside shared limitations like fatigue and inflexibility. It discusses the risks of over-reliance on AI, reduced situational awareness, and cybersecurity threats. Best practices for fostering trust, clear roles, and interdependence are presented, drawing from Crew Resource Management (CRM) principles. This work extends human factors research by applying a novel theoretical framework to human-AI collaboration in aviation. Unlike prior studies focused solely on technological advances, it provides actionable insights for task allocation, risk mitigation, and training, supporting balanced and effective human-AI teams in the flight deck.
Authors
- Lila Berger (ORCID: https://orcid.org/0009-0002-8852-5129)
- Jenna Korentsides (ORCID: https://orcid.org/0000-0002-9833-7036)
- Joseph Roland Keebler (ORCID: https://orcid.org/0000-0003-2246-7472)
- Briana M. Sobel (ORCID: https://orcid.org/0000-0003-2622-3926)
- Scott R. Winter
- Lana Laskey
- Elizabeth R. Merwin (ORCID: https://orcid.org/0009-0007-1556-190X)
Publication Details
- Journal
- Journal of the Air Transport Research Society
- Published
- 2025-12-22
- DOI
- https://doi.org/10.1016/j.jatrs.2025.100099
- Primary Topic
- Human-Automation Interaction and Safety
- Type
- article
- Field-Weighted Citation Impact
- 0.00