A systematic review on human-AI hybrid systems and human factors in air traffic management

Artificial intelligence (AI) is poised to play a transformative role in supporting human air traffic controllers, enabling them to manage increasing traffic amidst growing capacity pressures. By enhancing their ability to manage increasing air traffic demands under growing capacity pressures, AI holds the potential to transform this safety-critical and human-dependent domain. To explore this potential, this systematic review compiles and synthesises studies on human-AI interactions in air traffic management (ATM), aiming to (1) examine the characteristics of human-AI hybrid (HAH) systems, (2) identify relevant human factors studies and their contributions, and (3) derive guidelines for future advancements and experimental designs. Of the 125 studies that met the inclusion criteria, two primary themes were identified: HAH systems and human factors. This review explores the use of HAH systems in ATM as enhancement tools across different operational levels, detailing their implementation through stages of conceptualisation, development, evaluation, and training. Additionally, it examines key human factor dimensions – such as workload, situation awareness, vigilance, decision-making, teamwork, communication, acceptance, and trust – by presenting relevant measurements, findings, and their implications for improving HAH and human-AI collaboration efficiency in ATM systems. This review offers suggestions for future advancements in HAH systems, ultimately contributing to safer and more efficient ATM.