As autonomous technologies increasingly shape maritime operations, understanding why an AI system makes a decision becomes as crucial as what it decides. In complex and dynamic maritime environments, trust in AI depends not only on performance but also on transparency and interpretability. This paper highlights the importance of Explainable AI (XAI) as a foundation for effective human-machine teaming in the maritime domain, where informed oversight and shared understanding are essential. To support the user-centered integration of XAI, we propose a domain-specific survey designed to capture maritime professionals' perceptions of trust, usability, and explainability. Our aim is to foster awareness and guide the development of user-centric XAI systems tailored to the needs of seafarers and maritime teams.

Research and development of Maritime Autonomous Surface Ships (MASS) has surged in recent years. However, questions remain regarding the safety of interactions between MASS and conventionally crewed ships in mixed-traffic environments, particularly during collision avoidance. Although the critical role of MASS technology is widely recognised, few studies have explored how these interactions may affect the behaviour of the conventional ship’s Officer of the Watch (OOW). The primary aim of this paper is to explore the challenges and complexities of mixed-traffic interaction from the perspective of the conventional OOW as an active participant in collision avoidance. A collision-avoidance framework is presented as an analytical lens linking situational awareness, motion prediction, mental models, conflict detection, conflict resolution, and manoeuvre execution. This framework is used to examine challenges related to communication, COLREG interpretation, uncertainty surrounding MASS capabilities and operations, knowledge and training, and trust. We argue that these factors may influence how OOWs interpret MASS behaviour and determine whether, when, and how to intervene. To address this research gap, key research questions are formulated, and a scenario-driven methodological approach using Full Mission Bridge Simulators (FMBS) is proposed to investigate changes in OOW behaviour and decision-making during mixed-traffic encounters.