By Capt. Abhinandan Prasad MNILecturer – SUNY Maritime College, New York In recent years, breakthroughs in computing—ranging from artificial intelligence to adaptive learning systems—have demonstrated how algorithms ca...
By Capt. Abhinandan Prasad MNI
Lecturer – SUNY Maritime College, New York
In recent years, breakthroughs in computing—ranging from artificial intelligence to adaptive learning systems—have demonstrated how algorithms can change our work and, more importantly, our learning processes. In maritime education, where practical skills are as vital as theoretical knowledge, the ability of algorithms to enhance simulator training is particularly attractive.
Bridge Resource Management (BRM) courses, which follow STCW guidelines and the IMO Model Course, aim to build skills such as clear communication and effective teamwork. The learning outcomes are already clearly defined. Therefore, the “output” of a training session is known; the real challenge is creating simulation scenarios that effectively guide students toward those goals.
Modern bridge simulators offer instructors significant flexibility: they can change visibility, weather conditions, currents, vessel traffic, time of day, and even marine life. However, this wide range of options can sometimes be overwhelming. What is often lacking is a structured and smart method to generate scenarios that directly relate to training objectives. This is where algorithms could be very useful. Picture a system where an instructor inputs the class profile for a BRM course—specifying experience and background—and the simulator automatically creates a scenario that aligns with STCW learning goals. The instructor would then be able to review and modify the generated scenario, combining human insight with algorithmic efficiency. Currently, such a tool is not available on the market.
The IMO Model Course for BRM gives clear guidance on what a scenario should include, covering navigation challenges like shallow water and bank effects, and emergencies like engine or rudder failures. Turning these into the “building blocks” for an algorithm is technically possible, and simulator manufacturers could start by offering basic templates for each licensed area. These could be based on common traffic scenarios, with additional options for environmental factors such as wind, currents, or low visibility. Importantly, the assessor's role would remain the same: observing student performance and conducting essential debriefings. Algorithms would not replace instructors, but rather assist them in focusing on teaching instead of spending time creating scenarios from scratch.
Some companies are already testing AI in simulators, but their emphasis is often on automation or regulatory compliance rather than variety in scenarios. Taking small steps, like implementing template-based generation, would be a practical way forward, allowing the maritime industry to utilize algorithms without needing to overhaul existing systems.
If maritime education wants to proactively prepare officers for the future, it must adopt the tools of that future. Carefully applied algorithms can transform bridge simulators from customizable platforms into intelligent learning environments—ensuring that the next generation of officers is not only technically skilled but also adept at teamwork, by exposing them to optimal conditions using the latest advances in computing technology.