Posted on May 11, 2026
By Capt. Abhinandan Prasad MNI
In recent years, advances in computing — from artificial intelligence to adaptive learning systems — have shown how algorithms can transform the way we work and, more importantly, the way we learn. In maritime education, where hands-on practice is just as important as conceptual classroom theory, the potential for algorithms to refine simulator-based training is exceptionally appealing.
Bridge Resource Management (BRM) courses, guided by STCW requirements and the IMO Model Course for the same, aim to develop competencies ranging from clear communication to effective teamwork. The learning outcomes are already well defined. In that sense, the “output” of a training exercise is known; the challenge lies in designing simulation scenarios that efficiently guide students toward achieving it.
Modern bridge simulators provide instructors with enormous flexibility: visibility, weather, currents, vessel traffic, time of day, and even marine life can all be manipulated. Yet this abundance of choice can also be overwhelming. What is often missing is a structured and intelligent way to generate scenarios that directly map themselves to training objectives.
Here, algorithms could play a valuable role. Imagine a system where an instructor inputs the class profile for a BRM course, chiefly in terms of experience and background, and the simulator automatically designs a scenario aligned with the STCW learning objectives. The instructor could then review and adjust the generated scenario, combining human judgment with algorithmic efficiency. At present, no such tool is commercially available.
The IMO Model Course for BRM provides clear guidance on what a scenario should contain, from navigation phenomena such as shallow water and bank effect to emergencies like engine or rudder failure. Translating these into the “building blocks” or inputs of an algorithm is technically feasible, and simulator manufacturers could begin by offering basic templates within each licensed area.
These could be designed around common traffic situations, with layered options for environmental factors such as wind, current, or restricted visibility. Crucially, the role of the assessor would remain unchanged: observing student performance and conducting the all-important debriefing based on the same. Algorithms would not replace instructors but rather help them focus on pedagogy instead of spending valuable time assembling scenarios from scratch.
Some companies are already experimenting with AI in simulators, but their focus tends to be on automation or regulatory compliance rather than scenario diversity. Incremental steps such as template-based generation would be a practical way forward, allowing the maritime industry to begin leveraging algorithms without overhauling existing systems.
If maritime education is to evolve toward being proactive in preparing officers for the future, it must embrace the tools of that future. Algorithms, carefully applied, can help bridge simulators grow from being customizable platforms into intelligent learning environments — ensuring that the next generation of officers are not only technically competent, but that their ability to work as a team has been developed by exposing them to the most optimum conditions using the latest advances in computing technology.
