Usage of Algorithms in Bridge Simulators to Enhance Learning<\/p>\n
By Capt. Abhinandan Prasad MNI<\/p>\n
Lecturer \u2013 SUNY Maritime College, New York<\/p>\n
In recent years, advances in computing \u2014 from artificial intelligence to adaptive learning<\/p>\n
systems \u2014 have shown how algorithms can transform the way we work and more<\/p>\n
importantly, the way we learn. In maritime education, where hands-on practice is just as<\/p>\n
important as conceptual classroom theory, the potential for algorithms to refine simulator-<\/p>\n
based training is exceptionally appealing.<\/p>\n
Bridge Resource Management (BRM) courses, guided by STCW requirements and the IMO<\/p>\n
Model Course for the same, aim to develop competencies ranging from clear communication<\/p>\n
to effective teamwork. The learning outcomes are already well defined. In that sense, the<\/p>\n
\u201coutput\u201d of a training exercise is known; the challenge lies in designing simulation scenarios<\/p>\n
that efficiently guide students toward achieving it.<\/p>\n
Modern bridge simulators provide instructors with enormous flexibility: visibility, weather,<\/p>\n
currents, vessel traffic, time of day, and even marine life can all be manipulated. Yet this<\/p>\n
abundance of choice can also be overwhelming. What is often missing is a structured and<\/p>\n
intelligent way to generate scenarios that directly map themselves to training objectives.<\/p>\n
Here, algorithms could play a valuable role. Imagine a system where an instructor inputs the<\/p>\n
class profile for a BRM course, chiefly in terms of experience and background, and the<\/p>\n
simulator automatically designs a scenario aligned with the STCW learning objectives. The<\/p>\n
instructor could then review and adjust the generated scenario, combining human judgment<\/p>\n
with algorithmic efficiency. At present, no such tool is commercially available.<\/p>\n
The IMO Model Course for BRM provides clear guidance on what a scenario should contain,<\/p>\n
from navigation phenomena such as shallow water and bank effect to emergencies like<\/p>\n
engine or rudder failure. Translating these into the \u201cbuilding blocks\u201d or inputs of an algorithm<\/p>\n
is technically feasible, and simulator manufacturers could begin by offering basic templates<\/p>\n
within each licensed area. These could be designed around common traffic situations, with<\/p>\n
layered options for environmental factors such as wind, current, or restricted visibility.<\/p>\n
Crucially, the role of the assessor would remain unchanged: observing student performance<\/p>\n
and conducting the all-important debriefing based on the same. Algorithms would not<\/p>\n
replace instructors but rather help them focus on pedagogy instead of spending valuable<\/p>\n
time assembling scenarios from scratch.<\/p>\n
Some companies are already experimenting with AI in simulators, but their focus tends to be<\/p>\n
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.<\/p>\n
If maritime education is to evolve towards being proactive in preparing officers for the future,<\/p>\n
it must embrace the tools of that future. Algorithms, carefully applied, can help bridge<\/p>\n
simulators grow from being customizable platforms into intelligent learning environments \u2014<\/p>\n
ensuring that the next generation of officers are not only technically competent, but that their<\/p>\n
ability to work as a team has been developed by exposing them to the most optimum<\/p>\n
conditions using the latest advances in computing technology.<\/p>\n