Usage of Algorithms in Bridge Simulators to Enhance Learning
By Capt. Abhinandan Prasad MNI
Lecturer – SUNY Maritime College, New York
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 towards 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.