L2M – An Adaptive Instructional System for Simulator-Based Ice Management Training

Our project addresses a critical gap in maritime training, specifically the challenge of developing effective ice management skills in extreme environments. Traditional training methods rely heavily on human instructors, whose expertise is both scarce and costly, limiting the scalability and consistency of training programs. This issue is especially significant in industries like oil and gas, where inadequate training can lead to severe safety risks and operational inefficiencies. The core problem lies in the reliance on human instructors, which introduces inconsistencies in training outcomes and limits the ability to deliver comprehensive, standardized training. If unresolved, this inconsistency could lead to catastrophic failures in real-world operations.
Our solution introduces an AI-driven Adaptive Instructional System (AIS) that automates training in a simulator- based environment, providing personalized feedback to trainees and ensuring consistently high competency levels. By addressing the root cause of reliance on human instructors, the AIS has the potential to improve safety, reduce training costs, and provide more uniform skill set among trainees.
One of the primary challenges we face is convincing the traditionally cautious maritime industry to adopt AI-based training systems, especially for safety-critical tasks like ice management. There may also be skepticism about replacing human instructors with an automated system. In addition, obtaining regulatory approval and proving the system’s effectiveness through pilot programs are key hurdles.
This project aims to overcome these challenges by conducting pilot programs that demonstrate the AIS’s effectiveness in improving training outcomes. Independent validation and regulatory approval will also be pursued in the long-term to build trust with potential customers. By clearly communicating the cost savings, safety improvements, and consistency of our system, we will address industry concerns and pave the way for wider adoption.

Faculty Supervisor:

Jennifer Smith

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Engineering

Sector:

Ocean Tech; Technology; Artificial Intelligence

University:

Memorial University of Newfoundland

Program: