Adapting Maritime Education for the Autonomous Era: A Pilot Program as Approach for MASS Operator Training
Bibliographic record
Abstract
The technological advancements leading to fully autonomous transportation systems are already shaping the fleets of the future. Contrary to expectations, artificial intelligence and autonomous systems are increasing the demand for highly skilled crews and operators. Based on the EMSA report and a new non-mandatory International Code for Safety for Maritime Autonomous Surface Ships (MASS Code), it is justified that MASS operators require STCW training as a baseline. These findings underscore the pressing need for Maritime Education and Training Institutions (METIs) to work diligently to promptly update their curricula. As part of a coordinated initiative to incorporate MASS into MET, four European METIs have collaborated to develop a new Blended Intensive Programme. This paper introduces the aforementioned course implemented as a pilot program in the second semester of the 2024-2025 academic year. Findings can guide the development of future curriculum, support the standardization of training programs across METIs, and help to establish international recommendations for maritime education in the era of autonomous systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".