← Back to BMR — Volume 1 | Issue 1 | August 2026
Abstract
The global shipping industry is shifting away from calendar-based preventive maintenance toward artificial-intelligence-enabled predictive maintenance, a transition that depends on sensor-based condition monitoring, machine learning fault diagnosis, and digital twin technology, yet marine engineering curricula have been slower to adapt than the shipboard systems they prepare cadets to operate. This narrative review synthesizes peer-reviewed literature published between 2013 and 2025 on the pedagogical implications of this shift, asking how artificial intelligence (AI) and condition-monitoring content is, and should be, taught within marine engineering programs. Sources were identified through structured searches of Scopus- and Web of Science-indexed maritime and engineering-education journals, including the Journal of Marine Science and Engineering, the WMU Journal of Maritime Affairs, Maritime Policy and Management, the Australian Journal of Maritime and Ocean Affairs, Cogent Education, and Discover Education, together with Southeast Asian regional outlets; fifteen studies met inclusion criteria emphasizing peer review, topical relevance, and traceability. The synthesis shows convergence on three points: industry has already normalized predictive-maintenance technologies such as digital-twin-based fault prediction and machine-learning classifiers, achieving fault-detection accuracies as high as ninety-nine percent in engineering trials; maritime curricula, by contrast, remain organized around competency-based, procedural instruction that only partially incorporates data literacy and algorithmic reasoning; and where digitalization has been embedded, the strongest gains occur when institutions pair Industry 4.0 technology integration with competency-based curriculum redesign and sustained academia-industry collaboration rather than adding technology content in isolation. Contradictions emerge over readiness: Philippine and Southeast Asian evidence documents cadets who are procedurally competent with digital monitoring interfaces yet weak in diagnosing faults from the same data, exposing a gap between operating technology and reasoning with it. The review concludes that marine engineering education requires a deliberate pedagogical bridge, moving cadets from rule-based preventive schedules to data-interpretive, algorithmically literate diagnostic reasoning, and it proposes a Philippine-relevant curricular and faculty-development agenda to close this gap.