TAEP - Education and Training

This study examines the digital information literacy and the impact of digital tools on maritime studies in Greek universities, addressing gaps in the literature regarding their information behavior and barriers. A survey of 334 students was conducted using a structured questionnaire. The analysis highlights that students prioritize the perceived impact of different AI tools for academic success and career development. The analysis showed that maritime students recognize the need of digital competences in completing their studies and the great impact on their profession. Furthermore, digital seminars compliant with European Union's DigComp framework are recommended to enhance digital competences, aligning with industry 4.0 demands. This research contributes to the understanding of the intersection of digital literacy and maritime education, emphasizing on the need of specific interventions to equip students with advanced competences for a digitally transforming maritime sector to ensure students remain competitive by using AI tools in proficiency level and adaptable in their professional roles in the future. Simultaneously, this study examines the contribution of the use of AI tools in maritime information, according to maritime students' perceptions.

The maritime sector is undergoing an important transformation driven by disruptive technologies and the drive of shipping decarbonization. Maritime Education and Training (MET) must evolve to prepare seafarers for these global emerging demands, ensuring that education becomes as vital as technological advancement itself. This study investigates how disruptive technologies can be adapted within MET frameworks to foster environmental sustainability, focusing on the Latin American maritime sector. Specifically, we focus on the opportunities, advantages, shortcomings, and constraints of integrating disruptive technologies into MET frameworks and operations. A mixed-methods approach was employed, comprising an extensive literature review, surveys, case studies, focus groups, simulator-based assessments, and Human Reliability and Multi-criteria decision-making analyses. Primary results indicate a considerable mismatch between existing training programs and the competencies required for sustainable maritime operations and a decarbonized shipping industry. The competency gap analysis underscored the urgent need for curriculum reforms aligned with emerging industry demands and technology advancements. We interpret that without immediate modernization of MET curricula and enhanced collaboration between academia, industry, and policymakers, the maritime sector risks insufficiently preparing its workforce for a low-carbon and sustainable future. Targeted investment in continuous learning, the integration of advanced technologies, standardized certifications, and international cooperation are vital strategies for building a future-ready maritime workforce capable of supporting the global goals for decarbonization and sustainable development.

The maritime industry is undergoing rapid technological transformation with the emergence of Maritime Autonomous Surface Ships (MASS). While these advancements promise enhanced efficiency, safety, and environmental sustainability, they also present significant challenges, particularly concerning job security, workforce adaptation, and regulatory alignment. This study investigates the perceptions of South African ship officers towards MASS and explores strategies for their adaptation. Grounded in Socio-Technical Systems (STS) theory, the study employs a Sequential Explanatory Mixed-Methods Design, combining quantitative surveys with qualitative interviews to capture both broad trends and in-depth insights. The research aims to assess concerns regarding job displacement, identify skill requirements for autonomous operations, and propose workforce transition strategies. Due to systemic challenges in South Africa's maritime sector, including limited training infrastructure and regulatory gaps, the study seeks to inform policy development, training programs, and industry-academic collaborations. While data collection is pending ethics clearance, preliminary insights from the literature suggest a pressing need for reskilling initiatives and regulatory adaptation. The findings will contribute to a more sustainable and inclusive transition to autonomous shipping, ensuring that technological advancements complement rather than displace the maritime workforce.

This study utilizes the k-Nearest Neighbors (KNN) algorithm to classify student performance in a maritime simulation training exercise based on simulator log data. Log data of 10 features were used in training the model. Due to the unique data structure of the training data, a new correlation-based KNN classifier with k=3 and Hamming distance is proposed for prediction. The approach achieved classification accuracy of about 84% in the training sample. This result highlights the potential of KNN in educational data analytics to classify student performance, providing insights for personalized learning and developing learning analytics dashboards. Moreover, we also explored K-Means clustering for unsupervised analysis, achieving a silhouette score that suggests meaningful cluster separation.

Although still in the prototype stage, the emergence of Maritime Autonomous Surface Ships (MASS) has garnered significant interest from scholars in recent years. Research highlights the potential of MASS to address several longstanding challenges in the maritime industry, including labor shortages, operational inefficiencies, and safety risks. However, their development and deployment raise complex questions about the implications for the workforce, particularly seafarers, encompassing both officers and ratings. This literature review examines the evolution of unmanned vessels from the perspective of seafarers, focusing on vessel-terminal operations and the broader spatial and economic contexts. By centering on human factors-especially the division of labor between officers and ratings-this study contextualizes the discussion within larger trends in freight transport automation and its far-reaching implications for the future of work.