Toward personalised maritime training : seafarer perceptions of AI-based and traditional feedback

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Tornaci, Furkan and Pekdaş, İpek Gölbol and Turan, Osman and Kurt, Rafet and White, Martin and Love, Tim (2026) Toward personalised maritime training : seafarer perceptions of AI-based and traditional feedback. Journal of Marine Engineering & Technology. pp. 1-12. ISSN 2056-8487 (https://doi.org/10.1080/20464177.2026.2675418)

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Abstract

As maritime operations become increasingly dynamic and safety-critical, supporting effective learning and decision-making remains a central challenge in maritime education. Within such training contexts, feedback plays a central role in helping seafarers reflect on their weaknesses. Building on this, this study examines seafarers' perceptions of artificial intelligence (AI)-based feedback in maritime training by comparing three feedback formats following a COLREGs assessment: traditional feedback, AI-generated global performance summaries, and AI-generated personalised feedback at the question level. Forty-nine seafarers evaluated each feedback format across six instructional dimensions: correctness, sufficiency, usefulness, clarity, adaptiveness, and motivational impact.Statistical analysis confirmed that both AI-based feedback approaches significantly outperformed the traditional method by providing more relevant guidance, better addressing individual learning needs, and enhancing the overall instructional value. Participants expressed a clear preference for AI-generated feedback tailored to individual questions, which they described as more motivating, clearer, more informative, and more learner-centred. These findings highlight the potential of AI to deliver scalable, faster, and more cost-efficient feedback to support engagement and learning in maritime training.

ORCID iDs

Tornaci, Furkan, Pekdaş, İpek Gölbol, Turan, Osman ORCID logoORCID: https://orcid.org/0000-0003-1877-8462, Kurt, Rafet ORCID logoORCID: https://orcid.org/0000-0002-5923-0703, White, Martin and Love, Tim;
CORE (COnnecting REpositories)