@article{Baradziej_2025,
	author = {Baradziej, Simon},
	title = {LLM-based Maritime Training Feedback System: Implementing RAG-Enhanced Assessment Analysis with STCW Compliance},
	journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation},
	volume = {19},
	number = {3},
	pages = {831-838},
	year = {2025},
	url = {./Article_LLM-based_Maritime_Training_Feedback_Baradziej,75,1566.html},
	abstract = {This paper presents the implementation and evaluation of a Retrieval-Augmented Generation (RAG) system designed to provide automatic STCW- compliant feedback on maritime assessment questions. Building on preliminary findings from ongoing research into technological proficiency [1] (β=0.457) and institutional readiness [2] (β=0.341), this implementation addresses a critical gap: the need for automated feedback systems that maintain regulatory alignment while reducing instructor workload. The system utilizes the Mistral-7B large language model optimized with QLoRA for efficient local deployment, combined with a RAG architecture to ensure contextually relevant feedback. Evaluation results demonstrate the system’s ability to generate accurate feedback with response times under 15 seconds and STCW concept coverage of 85%, addressing key implementation barriers identified in our previous studies. The paper discusses how this implementation addresses technological proficiency barriers (β=0.457, p<0.001) and enhances perceived usefulness through automated, standards-compliant feedback that supports both individual competency development and institutional readiness.},
	doi = {10.12716/1001.19.03.16},
	issn = {2083-6473},
	publisher = {Gdynia Maritime University, Faculty of Navigation},
	keywords = {Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), STCW-Compliant Feedback, Automated Assessment in MET, Model Optimization, Regulatory Alignment, Feedback Generation Performance, Maritime Education Technology Adoption}
}