<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">CEF</journal-id><journal-title-group><journal-title>Contemporary Education Frontiers</journal-title></journal-title-group><issn>3029-1879</issn><eissn>3029-1860</eissn><publisher><publisher-name>WHIOCE PUBLISHING PTE. LTD.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.18063/CEF.v4i2.1587</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Application of AIS Data Analysis in Teaching of Collision Avoidance in Nautical Technology Specialty</title><url>https://artdesignp.com/journal/CEF/4/2/10.18063/CEF.v4i2.1587</url><author>WangGenyuan,LuGuoguang,ChenChuiyao</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>4</volume><issue>2</issue><history><date date-type="pub"><published-time>2026-02-26</published-time></date></history><abstract>Against the backdrop of deep integration between intelligent shipping and maritime data, the digital transformation of traditional maritime technology education has become an inevitable requirement for industry development. As the core resource documenting real-world vessel navigation behavior, Automatic Identification System (AIS) data holds irreplaceable value in analyzing collision avoidance decision-making logic and establishing objective quantitative teaching evaluation systems. This paper systematically reviews the research progress in the past five years, both domestically and internationally, on utilizing AIS data mining technology to identify collision avoidance scenarios, extract operational characteristics, and assess collision risks. It focuses on exploring application pathways of data analysis technologies in auxiliary maritime collision avoidance training, identifying core challenges and research gaps in current technical implementation scenarios. The study aims to provide theoretical foundations for vocational undergraduate programs to establish a "data-driven" collision avoidance teaching model, thereby fostering high-quality interdisciplinary maritime professionals equipped with data-driven thinking.</abstract><keywords>AIS data analysis, navigation technology, collision avoidance instruction, vocational undergraduate education, teaching reform</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] Yang Y, Liu Y, Li G, et al., 2024, Harnessing the Power of Machine Learning for AIS Data-Driven Maritime Research: A Comprehensive Review.&amp;nbsp;Transportation Research Part E: Logistics and Transportation Review, 183: 103426.
[2] Gang LH, Liu T, Wang XM, et al., 2021, Method for extracting ship collision avoidance behavior from AIS data.&amp;nbsp;Ship Science and Technology, 43(15): 31-36.
[3] Liu ZZ, Wang DQ, et al., 2024, Ship collision avoidance simulation system and method based on AIS big data. Chinese Patent: CN117634027A.
[4] Zhang J, You B, Hirdaris S, et al., 2023, A Review of Research on Autonomous Collision Avoidance Performance Testing and an Evaluation of Intelligent Vessels.&amp;nbsp;Journal of Marine Science and Engineering, 11(8): 1570.
[5] Xie X, et al., 2024, Research on AIS Data Analysis and Its Integration into Discipline-Specific Teaching.&amp;nbsp;Journal of Physics: Conference Series, 2863(1): 012028.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
