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<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">erd</journal-id><journal-title-group><journal-title>Education Reform and Development</journal-title></journal-title-group><issn>2652-5364</issn><eissn>2652-5372</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/erd.v8i1.13723</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Research on the AI-Driven Personalized Teaching Model for Secondary Vocational Computer Network Technology Courses</title><url>https://artdesignp.com/journal/erd/8/1/10.26689/erd.v8i1.13723</url><author>LiYongliang</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>8</volume><issue>1</issue><history><date date-type="pub"><published-time>2026-02-04</published-time></date></history><abstract>Digital transformation and industrial upgrading have put forward new requirements for the precise and personalized training of secondary vocational network technology talents. To address the current problems in secondary vocational computer network course teaching, such as significant differences in students’ basic foundations, insufficient adaptability of teaching resources, and rigid learning paths, this study constructs an AI-driven personalized teaching model centered on learner profiles. By collecting multi-source teaching data, the model dynamically builds fine-grained learner profiles. On this premise, it focuses on exploring personalized teaching path generation methods based on reinforcement learning and knowledge graphs, as well as adaptive resource recommendation mechanisms integrating content correlation, collaborative filtering, and sequence patterns. 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