<?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">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.v8i8.15386</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Digital‑Fluid Intelligent Learning: AI‑Driven Visualization for Dynamic Demonstration in Fluid Mechanics Classes</title><url>https://artdesignp.com/journal/erd/8/8/10.26689/erd.v8i8.15386</url><author>ZhangRuizi</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>8</volume><issue>8</issue><history><date date-type="pub"><published-time>2026-09-17</published-time></date></history><abstract>Aiming at common pain points in current fluid‑mechanics classes, including invisible flow phenomena, insufficient physical experiment supplies, high‑threshold conventional CFD tools, and poor interactivity of digital resources, this paper takes the &amp;ldquo;Digital‑Fluid Intelligent Learning&amp;rdquo; AI visualization demonstration system as the carrier to explore innovative paths for empowering dynamic demonstrations in fluid‑mechanics classes with AI technology. From four dimensions: flow‑field visualization, virtual‑experiment library, real‑time solving engine, and inquiry‑based ecosystem, this study puts forward implementation strategies. It breaks the disconnection between theories and phenomena in traditional teaching, realizes real‑time interactive generation of dynamic flow‑field effects in class, effectively lowers the cognitive threshold for core knowledge points, and provides referable practical solutions for the digital transformation of engineering fluid‑related courses.</abstract><keywords>Digital‑fluid intelligent learning, AI visualization, Fluid mechanics, In‑class dynamic demonstration, Virtual fluid experiment</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1]&amp;nbsp;&amp;nbsp; Zhou L, Liang Y, Yuan A, et al., 2025, Research on Teaching Innovation of Industry-Education Integration for the Course &amp;ldquo;Fundamentals of Thermal Engineering and Fluid Mechanics&amp;rdquo; Under the Background of Emerging Engineering Education. Guizhou Agricultural Mechanization, 2025(4): 56&amp;ndash;60.
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