<?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">JERA</journal-id><journal-title-group><journal-title>Journal of Electronic Research and Application</journal-title></journal-title-group><issn>2208-3502</issn><eissn>2208-3510</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/jera.v8i2.6091</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Research and Application of AI-Based Interactive Exhibits in Wuhan Museum of Science and Technology</title><url>https://artdesignp.com/journal/JERA/8/2/10.26689/jera.v8i2.6091</url><author>YanTing</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>8</volume><issue>2</issue><history><date date-type="pub"><published-time>2024-03-29</published-time></date></history><abstract>This article aims to explore the development and application of AI-based interactive exhibits in Wuhan Museum of Science and Technology. By utilizing computer vision, natural language processing, and machine learning technologies, an innovative exhibit development and application system is proposed. This system employs deep learning algorithms and data analysis methods to achieve real-time perception of visitor behavior and adaptive interaction. The development process involves designing user interfaces and interaction methods to effectively enhance visitor engagement and learning outcomes. Through evaluation and comparison in practical applications, the potential of this system in enhancing exhibit interaction, increasing visitor engagement, improving educational effectiveness, and expanding avenues for scientific knowledge dissemination are validated.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Smith A, Johnson B, 2020, Interactive Exhibit Design: Exploring the Role of Artificial Intelligence in Enhancing Visitor Engagement. Museum Management and Curatorship, 38(2): 123–140.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Brown C, Lee J, Jones M, 2019, Integrating Computer Vision and Natural Language Processing for Interactive Museum Exhibits. Journal of Interactive Exhibits, 27(3): 210–225.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Zhang D, Liu J, Wang L, 2018, Deep Learning for Interactive Exhibit Development: A Case Study in Science Museums. International Journal of Computer Vision, 126(2): 123–139.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Chen X, Li Y, Wang H, 2017, Enhancing Learning Outcomes Through AI-Based Interactive Exhibition Design. Journal of Educational Technology &amp; Society, 20(2): 98–109.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
