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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">JCER</journal-id><journal-title-group><journal-title>Journal of Contemporary Educational Research</journal-title></journal-title-group><issn>2208-8466</issn><eissn>2208-8474</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/jcer.v10i4.14830</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Development of AI-Based Personalized Textbooks: Theoretical Framework and Practical Strategies</title><url>https://artdesignp.com/journal/JCER/10/4/10.26689/jcer.v10i4.14830</url><author>PingYue</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>10</volume><issue>4</issue><history><date date-type="pub"><published-time>2026-05-20</published-time></date></history><abstract>Personalized learning has become a central issue in contemporary educational reform. As the fundamental carrier of teaching and learning, the intelligent transformation of textbooks has emerged as a key pathway for advancing the implementation of personalized education. This study focuses on the development of AI-based personalized textbooks. By defining their conceptual connotations and core components, identifies three essential characteristics: generative, adaptive and evolvable. Building upon this foundation, a three-dimensional theoretical framework—comprising the knowledge layer, cognitive layer, and technological layer—is constructed from the perspectives of epistemology, cognitive science, and technological synergy. Furthermore, four practical strategies are proposed: constructing a “human-machine co-creation” dynamic content ecosystem; implementing a “data-driven” precise adaptation scheme; creating an “integrated virtual-physical” intelligent interactive environment; upholding the ethical and safety boundary of “technology for good”.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Siemens G, Baker R, 2012, Learning Analytics and Educational Data Mining: Towards Communication and Collaboration, Proceedings of the 2nd International Conference on Learning and Analytics and Knowledge, ACM, New York, 252–254.</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>Tahiru F, 2021, AI in Education: A Systematic Literature Review. 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