Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Arumugam, Pitchai; Jayachristrayar, Singarayar; Rega, Rajendran; Rayar, Jesus Title A name given to the resource Developing a Framework and Wireframe for AI-Driven Personalization and Recommendation Systems in Library Management: A Design Thinking Approach Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Journal of Web Librarianship; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1080/19322909.2026.2666035" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/19322909.2026.2666035</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038963876?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038963876?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Arumugam P., Indian Institute of Astrophysics, Karnataka, Bengaluru, India; Jayachristrayar S., CHRIST (Deemed to be University), Bengaluru, India; Rega R., Government Law College, Tamil Nadu, Theni, India; Rayar J., Department of Tamil, Central University of Tamil Nadu, Tamil Nadu, Thiruvarur, India Description An account of the resource This research develops a framework and wireframes for an AI-driven personalization and recommendation system designed to enhance Library Management Systems (LMS). AI adapts library services dynamically to individual user characteristics and behaviors, such as reading preferences and interaction patterns, using predictive algorithms and behavioral analysis to deliver tailored recommendations. The study is primarily grounded in a User-Centered Design Thinking approach to ensure the system is intuitive, responsive, and meets diverse user needs. The proposed framework emphasizes seamless data integration and adaptive interface design. Prototypes created using Figma reflect intuitive, inclusive, and accessible features aligned with user needs. The prototype was evaluated in a controlled environment with high-frequency LMS users using the System Usability Scale (SUS) to assess usability and user satisfaction, achieving a score indicating excellent usability. Although the AI processing engine remains conceptual, this research provides a structured foundation for the future implementation of AI-driven recommendation systems in LMS, supporting enhanced user engagement and improved Selective Dissemination of Information through personalized and inclusive library experiences. 2026 P. Arumugam, Jayachristrayar S, Rega R and Jesus Rayar. Subject The topic of the resource AI recommendation system; design thinking; library management system; personalized recommendation system; selective dissemination of information; wireframe Publisher An entity responsible for making the resource available Routledge Relation A related resource ISSN: 19322909; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online