AUAR: AI-driven user-centric Experience in Augmented Reality for Content Creation Using Machine Learning and Geospatial Data

AUAR: AI-driven user-centric Experience in Augmented Reality for Content Creation Using Machine Learning and Geospatial Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article AUAR: AI-driven user-centric Experience in Augmented Reality for Content Creation Using Machine Learning and Geospatial Data Somaiieh Rokhsaritalemi, Abolghasem Sadeghi-Niaraki, Soo-Mi Choi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7134248/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Advancements in AI are enhancing augmented reality (AR), leading to a new generation of systems that revolutionize personalized user experiences. This paper presents AUAR, an innovative AI-Driven User-Centric AR experience. This system leverages generative AI, machine learning, and geospatial analysis, and multi-criteria decision-making (MCDM) to model the context related to users and their environments, facilitating the generation of personalized virtual content. Implemented in a park as a case study, AUAR offers two main features: the first provides an emotion-aware AR experience, which also features haptic interactions, by creating and rendering content related to a Christmas tree. The second feature, adaptive AR services, adjusts content dynamically using Level of Detail (LOD) principles for park elements, such as walking trajectories, derived from geospatial and user-generated data while incorporating MCDM with MAIRCA analysis to enhance user interactions and decision-making. The efficacy of our system is rigorously evaluated through user feedback and statistical analysis. This research aims to create a roadmap for AI-Driven User-Centric AR experiences, focusing on optimizing system performance and enhancing user satisfaction. Physical sciences/Engineering Physical sciences/Mathematics and computing Dynamic Content Creation Augmented Reality (AR) Emotion Detection Geospatial Data and Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7134248","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503164417,"identity":"8b7954e8-89ea-40a5-a521-ba34e2d32579","order_by":0,"name":"Somaiieh Rokhsaritalemi","email":"","orcid":"","institution":"Sejong University","correspondingAuthor":false,"prefix":"","firstName":"Somaiieh","middleName":"","lastName":"Rokhsaritalemi","suffix":""},{"id":503164419,"identity":"2a6682c8-af64-415d-aa94-799ebb1022db","order_by":1,"name":"Abolghasem Sadeghi-Niaraki","email":"","orcid":"","institution":"Sejong University","correspondingAuthor":false,"prefix":"","firstName":"Abolghasem","middleName":"","lastName":"Sadeghi-Niaraki","suffix":""},{"id":503164421,"identity":"2fdb5676-4c1a-44d5-92c6-1670ec161436","order_by":2,"name":"Soo-Mi Choi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYHACNgYGAxsgfQDK5yFOSxrJWhgOI/EJaZGfdvjZgw8F5+35GQ8fe/iFwU6egefsA7xaDG6nmRvOMLidOLPhWLqxDEOyYQNvuwF+LdI5bNI8BrcTDA6cMZOWYGBOYOBnI+Cw2WAt5+ztIVrqCWthuA3WcoBxA8MZM8kPDIcTGHjb8OsA+sVMcoZBcuKMA8fSpBkMjhu28Rwj5LDkZxIf/tjZ8884fEzyR0W1PD9PGgGHwYHEAQZmHgNINBEJ+BsYGH8Qr3wUjIJRMApGEAAA6Yo82ALGOPgAAAAASUVORK5CYII=","orcid":"","institution":"Sejong University","correspondingAuthor":true,"prefix":"","firstName":"Soo-Mi","middleName":"","lastName":"Choi","suffix":""}],"badges":[],"createdAt":"2025-07-15 22:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7134248/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7134248/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108300421,"identity":"e3a57c27-d26d-4081-a835-fd598e066cc0","added_by":"auto","created_at":"2026-05-02 01:55:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":816440,"visible":true,"origin":"","legend":"","description":"","filename":"AUARNature2025810Revised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7134248/v1_covered_3af2ed3f-29f0-488e-bcd8-a7f9ae73a700.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AUAR: AI-driven user-centric Experience in Augmented Reality for Content Creation Using Machine Learning and Geospatial Data","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dynamic Content Creation, Augmented Reality (AR), Emotion Detection, Geospatial Data and Analysis","lastPublishedDoi":"10.21203/rs.3.rs-7134248/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7134248/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAdvancements in AI are enhancing augmented reality (AR), leading to a new generation of systems that revolutionize personalized user experiences. 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