Mapping and Modeling the Role of Artificial Intelligence in Science Education: From Bibliometrics to Classroom Integration

Mapping and Modeling the Role of Artificial Intelligence in Science Education: From Bibliometrics to Classroom Integration | 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 Research Article Mapping and Modeling the Role of Artificial Intelligence in Science Education: From Bibliometrics to Classroom Integration Kadir Kesgin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6542160/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract This study presents a comprehensive and pedagogically grounded bibliometric and predictive investigation into the integration of artificial intelligence (AI) within science education. By applying advanced bibliometric techniques and predictive modeling, we examine global publication trends and emerging research themes between 2015 and 2024. Utilizing advanced regression methods—Linear Regression (LR) and Support Vector Regression (SVR)—we forecast scholarly activity, identify potential saturation points, and propose strategic alignments for future educational initiatives. Moving beyond bibliometric mapping, the study bridges AI research with pedagogical practice by proposing classroom-level implementation scenarios and structured teacher education frameworks. We demonstrate how generative AI tools (e.g., ChatGPT) can be embedded into science curricula to support inquiry-based learning, real-time feedback, and differentiated instruction. Furthermore, we outline how predictive modeling outcomes can inform teacher education programs focusing on ethical decision-making, AI tool selection, and culturally responsive AI-driven instruction. Our findings underscore the critical intersection of technological innovation and pedagogical preparedness. This multidimensional approach aims to empower researchers, educators, and policymakers to responsibly and effectively leverage AI technologies to advance science teaching and learning in diverse educational contexts worldwide. Artificial Intelligence Science Education Bibliometric Analysis Predictive Modeling Support Vector Regression Generative AI Teacher Training Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Jul, 2025 Reviews received at journal 11 Jun, 2025 Reviews received at journal 08 Jun, 2025 Reviews received at journal 08 Jun, 2025 Reviewers agreed at journal 07 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers invited by journal 02 Jun, 2025 Editor assigned by journal 10 May, 2025 Submission checks completed at journal 10 May, 2025 First submitted to journal 27 Apr, 2025 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-6542160","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454663142,"identity":"1bd5abd6-e7ad-4d45-b516-7d220f66cb94","order_by":0,"name":"Kadir Kesgin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACCQYGgwMMFQeAzMQGiNABorScQdXC2EBICwNjG0hLAgNxWiTbD288XDnvjrw5e3Lbpxs1DHJ8NxLYH1fg0SLNk1Zw8Oy2Z4Y7ex42z845xmAseSOBsfEMHi1yDDkGBxu3HWbccCOxmTmHjSFxA0gLPpfJ8b8Baplz2B6i5R9DPUEt0hIgWxoOJ4K15LYxJBgQ0iI541nBwYZjz5I3nHkI1NInYTjzzMPGmfi0SJxP3vyxoeaO7Ybj6Y+Zc77ZyPMdTz7wEZ8WDCOAGH+0jIJRMApGwSggAgAAJb1cVt5uI7wAAAAASUVORK5CYII=","orcid":"","institution":"Bandırma Onyedi Eylül University","correspondingAuthor":true,"prefix":"","firstName":"Kadir","middleName":"","lastName":"Kesgin","suffix":""}],"badges":[],"createdAt":"2025-04-27 19:23:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6542160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6542160/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82592785,"identity":"da067ad4-7008-47e8-92ac-9f77aad0677f","added_by":"auto","created_at":"2025-05-13 08:21:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":439895,"visible":true,"origin":"","legend":"","description":"","filename":"mappingmodelling1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6542160/v1_covered_b30a90b8-cd47-4a44-95fb-b4b86ac00082.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mapping and Modeling the Role of Artificial Intelligence in Science Education: From Bibliometrics to Classroom Integration","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Science Education, Bibliometric Analysis, Predictive Modeling, Support Vector Regression, Generative AI, Teacher Training","lastPublishedDoi":"10.21203/rs.3.rs-6542160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6542160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a comprehensive and pedagogically grounded bibliometric and predictive investigation into the integration of artificial intelligence (AI) within science education. 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