FairSYN-Edu: A Fairness-Aware, Privacy-Preserving Diffusion Model for Educational Data Synthesis

FairSYN-Edu: A Fairness-Aware, Privacy-Preserving Diffusion Model for Educational Data Synthesis | 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 FairSYN-Edu: A Fairness-Aware, Privacy-Preserving Diffusion Model for Educational Data Synthesis Kadir Kesgin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6631139/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The increasing demand for privacy-preserving, ethically aligned synthetic data generation in education has highlighted the limitations of existing tabular data generators. Traditional approaches often sacrifice fairness or privacy in pursuit of predictive accuracy, rendering them unsuitable for high-stakes academic settings. In this paper, we propose FairSYN-Edu, a novel diffusion-based synthetic data generation framework designed for educational data. By integrating adversarial debiasing and differentially private training into the generative process, FairSYN-Edu jointly optimizes utility, fairness, and privacy. We evaluate our approach on three real-world educational datasets spanning MOOC, K–12 tutoring, and LMS environments. Experimental results demonstrate that FairSYN-Edu achieves significantly lower demographic disparities, maintains competitive predictive performance (RMSE = 0.402), and provides moderate resistance to membership inference attacks (AUC = 0.705). Ablation studies, error gap analysis, and SHAP-based interpretability evaluations confirm the robustness and ethical soundness of our method. We release the full implementation, synthetic benchmark suite, and documentation to foster reproducibility and responsible AI practices in education. Data science applications in education Pedagogical issues Teaching/learning strategies Fairness in AI Differential privacy Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviews received at journal 25 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers agreed at journal 23 May, 2025 Reviewers invited by journal 23 May, 2025 Editor assigned by journal 11 May, 2025 Submission checks completed at journal 11 May, 2025 First submitted to journal 09 May, 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. 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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-6631139","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":461548383,"identity":"ee7a80ad-16da-436c-a45b-0d2b08151a5e","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":"2025-05-09 19:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6631139/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6631139/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83530039,"identity":"9627e2d2-7d0f-4596-b39e-0e3d1b5e313f","added_by":"auto","created_at":"2025-05-28 04:32:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":605883,"visible":true,"origin":"","legend":"","description":"","filename":"FairSYNEdu3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6631139/v1_covered_264e3b09-d54c-4459-8318-386682cff591.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"FairSYN-Edu: A Fairness-Aware, Privacy-Preserving Diffusion Model for Educational Data Synthesis","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Data science applications in education, Pedagogical issues, Teaching/learning strategies, Fairness in AI, Differential privacy","lastPublishedDoi":"10.21203/rs.3.rs-6631139/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6631139/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The increasing demand for privacy-preserving, ethically aligned synthetic data generation in education has highlighted the limitations of existing tabular data generators. 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