Pedagogical Transformation through Generative AI: A Hybrid Deep Learning Comparison with Traditional Digital Learning Materials Based on GenAI-EduNet Framework

Pedagogical Transformation through Generative AI: A Hybrid Deep Learning Comparison with Traditional Digital Learning Materials Based on GenAI-EduNet Framework | 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 Pedagogical Transformation through Generative AI: A Hybrid Deep Learning Comparison with Traditional Digital Learning Materials Based on GenAI-EduNet Framework Faisal Alshammari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8701708/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Mar, 2026 Read the published version in Quality & Quantity → Version 1 posted You are reading this latest preprint version Abstract Generative Artificial Intelligence (GenAI) tools integration in higher education have radically changed the paradigm of learning, but the empirical investigation comparing their efficiency to traditional digital goods has been severely underrepresented. GenAI-EduNet is a new hybrid deep learning architecture that, using Long Short-Term Memory (LSTM) networks, alongside multi-head self-attention transformers, predicts and analyzes learning results when students work with GenAI tools (in this case, Google NotebookLM) in comparison to traditional digital learning materials. Our study is rigorous quasi-experimental mixed-method research including 1847 undergraduate students in two academic semesters with model training and validation through Open University Learning Analytics Dataset, OULAD, and EdNet data. Some major innovations presented by our framework include: (1) a multi-modal encoding technique of engagement with gated attention which captures behavioral, cognitive and affective dimensions of learning; (2) an adaptive knowledge tracing module with transformer-based multi-head self-attention used to understand learning temporal patterns; (3) a comparative performance predictor based on dual-branch architecture with inverse propensity weighting (IPW) to perform causal inference; and (4) a binary outcome predictor to predict pass/fail. The experimental findings indicate that GenAI-EduNet classifies the outcomes of student performance with 94.7% accuracy and 0.967 AUC, which is 8.3% more than ten state-of-the-art baseline methods. The quasi-experimental analysis reveals that students using NotebookLM exhibited significantly higher learning gains across all measured outcomes: post-test scores ( \(\:d=0.73\) , \(\:p<.001\) ), higher-order thinking skills ( \(\:d=0.74\) , \(\:p<.001\) ), cognitive engagement ( \(\:d=0.59\) , \(\:p<.001\) ), and self-efficacy ( \(\:d=0.52\) , \(\:p<.001\) ). The findings of our study give practical implications related to integrating educational technology and add a proven computational model to the learning analytics research during the age of generative AI. Generative AI Higher Education Learning Analytics Deep Learning Knowledge Tracing Educational Technology Mixed-Methods Research Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Mar, 2026 Read the published version in Quality & Quantity → 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8701708","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584495343,"identity":"edcbf1f8-7e2c-4d5b-b779-bb0eab5813d1","order_by":0,"name":"Faisal Alshammari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3OIQvCQBTA8XccuHLDOovfQHhVRP0gFkHQsoFdQdOtqNmvsXIYbxxomVgXXVkyLIkr4qmYxEOb4f5l3LHfvQdgs/1hzhyIJPPnQRaPT99MmNR/vki8/plQ9hUBeozLTafXCPeZavsKqo6P5DI1kQoqNxkEIhmhCoSC2uKElG0/ky4AKsJpIOQQHgRTHylUTFOcIi75LBCHHFRTk256X+xqIgyly/X7qZ5C7lM8H8HlBkLZWLl8p0kO8VKMmJfk+mZlIE4YZSWf6MWGtChFq14NB1F2OX8mQN821UkDsNlsNtsX3QDvMVa0lyoP3wAAAABJRU5ErkJggg==","orcid":"","institution":"Majmaah University","correspondingAuthor":true,"prefix":"","firstName":"Faisal","middleName":"","lastName":"Alshammari","suffix":""}],"badges":[],"createdAt":"2026-01-26 15:10:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8701708/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8701708/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11135-026-02699-w","type":"published","date":"2026-03-24T16:09:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":105756088,"identity":"228d2019-1d78-4f94-8393-cc81c6e985a6","added_by":"auto","created_at":"2026-03-30 16:35:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1206886,"visible":true,"origin":"","legend":"","description":"","filename":"V1GenerativeAI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8701708/v1_covered_f13fc716-6fb7-4857-a5df-27f24fe15d1d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pedagogical Transformation through Generative AI: A Hybrid Deep Learning Comparison with Traditional Digital Learning Materials Based on GenAI-EduNet Framework","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Generative AI, Higher Education, Learning Analytics, Deep Learning, Knowledge Tracing, Educational Technology, Mixed-Methods Research","lastPublishedDoi":"10.21203/rs.3.rs-8701708/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8701708/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGenerative Artificial Intelligence (GenAI) tools integration in higher education have radically changed the paradigm of learning, but the empirical investigation comparing their efficiency to traditional digital goods has been severely underrepresented. GenAI-EduNet is a new hybrid deep learning architecture that, using Long Short-Term Memory (LSTM) networks, alongside multi-head self-attention transformers, predicts and analyzes learning results when students work with GenAI tools (in this case, Google NotebookLM) in comparison to traditional digital learning materials. Our study is rigorous quasi-experimental mixed-method research including 1847 undergraduate students in two academic semesters with model training and validation through Open University Learning Analytics Dataset, OULAD, and EdNet data. Some major innovations presented by our framework include: (1) a multi-modal encoding technique of engagement with gated attention which captures behavioral, cognitive and affective dimensions of learning; (2) an adaptive knowledge tracing module with transformer-based multi-head self-attention used to understand learning temporal patterns; (3) a comparative performance predictor based on dual-branch architecture with inverse propensity weighting (IPW) to perform causal inference; and (4) a binary outcome predictor to predict pass/fail. The experimental findings indicate that GenAI-EduNet classifies the outcomes of student performance with 94.7% accuracy and 0.967 AUC, which is 8.3% more than ten state-of-the-art baseline methods. The quasi-experimental analysis reveals that students using NotebookLM exhibited significantly higher learning gains across all measured outcomes: post-test scores (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d=0.73\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;.001\\)\u003c/span\u003e\u003c/span\u003e), higher-order thinking skills (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d=0.74\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;.001\\)\u003c/span\u003e\u003c/span\u003e), cognitive engagement (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d=0.59\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;.001\\)\u003c/span\u003e\u003c/span\u003e), and self-efficacy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d=0.52\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;.001\\)\u003c/span\u003e\u003c/span\u003e). The findings of our study give practical implications related to integrating educational technology and add a proven computational model to the learning analytics research during the age of generative AI.\u003c/p\u003e","manuscriptTitle":"Pedagogical Transformation through Generative AI: A Hybrid Deep Learning Comparison with Traditional Digital Learning Materials Based on GenAI-EduNet Framework","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 13:23:27","doi":"10.21203/rs.3.rs-8701708/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"8099044f-4c35-4835-8f86-375d5a65fa51","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:32:20+00:00","versionOfRecord":{"articleIdentity":"rs-8701708","link":"https://doi.org/10.1007/s11135-026-02699-w","journal":{"identity":"quality-and-quantity","isVorOnly":false,"title":"Quality \u0026 Quantity"},"publishedOn":"2026-03-24 16:09:50","publishedOnDateReadable":"March 24th, 2026"},"versionCreatedAt":"2026-02-03 13:23:27","video":"","vorDoi":"10.1007/s11135-026-02699-w","vorDoiUrl":"https://doi.org/10.1007/s11135-026-02699-w","workflowStages":[]},"version":"v1","identity":"rs-8701708","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8701708","identity":"rs-8701708","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}