Interactive online learning method for students based on Artificial Intelligence

Interactive online learning method for students based on Artificial Intelligence | 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 Interactive online learning method for students based on Artificial Intelligence Cizhang Li, Wenfen Yin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6460307/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract In recent years, the demand for intelligent and interactive online education systems has grown significantly, driven by the need for personalized learning experiences and effective student engagement. This study proposes a novel approach that integrates the Dwarf Mongoose Optimization (DMO) algorithm with a Gated Recurrent Unit (GRU) neural network to develop an AI-powered interactive online learning model. The proposed DMO-GRU framework leverages the optimization capability of DMO for feature selection and parameter tuning, while GRU effectively captures temporal learning patterns in student data. A comprehensive literature review was conducted using databases such as IEEE Xplore, ACM, and Google Scholar, focusing on studies from the last decade. The model was evaluated through experimental analysis using key regression and classification metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R² Score,Accuracy, Precision, Recall, Sensitivity, Specificity, and F1-Score with training time. Results indicate that the DMO-GRU outperforms traditional models such as Linear Regression, Random Forest, SVR, and XGBoost, offering higher prediction accuracy and better identification of student learning outcomes. The system also supports interactive modes such as audio, video, and one-to-one sessions, contributing to improved learner engagement. This study demonstrates the potential of AI-driven optimization techniques to enhance the effectiveness, adaptability, and personalization of online education platforms. Artificial Intelligence (AI) Interactive Online Learning Dwarf Mongoose Optimization (DMO) Gated Recurrent Unit (GRU) Student Performance Prediction Personalized Learning Educational Data Mining Deep Learning Adaptive Learning Systems Online Education Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviews received at journal 12 May, 2025 Reviewers agreed at journal 12 May, 2025 Reviews received at journal 11 May, 2025 Reviewers agreed at journal 11 May, 2025 Reviewers agreed at journal 09 May, 2025 Reviewers agreed at journal 09 May, 2025 Reviewers invited by journal 08 May, 2025 Editor invited by journal 07 May, 2025 Editor assigned by journal 01 May, 2025 Submission checks completed at journal 01 May, 2025 First submitted to journal 16 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. 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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-6460307","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":455399546,"identity":"2d24550e-7806-489b-aa6a-ee05f7b16df9","order_by":0,"name":"Cizhang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBAC/mYGNhCdwMbMkP7hA5DFx8DAjFeLxGGYFnaGZ4wzgAJshLQYOEC1MPAzPmPmIUoLO/uzBz931CbwMTOnPbapOFzHxt582IChxiYapxagFwx7zxwH+oUt3TjnzGEJNp5jyQkMx9JyG3BrOSbB23YMqIUnQTq3DahFIsf4AGPDYTxaGNsk/4K18H+QtiRKizMzmzRvWw0okNOkGaFaEvBpkTjMxiYt23YApCXZsOdMumQb0C8GCXj8wt9//Jnk27a6evn+A4kPflRY8/MDQ0ziQ40NTi1QcBiNn4BfOQjUEVYyCkbBKBgFIxcAANayTvvWOVb2AAAAAElFTkSuQmCC","orcid":"","institution":"Hunan Open University","correspondingAuthor":true,"prefix":"","firstName":"Cizhang","middleName":"","lastName":"Li","suffix":""},{"id":455399547,"identity":"612f837f-88f7-481e-989d-633fa51517fc","order_by":1,"name":"Wenfen Yin","email":"","orcid":"","institution":"Hunan Open University","correspondingAuthor":false,"prefix":"","firstName":"Wenfen","middleName":"","lastName":"Yin","suffix":""}],"badges":[],"createdAt":"2025-04-16 06:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6460307/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6460307/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82622595,"identity":"68b8bfc9-d668-415f-899c-d695cf0f21e2","added_by":"auto","created_at":"2025-05-13 12:32:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":593202,"visible":true,"origin":"","legend":"","description":"","filename":"InteractiveonlinelearningmethodforstudentsbasedonArtificialIntelligence.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6460307/v1_covered_c0cb985a-6a91-4618-9193-09f64288365b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Interactive online learning method for students based on Artificial Intelligence","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-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence (AI), Interactive Online Learning, Dwarf Mongoose Optimization (DMO), Gated Recurrent Unit (GRU), Student Performance Prediction, Personalized Learning, Educational Data Mining, Deep Learning, Adaptive Learning Systems, Online Education","lastPublishedDoi":"10.21203/rs.3.rs-6460307/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6460307/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn recent years, the demand for intelligent and interactive online education systems has grown significantly, driven by the need for personalized learning experiences and effective student engagement. 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