Undergraduate Students’ Perceptions of Large Language Models in Higher Education: Self-Perceived Benefits, Reliance, and Accuracy Assessment

Undergraduate Students’ Perceptions of Large Language Models in Higher Education: Self-Perceived Benefits, Reliance, and Accuracy Assessment | 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 Undergraduate Students’ Perceptions of Large Language Models in Higher Education: Self-Perceived Benefits, Reliance, and Accuracy Assessment Samaneh Zamanifard, Sajad Goudarzi, Masoumeh Soleimani, Andrew Robb This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7059648/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 Large Language Models (LLMs) have rapidly gained prominence in higher education, yet their impact on students' academic experiences remains understudied. This research investigates undergraduate students' perceptions of LLMs, focusing on four key areas: perceived benefits for comprehending complex course material, reliance on LLMs for academic success, assessment of LLM-generated information accuracy, and perceived benefits in completing coursework. Additionally, the study explores how these perceptions vary across gender, personality traits, education levels, LLM usage frequency, and subscription status. Data was collected through a survey of 653 undergraduate students from various disciplines. Employing factor analyses and structural equation modeling, the study found that students generally perceive LLMs as beneficial for understanding complex material and completing coursework, with 67-72\% reporting improved comprehension. However, only 24-30\% expressed high reliance on LLMs for academic success, while 76-77\% felt confident in assessing LLM-generated information accuracy. Significant correlations were found between personality traits, LLM usage frequency, and perceived benefits. These findings provide crucial insights into the role of LLMs in higher education, suggesting that while students find them valuable, students believe that they maintain critical engagement. The research highlights the need for tailored educational strategies that leverage LLMs' benefits while fostering independent learning skills. LLMs Artificial Intelligence Higher Education Student Perception Learning Technologies Full Text Additional Declarations The authors declare no competing interests. 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. 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-7059648","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481354673,"identity":"4bce8544-2bad-4dfb-8675-d71fc3e639f9","order_by":0,"name":"Samaneh Zamanifard","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYPACCTkGdjgngTgtxgzMJGphSGwgWot8/+GnG37usEjvb2Y+uvFHxR0GfvYcA7xaDA4cM7vZe0Yid8ZhtrTbPGeeMUj2vCGghbHB7AZvm0TuBmYes9uMbYcZDG4QsEW+mf3bzb9tEukGzPzfbv4EarEnpIXhGNBwoC0JBsw8bEDrgLZIEPLLGZ6y27JtEoZAv5gB/XKYR+LMswL8Dus/vu3m27Y6ef725mc3f1QcluNvT96A32HogIc05aNgFIyCUTAKsAIAEd5GF7JAQx0AAAAASUVORK5CYII=","orcid":"","institution":"Clemson University","correspondingAuthor":true,"prefix":"","firstName":"Samaneh","middleName":"","lastName":"Zamanifard","suffix":""},{"id":481354674,"identity":"c7fc738f-94f0-43d8-9027-496b6c11b154","order_by":1,"name":"Sajad Goudarzi","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Sajad","middleName":"","lastName":"Goudarzi","suffix":""},{"id":481354675,"identity":"fc7e0a57-e343-4d40-80de-182c6bb0f38e","order_by":2,"name":"Masoumeh Soleimani","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Masoumeh","middleName":"","lastName":"Soleimani","suffix":""},{"id":481354676,"identity":"5b3be430-b7c8-4f6f-9853-bbc98949cd67","order_by":3,"name":"Andrew Robb","email":"","orcid":"","institution":"Clemson University","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Robb","suffix":""}],"badges":[],"createdAt":"2025-07-06 19:42:50","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7059648/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7059648/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86228832,"identity":"ed78447a-6c5d-49ae-b026-c2e2dc7b147e","added_by":"auto","created_at":"2025-07-08 08:24:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":403743,"visible":true,"origin":"","legend":"","description":"","filename":"StudentsPerception.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7059648/v1_covered_2b4df3b7-5b78-4af6-b4fd-6f7c9c5d73de.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eUndergraduate Students’ Perceptions of Large Language Models in Higher Education: Self-Perceived Benefits, Reliance, and Accuracy Assessment\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Clemson University","isAcceptedByJournal":false,"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":"LLMs, Artificial Intelligence, Higher Education, Student Perception, Learning Technologies","lastPublishedDoi":"10.21203/rs.3.rs-7059648/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7059648/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLarge Language Models (LLMs) have rapidly gained prominence in higher education, yet their impact on students' academic experiences remains understudied. This research investigates undergraduate students' perceptions of LLMs, focusing on four key areas: perceived benefits for comprehending complex course material, reliance on LLMs for academic success, assessment of LLM-generated information accuracy, and perceived benefits in completing coursework. Additionally, the study explores how these perceptions vary across gender, personality traits, education levels, LLM usage frequency, and subscription status. Data was collected through a survey of 653 undergraduate students from various disciplines. Employing factor analyses and structural equation modeling, the study found that students generally perceive LLMs as beneficial for understanding complex material and completing coursework, with 67-72\\% reporting improved comprehension. However, only 24-30\\% expressed high reliance on LLMs for academic success, while 76-77\\% felt confident in assessing LLM-generated information accuracy. 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