Grade Expectations: Generative AI Use Does Not (Yet) Alter Student Achievement

Grade Expectations: Generative AI Use Does Not (Yet) Alter Student Achievement | 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 Grade Expectations: Generative AI Use Does Not (Yet) Alter Student Achievement Mareike Mueller, René Arnold, Stefan Wagenpfeil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9053629/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The rapid diffusion of generative artificial intelligence (AI) in higher education has reshaped academic practices, yet its implications for transparency, authorship, and academic performance remain insufficiently understood. This study analyzes 4,489 student projects submitted between 2022 and 2025 to investigate the relationship between self-reported AI use, linguistic characteristics of student writing, and academic achievement. Using mixed-effects regression models and a comprehensive institutional dataset, we assess to what extent declared AI use, overall academic achievement, demographic variables, and text readability metrics predict project grades. The findings show that declared AI use is not systematically associated with higher or lower marks once contextual factors are controlled for, while overall performance, attempt number, linguistic complexity, and submission length emerge as robust predictors. The results highlight the importance of nuanced, evidence-based institutional policies that balance ethical AI integration with the safeguarding of academic integrity. By offering a large-scale empirical examination of declared AI usage in authentic coursework, this study contributes critical insights for universities navigating the pedagogical and regulatory challenges of AI-supported learning. Generative Artificial Intelligence Higher Education Student Achievement Observational Data Multilevel Modeling Academic Performance AI Governance Readability Metrics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 22 Mar, 2026 First submitted to journal 19 Mar, 2026 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-9053629","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":626620502,"identity":"693a462b-0d58-414e-bd4c-dce9f51b1b5d","order_by":0,"name":"Mareike Mueller","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYNCCAgsGBvYGGC8RyGIjpMVAgoGB5wDJWiQSYDwQA48W3QbuNIkPBhJy5jNfJ374uccmmp89ufEDQ5kNTi1mB3i3Sc4wkDCWuZ27WbLnWVruzJ6HzRIM59LwapHmMZBInCGdu0Ga4cDh3A03EhskGNsO49fyx0Cifobk2c2/QVr230hs/sHY9h+/FqD3EyQkQAyQLRKJbUBbDuDWcph3s2WPgYThDJ7cbZY9B9JyZ5x52GaRcC4Zt5bjvRtv/KiwkZdgP7v5xo8DNrn97emPb3wos8OphYEZq2gCbg2jYBSMglEwCogAAEajVekIa1WJAAAAAElFTkSuQmCC","orcid":"","institution":"Hochschule Macromedia","correspondingAuthor":true,"prefix":"","firstName":"Mareike","middleName":"","lastName":"Mueller","suffix":""},{"id":626620507,"identity":"74acb127-8f7e-4a37-a97a-57e9c20362f4","order_by":1,"name":"René Arnold","email":"","orcid":"","institution":"Hochschule Macromedia","correspondingAuthor":false,"prefix":"","firstName":"René","middleName":"","lastName":"Arnold","suffix":""},{"id":626620511,"identity":"4851c8ae-fc21-4163-99c5-039bf9f95c0d","order_by":2,"name":"Stefan Wagenpfeil","email":"","orcid":"","institution":"PFH Private University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Wagenpfeil","suffix":""}],"badges":[],"createdAt":"2026-03-06 20:08:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9053629/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9053629/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107422957,"identity":"ea64728f-bd65-4cd6-888c-9dea55201c5d","added_by":"auto","created_at":"2026-04-21 10:50:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":425863,"visible":true,"origin":"","legend":"","description":"","filename":"GenerativeAIUseDoesNotYetAlterStudentAchievementV2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9053629/v1_covered_499cef2f-8d92-4574-b3a7-20824cbe5d60.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Grade Expectations: Generative AI Use Does Not (Yet) Alter Student Achievement","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-educational-integrity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijei","sideBox":"Learn more about [International Journal for Educational Integrity](https://edintegrity.biomedcentral.com/)","snPcode":"40979","submissionUrl":"https://submission.springernature.com/new-submission/40979/3","title":"International Journal for Educational Integrity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Generative Artificial Intelligence, Higher Education, Student Achievement, Observational Data, Multilevel Modeling, Academic Performance, AI Governance, Readability Metrics","lastPublishedDoi":"10.21203/rs.3.rs-9053629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9053629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid diffusion of generative artificial intelligence (AI) in higher education has reshaped academic practices, yet its implications for transparency, authorship, and academic performance remain insufficiently understood. This study analyzes 4,489 student projects submitted between 2022 and 2025 to investigate the relationship between self-reported AI use, linguistic characteristics of student writing, and academic achievement. Using mixed-effects regression models and a comprehensive institutional dataset, we assess to what extent declared AI use, overall academic achievement, demographic variables, and text readability metrics predict project grades. The findings show that declared AI use is not systematically associated with higher or lower marks once contextual factors are controlled for, while overall performance, attempt number, linguistic complexity, and submission length emerge as robust predictors. The results highlight the importance of nuanced, evidence-based institutional policies that balance ethical AI integration with the safeguarding of academic integrity. By offering a large-scale empirical examination of declared AI usage in authentic coursework, this study contributes critical insights for universities navigating the pedagogical and regulatory challenges of AI-supported learning.\u003c/p\u003e","manuscriptTitle":"Grade Expectations: Generative AI Use Does Not (Yet) Alter Student Achievement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 10:50:36","doi":"10.21203/rs.3.rs-9053629/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"16344565462742500112860118518625544679","date":"2026-05-11T14:19:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T23:31:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T23:27:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T02:45:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal for Educational Integrity","date":"2026-03-19T11:28:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-for-educational-integrity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijei","sideBox":"Learn more about [International Journal for Educational Integrity](https://edintegrity.biomedcentral.com/)","snPcode":"40979","submissionUrl":"https://submission.springernature.com/new-submission/40979/3","title":"International Journal for Educational Integrity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18fd63af-efeb-40a0-b72d-b70baa2f6a1e","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"16344565462742500112860118518625544679","date":"2026-05-11T14:19:47+00:00","index":27,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T10:50:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 10:50:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9053629","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9053629","identity":"rs-9053629","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}