L-VEIGe: Development of L2 Vocabulary Learning Support System with Suggestibility of Error by Image Generation

L-VEIGe: Development of L2 Vocabulary Learning Support System with Suggestibility of Error by Image Generation | 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 L-VEIGe: Development of L2 Vocabulary Learning Support System with Suggestibility of Error by Image Generation Kazuki Sugita, Wen Gu, Koichi Ota, Shinobu Hasegawa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3869190/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 Vocabulary learning has long been taught as a basis for L2 English language learning. The use of visual clues is a widely recognized method in vocabulary learning. Several research have applied automatic image caption generation, invented with computer vision and natural language processing, to English vocabulary learning. However, these vocabulary learning systems mainly use correct answers and their corresponding images. On the other hand, in English vocabulary learning, Fossilization is a problem where errors become difficult to correct and become established through repeated errors by learners. While the use of images has been effective in traditional English vocabulary learning, there has been little research focusing on learners' incorrect answers. In this research, we intentionally created situations where learners are likely to give incorrect answers and constructed an English vocabulary learning support system L-VEIGe (Learning-Vocabulary Error Image Generation) that generates images in response to learners' incorrect answers to promote effective introspection and eliminate repeated errors. An evaluation experiment targeting graduate students who are second language English learners revealed that a proposed method effectively prevents repetitive errors compared to a method without image generation. L2 Vocabulary Learning Image Generation Question Generation Suggestibility of Error Full Text Additional Declarations No competing interests reported. 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. 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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-3869190","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271931642,"identity":"849f0e09-2bd9-4db6-9959-97ce46415cba","order_by":0,"name":"Kazuki Sugita","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIie3RMWqEQBTG8bcsZJsR21kCeoW3DFgZcpUnwqRNuaVhwW0Cae1yBWEv8IYBbeYQpkla05kqMZuwpVoGMr8Bq/nDNwjgeX/R5mg6wjQKgcfzg6YT4XLs91pti++EliSSkm3lbFbzgstncUF0HZRrpYx9MR8DxGEBr91UgmxYBeVVlHCDVhDsKoY7nExWBeVBKVTCjHYctqoBtJwcdgC0QSmzU9H2ZiC4nU2ggd1D5TCrwSGPw7LZBJ3Iod+TkuzurdAyr+zMW+Lnt3Yg/IzCqj29D2l683R81N3ksAvJ5y/AWuhlBYz/49emWZh4nuf9E19hK1L8u8XJ5wAAAABJRU5ErkJggg==","orcid":"","institution":"Japan Advanced Institute of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Kazuki","middleName":"","lastName":"Sugita","suffix":""},{"id":271931643,"identity":"96d43f9c-534d-475d-a2eb-2e2c8071236e","order_by":1,"name":"Wen Gu","email":"","orcid":"","institution":"Japan Advanced Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Gu","suffix":""},{"id":271931644,"identity":"f08d21c2-bd95-422b-aaef-b2ec83c79555","order_by":2,"name":"Koichi Ota","email":"","orcid":"","institution":"Japan Advanced Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Koichi","middleName":"","lastName":"Ota","suffix":""},{"id":271931645,"identity":"a7ae6ff1-f292-4a35-9f49-ef6f61714be9","order_by":3,"name":"Shinobu Hasegawa","email":"","orcid":"","institution":"Japan Advanced Institute of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shinobu","middleName":"","lastName":"Hasegawa","suffix":""}],"badges":[],"createdAt":"2024-01-16 08:14:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3869190/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3869190/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62589480,"identity":"139b8ecb-ce3d-48ac-9dfc-3cca6bc7d778","added_by":"auto","created_at":"2024-08-16 07:44:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4944933,"visible":true,"origin":"","legend":"","description":"","filename":"rereresubmitKazukiSugitaAsianPacificJournalofSecondandForeignLanguageEducation..pdf","url":"https://assets-eu.researchsquare.com/files/rs-3869190/v1_covered_7ffbdde2-bbe3-41b8-b15b-9bad968bcaf9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"L-VEIGe: Development of L2 Vocabulary Learning Support System with Suggestibility of Error by Image Generation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"L2 Vocabulary Learning, Image Generation, Question Generation, Suggestibility of Error","lastPublishedDoi":"10.21203/rs.3.rs-3869190/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3869190/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVocabulary learning has long been taught as a basis for L2 English language learning. The use of visual clues is a widely recognized method in vocabulary learning. Several research have applied automatic image caption generation, invented with computer vision and natural language processing, to English vocabulary learning. However, these vocabulary learning systems mainly use correct answers and their corresponding images. On the other hand, in English vocabulary learning, Fossilization is a problem where errors become difficult to correct and become established through repeated errors by learners. While the use of images has been effective in traditional English vocabulary learning, there has been little research focusing on learners' incorrect answers. 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