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This study defines machine translation literacy for translation education and builds a diagnostic scale to measure students’ proficiency. Using a literature review and case studies, the study proposes an initial framework. Data from 389 undergraduate and graduate translation students in South Korea are analyzed with EFA and CFA, yielding a robust six-factor model covering risk management, verification and correction, basic and operational knowledge, flexibility, and transparency.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/development-and-validation-of-machine-translation-literacy-scale-for-translation-education/594318/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/development-and-validation-of-machine-translation-literacy-scale-for-translation-education/594318.png","ImageObject",300,407,{"name":92,"@type":93},"Kurz","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-21","2026-10-15",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does the study aim to develop and validate?","Question",{"text":112,"@type":113},"It develops and validates a diagnostic scale for assessing students’ machine translation literacy within translation education.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How was the machine translation literacy model validated?",{"text":117,"@type":113},"The study collected data from 389 translation students in South Korea and analyzed it using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).",{"name":119,"@type":110,"acceptedAnswer":120},"What factors are included in the final machine translation literacy model?",{"text":121,"@type":113},"The six-factor model includes risk management, verification and correction, basic knowledge, operational knowledge, flexibility, and transparency.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},594318,1792210767,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},2336478945635,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Australian Journal of Applied Linguistics ISSN 2209-0959  \n[https://www.castledown.com/journals/ajal/](https://www.castledown.com/journals/ajal/)  \nAustralian Journal of Applied Linguistics, 8 (5), 102789 (2025)  \n[https://doi.org/10.29140/ajal.v8n5.102789](https://doi.org/10.29140/ajal.v8n5.102789)  \nDevelopment and validation of machine translation literacy scale for translation education  \nJUNHO LEEa (1st author)  SOWON AHNb (co-author)   \nYEONG-HOUN YIc (corresponding author)   \naChung-Ang University, Seoul, Korea  \nbSeoul National University of Science and Technology, Seoul, Korea cKorea University, Seoul, Korea  \nAbstract  \nThe rise of machine translation demands a fundamental shift in both translators’ roles and educational approaches. However, translation education research and practice have struggled to keep pace with the latest developments. To bridge the pedagogical gap, this study conceptualises machine translation literacy within the context of translation education and develops a diagnostic scale to assess students’ proficiency in machine translation literacy. Drawing on a review of existing literature and case studies on machine translation literacy applications in translation pedagogy and related disciplines, this study establishes an initial framework for machine translation literacy. To validate the structure and reliability of a proposed model for machine translation (MT) literacy, data were collected from 389 undergraduate and graduate translation students in South Korea and analysed using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). The analysis yielded a robust six-factor model encompassing risk management, verification and correction, basic knowledge, operational knowledge, flexibility, and transparency. This study provides a reliable assessment model for evaluating students’ machine translation literacy and lays the groundwork for the translation education planning at both macro and micro levels. Additionally, it positions machine translation literacy as an essential component within the broader theoretical framework of translation education research and serves as a foundation for expanding research into interdisciplinary domains.  \nKeywords: Machine translation, machine translation literacy, translation education, assessment tool, EFA, CFA  \nCopyright © 2025  \nJunho Lee, Sowon Ahn & Yeong-Houn Yi  \nIntroduction  \nWith the rapid advancement of information and communication technologies, the generation of digital data has grown at an unprecedented rate. In 2020 alone, it was estimated that humanity produced nearly 300,000 terabytes of data daily (DePalma, 2020, pp. 28–29). This immense volume and the diverse nature of the content have made it increasingly difficult to address translation needs through human effort alone or by relying solely on machine translation. Asa result, there is an urgent necessity for effective collaboration between human expertise and machine capabilities.  \nAt the same time, advancements in machine translation and the automation of translation workflows have established machine translation post-editing (MTPE) as a dominant pillar in the global translation market. Consequently, the industry has undergone noticeable bipolarization. According to recent studies (Bowker, 2021; Olohan, 2021; Pym & Torres-Simón, 2021), translation services are increasingly divided between the bulk market, driven by MTPE and Computer-Assisted Translation (CAT), and the premium market, where human creativity and linguistic artistry remain indispensable. This shift is further evidenced by the growing reliance on machine translation among Language Service Providers (LSPs) . In Europe, for instance, the majority of LSPs were already utilising machine translation by 2018 (ELIS Report, 2018), with adoption rates rising to over 70% by 2024 (ELIS Report, 2024) . Reflecting this evolution, European Master’s in Translation—tasked with overseeing the master’s programme in translation f","cbCaijjcjGq2tbf8","https://ap.wps.com/l/cbCaijjcjGq2tbf8","pdf",532409,23,"English","# Introduction\n## Machine translation literacy and educational needs\n## Development of the diagnostic scale\n## Validation approach and factors\n## Implications for translation education","[{\"question\":\"What does the study aim to develop and validate?\",\"answer\":\"It develops and validates a diagnostic scale for assessing students’ machine translation literacy within translation education.\"},{\"question\":\"How was the machine translation literacy model validated?\",\"answer\":\"The study collected data from 389 translation students in South Korea and analyzed it using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).\"},{\"question\":\"What factors are included in the final machine translation literacy model?\",\"answer\":\"The six-factor model includes risk management, verification and correction, basic knowledge, operational knowledge, flexibility, and transparency.\"}]","Development and validation of machine translation literacy scale for translation education | PDF",1792078935,58]