Machine Translation in Language Learning: A Systematic Review

Machine Translation in Language Learning: A Systematic Review | 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 Systematic Review Machine Translation in Language Learning: A Systematic Review Guillermo Barrera Gómez, Emma Patricia Mercado, Alexandro Escudero-Nahón, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9283782/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The rapid development of Machine Translation (MT) integrated with Artificial Intelligence (AI) has significantly influenced second and foreign language education, prompting renewed debate about their pedagogical position. This systematic review examines research published in the period of 2016 to 2025 on the use of MTs in formal language learning contexts. Guided by the PRISMA 2022 framework, a structured search was conducted in the Dimensions database using predefined inclusion and exclusion criteria. Eighteen open-access studies written in English or Spanish were selected for analysis. The studies were analyzed based on research purposes, methodological designs, technologies investigated, learning domains, educational settings, and key findings. The results indicate a growing scholarly interest in MTs and AI tools, particularly in relation to L2 writing development, learner autonomy, and pedagogical integration. Most studies adopt qualitative or theoretical approaches, focusing on learners’ and teachers’ perceptions, attitudes, and ethical considerations highlighting a lack of strong empirical evidence. MTs are conceptualized as mediational resources rather than replacements for instructions, offering affordances such as writing support, translanguaging practices, and increased access to linguistic input. However, the literature also reports challenges, including overreliance on technology, concerns about linguistic accuracy, and uncertainty regarding effective instructional use. The review concludes that the educational value of technology depends largely on guided, reflective, and pedagogically informed integration. Limitations include the small number of studies, uneven geographical distribution, and a predominance of perception-based research. Future research should employ stronger empirical and longitudinal designs to better understand the impact of MT on language development over time. Second Language Learning Educational Technology Machine Translation Artificial Intelligence Figures Figure 1 Figure 2 Figure 3 Introduction The advances in technology have shaped the interaction among humans and changed communication significantly [ 27 ]. Technologies have also transformed many activities, such as education, from traditional perspectives to a more modern approach. One of those activities in education that has received more attention is the learning of a second language (L2). Many authors have explored the interaction between technologies and foreign language (FL) learning, particularly the use of Machine Translation (MT). As Stapleton [ 26 ] reports, L2 students started using MT, especially when it became free, even though the perception was negative regarding the accuracy. However, with the arrival of neural networks, that perception has improved to a more positive one, and students now use these tools for language activities like writing and reading, and now, even speaking [ 25 ]. Though translation as a methodological approach to learn a language stopped being used and replaced by a direct method [ 26 ], the new technologies influence the way to learn a second language because students have access to them and constantly consult translation tools like Google Translate [ 9 ]. Therefore, the questions of whether to integrate these technologies in class and whether students learn or not from them arise. As Resende & Way [ 25 ] state, though translation and MTs are not generally approved, there is a widespread use of these tools. In studies carried out by Gokgoz-Kurt [ 11 ], it was analyzed how MT could be helpful didactically by correcting the errors in the texts generated by MTs. Though concerns regarding the consequences of linguistic features appeared, there has been more interest in exploring how MTs enhance learning, particularly writing skills [ 7 ]. There have been different studies and systematic reviews that have tried to synthesize the research on the impact of MT in language learning particularly to assist language teachers [ 21 ], to try to find a compatibility of MT in language learning [ 13 ], as well as to identify the main users, theories, attitudes and how the integration of MTs is performed [ 7 ]. As MT tools are constantly diversifying and introducing always the most advanced systems, like Artificial Intelligence (AI), it is always necessary to update and map the evidence found. The purpose of this systematic review is to examine and synthesize recent research on the use of MTs and related technologies in L2 and FL education. Specifically, the review analyzes how MT has been conceptualized, implemented, and investigated in formal educational contexts, focusing on research purposes, methodological approaches, learning domains, and educational settings. By focusing on studies published between 2016 and 2025, this review aims to identify trends, pedagogical affordances, and challenges associated with MT use, as well as to highlight gaps in the existing literature. The review pretends to contribute to a clearer understanding of the role of MT in language teaching and learning. Therefore, this systematic review attempts to answer the following research questions: Q1: What are the main characteristics of research on MT in L2, and FL education published between 2016 and 2025, in terms of research purposes, study designs, technologies examined, and learning domains? Q2: What pedagogical affordances and challenges of MT tools are reported for language learners and teachers in formal education contexts? Method Considering the recommendations of the 2020 PRISMA guidelines [ 22 ], there are different elements to report systematic reviews and to evaluate the reliability and applicability of the findings. The search was done through the Dimensions database using the string “Machine Translation AND language learning AND pedagogy”. The Dimensions database was used because it hosts a global research database with integrated Artificial Intelligence (AI) to accelerate the interpretation process [ 8 ]. In recent studies, it has been proven that Dimensions has a wider coverage than other databases such as Web of Science or Scopus, particularly in social sciences and humanities [ 26 ]. The next step was to determine the inclusion and exclusion criteria to select the articles (Table 1 ). The Dimensions database triggers an Excel file with related information to the articles found, including DOI, title, abstract, year of publication, authors, countries, and other elements such as volume, pages, etc. The focus was on articles that had a clear method regarding the use of machine translation or technology in language teaching. No exclusion regarding the age or level of learners was made. The period was set from 2016 to 2025 to provide the most recent trends, and to focus on the period where AI has acquired a more relevant presence. Although the inclusion only considers articles written in English or Spanish, no location was excluded for a more global coverage. Table 1 Eligibility criteria Criteria Inclusion Exclusion Type of document Articles, book chapters books Book reviews, comment studies without clear method Accessibility Open access Paid Population Language students and teachers Not related to the area of linguistics Period 2016–2025 Prior to 2016 Language English and Spanish Other languages 2.1 Study selection As a result of the search, 43 articles were retrieved. Seven articles were removed because they were written before 2016 (n = 36). Next, inclusion and exclusion criteria were applied, and 11 articles were removed for not complying with the requirements (n = 25); finally, from the remaining articles, seven were removed because they were not open access (n = 18) as shown in Fig. 1 . The articles for synthesis were selected for their relevance to the language learning topic using technologies. The categories to classify them included purpose, design of study, technologies used, main domain, country, subjects of study, and findings (Table 2 ). The classification was captured and processed in Excel. Table 2 List of reviewed articles based on technologies for language learning No. Study ID Purpose Design Technologies Learning domain Country Subjects Finding 1 [ 23 ] To research on technologies in L2 writing Empirical, reviews and descriptive essays Multimodal composing, data-driven learning, translation software, computer assisted communication, social networks, corpus based learning, e-feedback L2 writing, language learning Various Not specified Technology is transforming L2 writing offering various composition, pedagogy and research approaches 2 [ 18 ] To explore online MT for English students as a foreign language Mixed: surveys and interviews Online MTs MT in EFL Japan 10–15 EFL students Theoretical and methodological background that will show specific MT affordances for EFL 3 [ 15 ] How English students use MT in formal education and everyday life Qualitative: focus groups and interviews MT tools Multilingual pedagogies, translanguaging Northern Ireland 28 students / 14 teachers MT permeates learning and communication 4 [ 17 ] How human-MT interaction balances quality and costs and propose innovation for bilingual pedagogy Theoretical-analytical focused on text strata and corpora building NMT; AI; corpus building Translation, language learning, bilingual pedagogy China N/A NMT has reconfigured language learning and industry; an adequate human-MT interaction maximizes efficiency and mantains quality 5 [ 3 ] To explore language teachers perspectives on MT integration in teaching Qualitative: interviews MTs; ChatGPT; ADAPT framework Language teaching in higher education New Zealand Four teachers Moderate MT acceptance; ADAPT elements used in teaching 6 [ 14 ] To analyze the impact of MT in language courses and proposing an alternative metacognitive approach Conceptual chapter Voice and text MT engines; ChatGPT for self-learning Japanese as L2 in higher education Japan Intermediate-advanced japanese learners The presence of MT weakens the communicative approach; ChatGPT may support self-learning 7 [ 10 ] To examine how AI technologies re-configure cultural concepts in translation and linguistics and how MT guide to intercultural understanding Case studies; mixed approach MTs; AI language learning platforms Linguistic education, translation, intercultural communicative competence N/A MT and language learning platform users AI improves linguistic accuracy and makes the cultural contents easier 8 [ 28 ] To explore MT use and perception in Language learning Quantitative MT tools Language learning in middle school, foreign programs and immersion Japan Three cohorts of middle school students MT use varies in frequency and type as the learners progress; MT may be positive 9 [ 6 ] To analyze EFL learners towards MT to learn English Surveys Google Translate and other MTs EFL, L2 acquisition, MT pedagogy Algeria 80 students MT useful to learn English; Google Translate favours the learning of vocabulary but its integration has to be careful 10 [ 19 ] To assess ChatGPT efficieny as a tool for teachers to improve Arabian-German translations in L2 learners Experimental with control group ChatGPT as translator assistant L2 translation learning Jordan Arabian-German translation students Users of ChatGPT overcame the control with improvements in sentence structure, lexical choosing, grammar accuracy and idiom translation but shows minor errors 11 [ 24 ] To describe the integration of AI for English learning, benefits, limitations and trends Review article AI tools EFL, CALL and AI in education Thailand Previous studies on EFL learners AI is revolutionizing linguistic education thorugh personalized experiences; it presents innovative suggestions 12 [ 16 ] To research how translation learning respond transformations towards an emerging pedagogy Theoretical-pedagogical study MT tools Translation learners, professional competencies Poland Translation students and linguistic providers The industry demands technological competencies and the education has to move towards autonomy, critical thinking, collaboration and use of technologies 13 [ 4 ] Systematic review of the integration of technologies for English learning Systematic review Chatbots, voice recognition, MT tools, Automatic Evaluation Tools, GenAI ESL/EFL, language skills Multiple country study 55 articles GenAI tools may transform and enhance linguistic skills offering personalized and dynamic environments with limitations and challenges 14 [ 1 ] To analyze pros and cons of integrating MT in translation teaching from the perspective of students and teachers Experimental method: interviews MT tools Pedagogy in translation Iran 100 students of translation Students consider MT useful and they feel eager to use them; teachers acknowledge their benefits if the outcome quality improves 15 [ 12 ] To understand how EFL students relate to a chatbot when writing argumentative essays Qualitative study Chatbot Argumate; online resources, MTs EFL argumentative writing; pedagogy on chatbot assisted writing China Five students Students formed a learning community with Argumate using multiple tools; collaboration was conditioned by task rules, genre conventions and additional scaffolding 16 [ 5 ] To research on perceptions and uses of generative AI in degree students of translation Surveys ChatGPT, other AI tools and MT Translation learning, multilingual competence, generative AI Spain Degree students of translation Students show neutral attitudes towards Chat GPT efficiency in translation, writing, and language learning; they focus on human centered AI 17 [ 2 ] To examine how university students of different languages and proficiency levels use MT for L2 writing Quantitative (survey) / qualitative (open ended questions) MT L2 writing New Zealand 150 university students / 12 teachers Students use MT for L2 writing and perceive it as helpful 18 [ 20 ] To clarify the notions of translation pedagogy and its relevance for Translation in Language Learning Theoretical N/A ESL and EFL Malaysia N/A Translation is a useful tool in ESL/EFL classrooms; teachers lack knowledge and competencies in translation pedagogy; it proposes an ELT Translation Framework Findings and discussion 4.1 Purpose The main purpose of all articles was to determine the use of technologies in linguistic education. From all the results, the most relevant topic was regarding how technologies, including MT, affect or integrate into L2 learning (77.8%). This result confirms what Gokgoz-Kurt [ 11 ] says about MT tools, which are popular among L2 learners, especially in aspects of writing development. The rest of the studies (n = 4; 22.22%) were focused on how technologies in general integrate in education, covering different disciplines other than L2, given that the advances in technology are changing the processes in industries and they are having a deep impact on translation and language education [ 10 ]. Therefore, as Munday et al. (2022) as cited in [ 19 ] state, there is more interest in investigating the connection between technology and language learning. This concentration suggests a potential imbalance in the literature, with limited attention given to other language skills such as speaking or listening. 4.2 Design The main methods used in the studies show a trend towards qualitative and theoretical research. Most of the studies (n = 8; 44.44%) used qualitative methods such as interviews, surveys and focus groups to explore the pedagogical and ethical implications of using the technologies and AI in educational environments [ 5 ]. A total of 38.9% (n = 7) is classified in theories, concepts and reviews focusing on the development of policies and understanding the function of technologies in education. The interest is supported by the fact that the enrollment in foreign language courses has dropped (Lusin et al., 2023, as cited in [ 14 ]). Finally, the other designs were quantitative/experimental, mixed methods and case studies, each one representing 16.7% of the results. These studies mainly focus on the interaction between students and the technologies for language learning development [ 28 ]. This distribution reveals a strong reliance on qualitative and theoretical approaches, suggesting a lack of robust empirical and experimental research in the field. 4.3 Technologies From the studies analyzed, there were quite a few technologies explored (Fig. 2 ). Various authors (44.44%) analyze different technologies in the same studies. As it can be observed from the chart, the main technologies identified in the results focus mainly on MTs, covering a range of options such as NMT, Google Translate, and other MT tools (n = 15), to identify the potential pedagogies in language education [ 6 ]. The studies also show the growing presence of AI (n = 8), which have brought a considerable change in education [ 4 ], for example, the use of ChatGPT to translate and autonomous learning, and specialized chatbots like Argumate (43.75%) to understand how students interact with such technology, specifically when composing essays [ 12 ]. Other technologies mentioned cover Computer Assisted Communications (n = 3), the use of chatbots (n = 2), and corpus (n = 2) to learn. Finally, some studies analyzed other technologies like social networks, electronic feedback, voice recognition, and automated evaluation. These show how the diverse technologies are being used in the pedagogies for more personalized and dynamic learning. However, this diversity may also reflect a lack of consistency in pedagogical approaches across studies. 4.4 Learning domain The focus of most of the studies is language acquisition, specifically English, German, and Japanese as a Foreign or Second Language (EFL/ESL) (n = 14). Fewer studies concentrated on the pedagogy of translation (n = 5). All of them attempt to determine how the technology is mediating the learning of languages [ 18 ]. The studies explore different levels and education contexts, including middle school, higher education, and immersion programs. Research specializes in areas like L2 writing, argumentative writing, intercultural development, and translingualism mediated by technology, which, altogether, offer new opportunities in teaching and learning [ 26 ]. Other domains analyzed in the studies were multilingual pedagogies (n = 1) and intercultural communicative competencies (n = 3). This distribution indicates a strong emphasis on writing-related skills, while other areas such as speaking and listening remain underrepresented in the literature. 4.5 Distribution by country The studies analyzed show a high concentration in Asia, with a 50% of the total. In this region, Japan occupies the first place (n = 3), with studies regarding the use and perception of MT for students learning English as a foreign language (18; 28]; China (n = 2), where the studies were focused on the aspects of text stratification and corpus construction [ 17 ], as well as the use of chatbots to engage foreign language students [ 12 ]. The rest are contributions in Jordan where they studied ChatGPT as a tool to enhance Arabic-to-German translation skills [ 19 ]; Thailand, with a study on the integration of Artificial Intelligence in English language learning [ 24 ]; and Iran, where the study focused on the integration of MT in translation teaching [ 1 ]. Finally, Malaysia, where the conceptual study focuses on the understanding of translation knowledge and competencies and how it can help language learning [ 20 ]. Europe reaches 16.7% of the studies, mainly in Northern Ireland, with a study exploring how multilingual students learning English use MT in their formal education [ 15 ]; Spain where they study the perceptions of students using generative AI [ 5 ]; and Poland, which study offers a more theoretical perspective on translation pedagogy in the digital age [ 16 ]. Two studies were developed in New Zealand by Alm & Watanabe [ 2 , 3 ], where they studied the perceptions of L2 students, in one study, and teachers’ perceptions, in the second one, by using MT in writing. Finally, Algeria presents one study that focuses on the innovation of pedagogy in language learning by using MTs [ 6 ]. The rest of the studies are systematic reviews from multiple nations or not specified (Fig. 3 ). It is noteworthy that no studies reflect research in the Americas. This uneven geographical distribution suggests that research on MT in language learning is heavily concentrated in specific regions, potentially limiting the generalizability of findings across diverse educational contexts. 4.6 Subjects of study From the studies analyzed, nine had quantitative or qualitative studies where mainly students and teachers were included (Table 2 ). Most of the studies (n = 7) focused on English learning as a second or foreign language; the other languages were Chinese, French, German, Japanese, and Spanish, with two studies focused on each. A total of ≈ 418–423 participants were mainly adults between 18 and 30 years old; the rest, 152 in total, were students between 12 and 18 years old. Four studies explored the participation of teachers with a total of 62 participants. From all the teachers, the majority (n = 30) speak Persian; English occupies the second position (n = 11); followed by Chinese, French, and Spanish (n = 4 each); and finally German, Japanese, and Polish (n = 3 each). The origin and first language of the students are also diverse. Most of the participants were Japanese, with a total of ≈ 134–139 students; followed by English, 150 students; Arabic, 130 students; Persian, 100 students; Catalan and Spanish, 32. In a lesser extent, speakers of Lithuanian, Russian (n = 3, each), Romanian (n = 2), Turkish, Tagalog, Portuguese, Punjabi, German, and Italian (n = 1, each) are reported. It is noteworthy to mention that Kelly & Hou [ 15 ] report a total of 11 bilingual students from the 28 participants. This distribution suggests that research is heavily concentrated on adult learners and English as the target language, potentially limiting insights into younger populations and less commonly taught languages. Table 2 Analysis of subjects in the studies Study Age Level of study Mother language Target language Sample size [ 28 ] ≈ 15–18 High school Japanese English 124 students [ 15 ] 12–18 Middle-school/Teachers Polish, Arabic, Hungarian, Romanian, Portuguese, Punjabi, Lithuanian, Russian, German, Italian, Tagalog, French, Turkish English 28 students 14 teachers [ 5 ] 18–25 (n = 31) 26 y 30 (n = 1) English Degree Catalan/Spanish English 32 students [ 2 ] \(\:>\) 18 Languages and Culltures tertiary English Chinese, French, German, Japanese, Spanish 12 teachers 150 students [ 18 ] 18–21 English courses Japanese English 10–15 [ 1 ] 22–30 Translation degree level Persian English 100 students 30 teachers [ 6 ] \(\:>\) 18 EFL university students Arabic English 80 students [ 19 ] 20–23 Undergraduate in German Language and Literature Arabic German 40 students [ 12 ] 19–21 EFL writing class Chinese English 5 students [ 3 ] - Languages and Cultures Department French, Spanish, German, English, Chinese, Japanese French, Spanish, Chinese, Japanese 6 teachers 4.7 Findings The reviewed studies indicate that the technologies are changing L2 learning in different ways. Singh et al. [ 26 ] state that the technologies have surpassed the notion of learning from a simple text as the only visual representation, opening up new opportunities for learning and teaching. From the different technologies analyzed, MT and AI tools are the ones more analyzed the most in the studies. For instance, Moore et al. [ 18 ] report that MT offers a theoretical and methodological background for specific affordances for English as a foreign language, representing a potential in pedagogy. Other authors like Kelly & Hou [ 15 ] indicate that MTs permeate learning and communication; therefore, they could be integrated into formal education. Regarding AI, studies show that they have reconfigured language learning and the language industry, and with an adequate interaction efficiency, quality can be maximized [ 17 ]. Finally, as Alm & Watanabe [ 2 ] state, it is evident that there is more acceptance of these technologies not only on behalf of the students, but also teachers who have integrated them in a cautious way in language teaching. However, this growing acceptance may also raise concerns about overreliance on technology and the potential reduction of critical language skills. Conclusions This systematic review shows research published in the period 2016–2025 regarding the use of MT and AI translation technologies in L2 and FL education. The findings indicate a sustained and growing interest in understanding how these technologies mediate language learning processes, particularly in relation to writing development, learner autonomy, and pedagogical practices. Across the different studies, MT and AI tools are not positioned as substitutes for instruction but as resources on which educational values depend, on how they are pedagogically integrated. The studies are predominantly qualitative and theoretical, with emphasis on the perceptions, attitudes, and ethical considerations. The review also analyzes how MTs and AI offer pedagogical affordances, including support for L2 writing, translanguaging, and access to linguistic input. The literature reports challenges related to overreliance on technology, potential impacts on linguistic accuracy and development, as well as uncertainty regarding appropriate instructional use. The teacher’s response suggests a shift from resistance towards cautious integration, emphasizing the need for guided, reflective, and metacognitive approaches. These findings highlight the role of educators in mediating technology use so that MTs function as a learning tool rather than a shortcut that prevents language development. This research, however, presents some limitations that need to be acknowledged. First, the number of included studies is relatively small and distributed across regions, languages, and educational contexts, with a strong concentration in Asian and English-dominant settings. Second, the reliance on open-access publications written in English and Spanish may have excluded relevant research in other languages or paid. Third, many of the results focus on perceptions and experiences rather than longitudinal or outcome-based measures of learning. Future research is needed in more diverse geographical representation, stronger empirical designs, and longitudinal studies that analyze how MT use influences language development over time and, of course, as Deng & Yu [ 7 ] recommend, to explore the design of MT integration to establish the objectives necessary for an adequate pedagogical approach. Overall, the findings underscore the need for a balanced and pedagogically informed approach to integrating MT and AI in language education. Declarations Acknowledgement This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. Funding This study was supported by 2025 Autonomous University of Queretaro FONFIVE funding (202515055). Data availability No datasets were generated or analysed during the current study. Ethics approval, consent to participate and consent to publish Not applicable. References Akbari Motlaq MD, Tengku Mahadi TS. Advantages and Disadvantages of Using Machine Translation in Translation Pedagogy from the Perspective of Instructors and Learners. Humanit Social Sci Reviews. 2020;8(4):121–37. https://doi.org/10.18510/hssr.2020.8414 . Alm A, Watanabe Y. (2022). Online Machine Translation for L2 Writing Across Languages and Proficiency Levels. Australian Journal of Applied Linguistics , 5 (3 Special Issue), 135–157. https://doi.org/10.29140/ajal.v5n3.53si3 Alm A, Watanabe Y. 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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-9283782","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":631677524,"identity":"658d3e41-d46a-4cb3-8305-141cbabaa60d","order_by":0,"name":"Guillermo Barrera Gómez","email":"data:image/png;base64,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","orcid":"","institution":"Autonomous University of Querétaro","correspondingAuthor":true,"prefix":"","firstName":"Guillermo","middleName":"Barrera","lastName":"Gómez","suffix":""},{"id":631677525,"identity":"7ee244d4-487e-43c8-aa9e-8d6d73191556","order_by":1,"name":"Emma Patricia Mercado","email":"","orcid":"","institution":"Autonomous University of Querétaro","correspondingAuthor":false,"prefix":"","firstName":"Emma","middleName":"Patricia","lastName":"Mercado","suffix":""},{"id":631677527,"identity":"487ae7d6-fd55-42d0-b258-29757f61f125","order_by":2,"name":"Alexandro Escudero-Nahón","email":"","orcid":"","institution":"Autonomous University of Querétaro","correspondingAuthor":false,"prefix":"","firstName":"Alexandro","middleName":"","lastName":"Escudero-Nahón","suffix":""},{"id":631677528,"identity":"5ea64f33-59be-43ff-b35e-f99b9daa9c34","order_by":3,"name":"Selene Maya-Ruiz","email":"","orcid":"","institution":"Autonomous University of Querétaro","correspondingAuthor":false,"prefix":"","firstName":"Selene","middleName":"","lastName":"Maya-Ruiz","suffix":""}],"badges":[],"createdAt":"2026-03-31 19:23:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9283782/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9283782/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108226694,"identity":"1e7cca65-1353-4d8f-a0a4-6c38daf0ec04","added_by":"auto","created_at":"2026-04-30 16:30:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":270937,"visible":true,"origin":"","legend":"\u003cp\u003eAdapted from PRISMA 2020 flow of study identification.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9283782/v1/7c707801e21fcadd1d3aaf91.png"},{"id":108226696,"identity":"754b7b5c-a3df-40e4-a006-0da52d2ef6fd","added_by":"auto","created_at":"2026-04-30 16:30:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127337,"visible":true,"origin":"","legend":"\u003cp\u003eTechnologies explored in the studies.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9283782/v1/bb6bc5eb513a5756b2fdef89.png"},{"id":108491721,"identity":"0255435e-7f80-4aed-b6c4-dbcb37cfe99a","added_by":"auto","created_at":"2026-05-05 09:55:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":478772,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal distribution of studies by country and region. Map generated with Formula Bot.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9283782/v1/2693aebe3c0f98408ed87954.png"},{"id":108803986,"identity":"f1421726-8938-40ff-b52d-7ff7d6acdb49","added_by":"auto","created_at":"2026-05-08 15:13:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1256908,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9283782/v1/efdf50ea-dcd1-4175-92d7-bb960e675941.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Translation in Language Learning: A Systematic Review","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe advances in technology have shaped the interaction among humans and changed communication significantly [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Technologies have also transformed many activities, such as education, from traditional perspectives to a more modern approach. One of those activities in education that has received more attention is the learning of a second language (L2). Many authors have explored the interaction between technologies and foreign language (FL) learning, particularly the use of Machine Translation (MT). As Stapleton [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] reports, L2 students started using MT, especially when it became free, even though the perception was negative regarding the accuracy. However, with the arrival of neural networks, that perception has improved to a more positive one, and students now use these tools for language activities like writing and reading, and now, even speaking [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThough translation as a methodological approach to learn a language stopped being used and replaced by a direct method [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], the new technologies influence the way to learn a second language because students have access to them and constantly consult translation tools like Google Translate [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, the questions of whether to integrate these technologies in class and whether students learn or not from them arise. As Resende \u0026amp; Way [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] state, though translation and MTs are not generally approved, there is a widespread use of these tools. In studies carried out by Gokgoz-Kurt [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], it was analyzed how MT could be helpful didactically by correcting the errors in the texts generated by MTs. Though concerns regarding the consequences of linguistic features appeared, there has been more interest in exploring how MTs enhance learning, particularly writing skills [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere have been different studies and systematic reviews that have tried to synthesize the research on the impact of MT in language learning particularly to assist language teachers [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], to try to find a compatibility of MT in language learning [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], as well as to identify the main users, theories, attitudes and how the integration of MTs is performed [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As MT tools are constantly diversifying and introducing always the most advanced systems, like Artificial Intelligence (AI), it is always necessary to update and map the evidence found.\u003c/p\u003e \u003cp\u003eThe purpose of this systematic review is to examine and synthesize recent research on the use of MTs and related technologies in L2 and FL education. Specifically, the review analyzes how MT has been conceptualized, implemented, and investigated in formal educational contexts, focusing on research purposes, methodological approaches, learning domains, and educational settings. By focusing on studies published between 2016 and 2025, this review aims to identify trends, pedagogical affordances, and challenges associated with MT use, as well as to highlight gaps in the existing literature. The review pretends to contribute to a clearer understanding of the role of MT in language teaching and learning.\u003c/p\u003e \u003cp\u003eTherefore, this systematic review attempts to answer the following research questions:\u003c/p\u003e \u003cp\u003eQ1: What are the main characteristics of research on MT in L2, and FL education published between 2016 and 2025, in terms of research purposes, study designs, technologies examined, and learning domains?\u003c/p\u003e \u003cp\u003eQ2: What pedagogical affordances and challenges of MT tools are reported for language learners and teachers in formal education contexts?\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eConsidering the recommendations of the 2020 PRISMA guidelines [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], there are different elements to report systematic reviews and to evaluate the reliability and applicability of the findings. The search was done through the Dimensions database using the string \u0026ldquo;Machine Translation AND language learning AND pedagogy\u0026rdquo;. The Dimensions database was used because it hosts a global research database with integrated Artificial Intelligence (AI) to accelerate the interpretation process [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In recent studies, it has been proven that Dimensions has a wider coverage than other databases such as Web of Science or Scopus, particularly in social sciences and humanities [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe next step was to determine the inclusion and exclusion criteria to select the articles (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Dimensions database triggers an Excel file with related information to the articles found, including DOI, title, abstract, year of publication, authors, countries, and other elements such as volume, pages, etc. The focus was on articles that had a clear method regarding the use of machine translation or technology in language teaching. No exclusion regarding the age or level of learners was made. The period was set from 2016 to 2025 to provide the most recent trends, and to focus on the period where AI has acquired a more relevant presence. Although the inclusion only considers articles written in English or Spanish, no location was excluded for a more global coverage.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEligibility criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInclusion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExclusion\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of document\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArticles, book chapters books\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBook reviews, comment studies without clear method\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccessibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOpen access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePaid\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLanguage students and teachers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot related to the area of linguistics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2016\u0026ndash;2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrior to 2016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanguage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish and Spanish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOther languages\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study selection\u003c/h2\u003e \u003cp\u003eAs a result of the search, 43 articles were retrieved. Seven articles were removed because they were written before 2016 (n\u0026thinsp;=\u0026thinsp;36). Next, inclusion and exclusion criteria were applied, and 11 articles were removed for not complying with the requirements (n\u0026thinsp;=\u0026thinsp;25); finally, from the remaining articles, seven were removed because they were not open access (n\u0026thinsp;=\u0026thinsp;18) as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe articles for synthesis were selected for their relevance to the language learning topic using technologies. The categories to classify them included purpose, design of study, technologies used, main domain, country, subjects of study, and findings (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The classification was captured and processed in Excel.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of reviewed articles based on technologies for language learning\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePurpose\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDesign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTechnologies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLearning domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSubjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eFinding\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo research on technologies in L2 writing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmpirical, reviews and descriptive essays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultimodal composing, data-driven learning, translation software, computer assisted communication, social networks, corpus based learning, e-feedback\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL2 writing, language learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVarious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNot specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTechnology is transforming L2 writing offering various composition, pedagogy and research approaches\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo explore online MT for English students as a foreign language\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMixed: surveys and interviews\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOnline MTs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMT in EFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u0026ndash;15 EFL students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTheoretical and methodological background that will show specific MT affordances for EFL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow English students use MT in formal education and everyday life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQualitative: focus groups and interviews\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMT tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMultilingual pedagogies, translanguaging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNorthern Ireland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e28 students / 14 teachers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMT permeates learning and communication\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHow human-MT interaction balances quality and costs and propose innovation for bilingual pedagogy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTheoretical-analytical focused on text strata and corpora building\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNMT; AI; corpus building\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTranslation, language learning, bilingual pedagogy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNMT has reconfigured language learning and industry; an adequate human-MT interaction maximizes efficiency and mantains quality\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo explore language teachers perspectives on MT integration in teaching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQualitative: interviews\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMTs; ChatGPT; ADAPT framework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLanguage teaching in higher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNew Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFour teachers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModerate MT acceptance; ADAPT elements used in teaching\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo analyze the impact of MT in language courses and proposing an alternative metacognitive approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConceptual chapter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVoice and text MT engines; ChatGPT for self-learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJapanese as L2 in higher education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntermediate-advanced japanese learners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eThe presence of MT weakens the communicative approach; ChatGPT may support self-learning\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo examine how AI technologies re-configure cultural concepts in translation and linguistics and how MT guide to intercultural understanding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase studies; mixed approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMTs; AI language learning platforms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLinguistic education, translation, intercultural communicative competence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMT and language learning platform users\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAI improves linguistic accuracy and makes the cultural contents easier\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo explore MT use and perception in Language learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuantitative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMT tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLanguage learning in middle school, foreign programs and immersion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eThree cohorts of middle school students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMT use varies in frequency and type as the learners progress; MT may be positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo analyze EFL learners towards MT to learn English\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurveys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGoogle Translate and other MTs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEFL, L2 acquisition, MT pedagogy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAlgeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80 students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMT useful to learn English; Google Translate favours the learning of vocabulary but its integration has to be careful\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo assess ChatGPT efficieny as a tool for teachers to improve Arabian-German translations in L2 learners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExperimental with control group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChatGPT as translator assistant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL2 translation learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJordan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eArabian-German translation students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUsers of ChatGPT overcame the control with improvements in sentence structure, lexical choosing, grammar accuracy and idiom translation but shows minor errors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo describe the integration of AI for English learning, benefits, limitations and trends\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReview article\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAI tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEFL, CALL and AI in education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThailand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePrevious studies on EFL learners\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAI is revolutionizing linguistic education thorugh personalized experiences; it presents innovative suggestions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo research how translation learning respond transformations towards an emerging pedagogy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTheoretical-pedagogical study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMT tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTranslation learners, professional competencies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTranslation students and linguistic providers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eThe industry demands technological competencies and the education has to move towards autonomy, critical thinking, collaboration and use of technologies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSystematic review of the integration of technologies for English learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSystematic review\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChatbots, voice recognition, MT tools, Automatic Evaluation Tools, GenAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eESL/EFL, language skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultiple country study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55 articles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGenAI tools may transform and enhance linguistic skills offering personalized and dynamic environments with limitations and challenges\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo analyze pros and cons of integrating MT in translation teaching from the perspective of students and teachers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExperimental method: interviews\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMT tools\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePedagogy in translation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e100 students of translation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStudents consider MT useful and they feel eager to use them; teachers acknowledge their benefits if the outcome quality improves\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo understand how EFL students relate to a chatbot when writing argumentative essays\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQualitative study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChatbot Argumate; online resources, MTs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEFL argumentative writing; pedagogy on chatbot assisted writing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eFive students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStudents formed a learning community with Argumate using multiple tools; collaboration was conditioned by task rules, genre conventions and additional scaffolding\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo research on perceptions and uses of generative AI in degree students of translation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurveys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChatGPT, other AI tools and MT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTranslation learning, multilingual competence, generative AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDegree students of translation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStudents show neutral attitudes towards Chat GPT efficiency in translation, writing, and language learning; they focus on human centered AI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo examine how university students of different languages and proficiency levels use MT for L2 writing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuantitative (survey) / qualitative (open ended questions)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL2 writing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNew Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e150 university students / 12 teachers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStudents use MT for L2 writing and perceive it as helpful\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo clarify the notions of translation pedagogy and its relevance for Translation in Language Learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTheoretical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eESL and EFL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMalaysia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTranslation is a useful tool in ESL/EFL classrooms; teachers lack knowledge and competencies in translation pedagogy; it proposes an ELT Translation Framework\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Findings and discussion","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Purpose\u003c/h2\u003e \u003cp\u003eThe main purpose of all articles was to determine the use of technologies in linguistic education. From all the results, the most relevant topic was regarding how technologies, including MT, affect or integrate into L2 learning (77.8%). This result confirms what Gokgoz-Kurt [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] says about MT tools, which are popular among L2 learners, especially in aspects of writing development. The rest of the studies (n\u0026thinsp;=\u0026thinsp;4; 22.22%) were focused on how technologies in general integrate in education, covering different disciplines other than L2, given that the advances in technology are changing the processes in industries and they are having a deep impact on translation and language education [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, as Munday et al. (2022) as cited in [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] state, there is more interest in investigating the connection between technology and language learning. This concentration suggests a potential imbalance in the literature, with limited attention given to other language skills such as speaking or listening.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Design\u003c/h2\u003e \u003cp\u003eThe main methods used in the studies show a trend towards qualitative and theoretical research. Most of the studies (n\u0026thinsp;=\u0026thinsp;8; 44.44%) used qualitative methods such as interviews, surveys and focus groups to explore the pedagogical and ethical implications of using the technologies and AI in educational environments [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A total of 38.9% (n\u0026thinsp;=\u0026thinsp;7) is classified in theories, concepts and reviews focusing on the development of policies and understanding the function of technologies in education. The interest is supported by the fact that the enrollment in foreign language courses has dropped (Lusin et al., 2023, as cited in [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]). Finally, the other designs were quantitative/experimental, mixed methods and case studies, each one representing 16.7% of the results. These studies mainly focus on the interaction between students and the technologies for language learning development [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This distribution reveals a strong reliance on qualitative and theoretical approaches, suggesting a lack of robust empirical and experimental research in the field.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Technologies\u003c/h2\u003e \u003cp\u003eFrom the studies analyzed, there were quite a few technologies explored (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Various authors (44.44%) analyze different technologies in the same studies. As it can be observed from the chart, the main technologies identified in the results focus mainly on MTs, covering a range of options such as NMT, Google Translate, and other MT tools (n\u0026thinsp;=\u0026thinsp;15), to identify the potential pedagogies in language education [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The studies also show the growing presence of AI (n\u0026thinsp;=\u0026thinsp;8), which have brought a considerable change in education [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], for example, the use of ChatGPT to translate and autonomous learning, and specialized chatbots like Argumate (43.75%) to understand how students interact with such technology, specifically when composing essays [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Other technologies mentioned cover Computer Assisted Communications (n\u0026thinsp;=\u0026thinsp;3), the use of chatbots (n\u0026thinsp;=\u0026thinsp;2), and corpus (n\u0026thinsp;=\u0026thinsp;2) to learn. Finally, some studies analyzed other technologies like social networks, electronic feedback, voice recognition, and automated evaluation. These show how the diverse technologies are being used in the pedagogies for more personalized and dynamic learning. However, this diversity may also reflect a lack of consistency in pedagogical approaches across studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Learning domain\u003c/h2\u003e \u003cp\u003eThe focus of most of the studies is language acquisition, specifically English, German, and Japanese as a Foreign or Second Language (EFL/ESL) (n\u0026thinsp;=\u0026thinsp;14). Fewer studies concentrated on the pedagogy of translation (n\u0026thinsp;=\u0026thinsp;5). All of them attempt to determine how the technology is mediating the learning of languages [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The studies explore different levels and education contexts, including middle school, higher education, and immersion programs. Research specializes in areas like L2 writing, argumentative writing, intercultural development, and translingualism mediated by technology, which, altogether, offer new opportunities in teaching and learning [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Other domains analyzed in the studies were multilingual pedagogies (n\u0026thinsp;=\u0026thinsp;1) and intercultural communicative competencies (n\u0026thinsp;=\u0026thinsp;3). This distribution indicates a strong emphasis on writing-related skills, while other areas such as speaking and listening remain underrepresented in the literature.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Distribution by country\u003c/h2\u003e \u003cp\u003eThe studies analyzed show a high concentration in Asia, with a 50% of the total. In this region, Japan occupies the first place (n\u0026thinsp;=\u0026thinsp;3), with studies regarding the use and perception of MT for students learning English as a foreign language (18; 28]; China (n\u0026thinsp;=\u0026thinsp;2), where the studies were focused on the aspects of text stratification and corpus construction [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], as well as the use of chatbots to engage foreign language students [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The rest are contributions in Jordan where they studied ChatGPT as a tool to enhance Arabic-to-German translation skills [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]; Thailand, with a study on the integration of Artificial Intelligence in English language learning [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; and Iran, where the study focused on the integration of MT in translation teaching [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Finally, Malaysia, where the conceptual study focuses on the understanding of translation knowledge and competencies and how it can help language learning [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Europe reaches 16.7% of the studies, mainly in Northern Ireland, with a study exploring how multilingual students learning English use MT in their formal education [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; Spain where they study the perceptions of students using generative AI [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; and Poland, which study offers a more theoretical perspective on translation pedagogy in the digital age [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Two studies were developed in New Zealand by Alm \u0026amp; Watanabe [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], where they studied the perceptions of L2 students, in one study, and teachers\u0026rsquo; perceptions, in the second one, by using MT in writing. Finally, Algeria presents one study that focuses on the innovation of pedagogy in language learning by using MTs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The rest of the studies are systematic reviews from multiple nations or not specified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It is noteworthy that no studies reflect research in the Americas. This uneven geographical distribution suggests that research on MT in language learning is heavily concentrated in specific regions, potentially limiting the generalizability of findings across diverse educational contexts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Subjects of study\u003c/h2\u003e \u003cp\u003eFrom the studies analyzed, nine had quantitative or qualitative studies where mainly students and teachers were included (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Most of the studies (n\u0026thinsp;=\u0026thinsp;7) focused on English learning as a second or foreign language; the other languages were Chinese, French, German, Japanese, and Spanish, with two studies focused on each. A total of \u0026asymp;\u0026thinsp;418\u0026ndash;423 participants were mainly adults between 18 and 30 years old; the rest, 152 in total, were students between 12 and 18 years old. Four studies explored the participation of teachers with a total of 62 participants. From all the teachers, the majority (n\u0026thinsp;=\u0026thinsp;30) speak Persian; English occupies the second position (n\u0026thinsp;=\u0026thinsp;11); followed by Chinese, French, and Spanish (n\u0026thinsp;=\u0026thinsp;4 each); and finally German, Japanese, and Polish (n\u0026thinsp;=\u0026thinsp;3 each). The origin and first language of the students are also diverse. Most of the participants were Japanese, with a total of \u0026asymp;\u0026thinsp;134\u0026ndash;139 students; followed by English, 150 students; Arabic, 130 students; Persian, 100 students; Catalan and Spanish, 32. In a lesser extent, speakers of Lithuanian, Russian (n\u0026thinsp;=\u0026thinsp;3, each), Romanian (n\u0026thinsp;=\u0026thinsp;2), Turkish, Tagalog, Portuguese, Punjabi, German, and Italian (n\u0026thinsp;=\u0026thinsp;1, each) are reported. It is noteworthy to mention that Kelly \u0026amp; Hou [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] report a total of 11 bilingual students from the 28 participants. This distribution suggests that research is heavily concentrated on adult learners and English as the target language, potentially limiting insights into younger populations and less commonly taught languages.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of subjects in the studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel of study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMother language\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTarget language\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR29\" 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\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePolish, Arabic, Hungarian, Romanian, Portuguese, Punjabi, Lithuanian, Russian, German, Italian, Tagalog, French, Turkish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 students\u003c/p\u003e \u003cp\u003e14 teachers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;25 (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003cp\u003e26 y 30 (n\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnglish Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCatalan/Spanish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 students\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026gt;\\)\u003c/span\u003e\u003c/span\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLanguages and Culltures tertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChinese, French, German, Japanese, Spanish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 teachers\u003c/p\u003e \u003cp\u003e150 students\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEnglish courses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJapanese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTranslation degree level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePersian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100 students\u003c/p\u003e \u003cp\u003e30 teachers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026gt;\\)\u003c/span\u003e\u003c/span\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEFL university students\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArabic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e80 students\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUndergraduate in German Language and Literature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArabic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGerman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40 students\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u0026ndash;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEFL writing class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChinese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 students\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLanguages and Cultures Department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFrench, Spanish, German, English, Chinese, Japanese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFrench, Spanish, Chinese, Japanese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 teachers\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Findings\u003c/h2\u003e \u003cp\u003eThe reviewed studies indicate that the technologies are changing L2 learning in different ways. Singh et al. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] state that the technologies have surpassed the notion of learning from a simple text as the only visual representation, opening up new opportunities for learning and teaching. From the different technologies analyzed, MT and AI tools are the ones more analyzed the most in the studies. For instance, Moore et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] report that MT offers a theoretical and methodological background for specific affordances for English as a foreign language, representing a potential in pedagogy. Other authors like Kelly \u0026amp; Hou [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] indicate that MTs permeate learning and communication; therefore, they could be integrated into formal education. Regarding AI, studies show that they have reconfigured language learning and the language industry, and with an adequate interaction efficiency, quality can be maximized [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Finally, as Alm \u0026amp; Watanabe [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] state, it is evident that there is more acceptance of these technologies not only on behalf of the students, but also teachers who have integrated them in a cautious way in language teaching. However, this growing acceptance may also raise concerns about overreliance on technology and the potential reduction of critical language skills.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis systematic review shows research published in the period 2016\u0026ndash;2025 regarding the use of MT and AI translation technologies in L2 and FL education. The findings indicate a sustained and growing interest in understanding how these technologies mediate language learning processes, particularly in relation to writing development, learner autonomy, and pedagogical practices. Across the different studies, MT and AI tools are not positioned as substitutes for instruction but as resources on which educational values depend, on how they are pedagogically integrated. The studies are predominantly qualitative and theoretical, with emphasis on the perceptions, attitudes, and ethical considerations.\u003c/p\u003e \u003cp\u003eThe review also analyzes how MTs and AI offer pedagogical affordances, including support for L2 writing, translanguaging, and access to linguistic input. The literature reports challenges related to overreliance on technology, potential impacts on linguistic accuracy and development, as well as uncertainty regarding appropriate instructional use. The teacher\u0026rsquo;s response suggests a shift from resistance towards cautious integration, emphasizing the need for guided, reflective, and metacognitive approaches. These findings highlight the role of educators in mediating technology use so that MTs function as a learning tool rather than a shortcut that prevents language development.\u003c/p\u003e \u003cp\u003eThis research, however, presents some limitations that need to be acknowledged. First, the number of included studies is relatively small and distributed across regions, languages, and educational contexts, with a strong concentration in Asian and English-dominant settings. Second, the reliance on open-access publications written in English and Spanish may have excluded relevant research in other languages or paid. Third, many of the results focus on perceptions and experiences rather than longitudinal or outcome-based measures of learning. Future research is needed in more diverse geographical representation, stronger empirical designs, and longitudinal studies that analyze how MT use influences language development over time and, of course, as Deng \u0026amp; Yu [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] recommend, to explore the design of MT integration to establish the objectives necessary for an adequate pedagogical approach. Overall, the findings underscore the need for a balanced and pedagogically informed approach to integrating MT and AI in language education.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by 2025 Autonomous University of Queretaro FONFIVE funding (202515055).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval, consent to participate and consent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkbari Motlaq MD, Tengku Mahadi TS. 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Springer Nature; 2025. pp. 415\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Second Language Learning, Educational Technology, Machine Translation, Artificial Intelligence","lastPublishedDoi":"10.21203/rs.3.rs-9283782/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9283782/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid development of Machine Translation (MT) integrated with Artificial Intelligence (AI) has significantly influenced second and foreign language education, prompting renewed debate about their pedagogical position. This systematic review examines research published in the period of 2016 to 2025 on the use of MTs in formal language learning contexts. Guided by the PRISMA 2022 framework, a structured search was conducted in the Dimensions database using predefined inclusion and exclusion criteria. Eighteen open-access studies written in English or Spanish were selected for analysis. The studies were analyzed based on research purposes, methodological designs, technologies investigated, learning domains, educational settings, and key findings. The results indicate a growing scholarly interest in MTs and AI tools, particularly in relation to L2 writing development, learner autonomy, and pedagogical integration. Most studies adopt qualitative or theoretical approaches, focusing on learners\u0026rsquo; and teachers\u0026rsquo; perceptions, attitudes, and ethical considerations highlighting a lack of strong empirical evidence. MTs are conceptualized as mediational resources rather than replacements for instructions, offering affordances such as writing support, translanguaging practices, and increased access to linguistic input. However, the literature also reports challenges, including overreliance on technology, concerns about linguistic accuracy, and uncertainty regarding effective instructional use. The review concludes that the educational value of technology depends largely on guided, reflective, and pedagogically informed integration. Limitations include the small number of studies, uneven geographical distribution, and a predominance of perception-based research. Future research should employ stronger empirical and longitudinal designs to better understand the impact of MT on language development over time.\u003c/p\u003e","manuscriptTitle":"Machine Translation in Language Learning: A Systematic Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 16:30:41","doi":"10.21203/rs.3.rs-9283782/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-21T15:25:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T02:33:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-02T02:32:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Education","date":"2026-03-31T19:17:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diedu","sideBox":"Learn more about [Discover Education](https://www.springer.com/journal/44217)","snPcode":"44217","submissionUrl":"https://submission.nature.com/new-submission/44217/3","title":"Discover Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15bd390d-6fcf-4942-af9b-58a5b49125d0","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-30T16:30:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 16:30:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9283782","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9283782","identity":"rs-9283782","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}