DeepL as a Translanguaging Tool in an Indonesian EFL Student’s Academic Writing

BackgroundLanguage learners are taking advantage of the increased accuracy afforded by machine translation (MT) tools like Google Translate. There is a growing debate as to whether these tools support or hinder English as a foreign language (EFL) education. Some EFL students are drawn to MT tools to save time and energy when communicating, but how this use relates to second language acquisition needs to be clarified.ObjectiveThis study investigated the perceptions and practices of MT tools among university EFL students in Saudi Arabia and South Korea.MethodsThe study surveyed Saudi (n = 310) and South Korean (n = 160) university students to explore the use of MT tools in EFL learning. It used mean scores, correlation analysis, and structural equation modelling to understand the perceived benefits and limitations of MT tools and their influence on the use of translation websites. The findings were triangulated using content analysis of open‐ended items.Results and ConclusionFindings indicate students showed a high level of acceptance and utilization of MT tools. English avoidance behaviour (B = 0.43), benefits (B = 0.32), and foreign language proficiency (B = −0.19) predicted 61% of actual use of MT tools like Google Translate. Further, students frequently used MT tools despite the reported concerns about accuracy mistakes. Saudi students listed a more comprehensive array of why they use MT tools than Korean students, while Saudis and Koreans frequently used MT tools for individual word and sentence translations.ImplicationsStudents should edit MT output when writing and are encouraged to use the voice recording and other functions of MT tools to learn language skills. Instructors must prevent overuse and dependency on MT tools, especially among low‐proficiency students who want to avoid using English.

This systematic literature review (SLR) examines the functions, limitations, and effectiveness of machine translation (MT) tools in English language learning. Most of the reviewed studies addressed the context of English as a Foreign Language (EFL), with few references to other linguistic environments. MT tools have been successful in translating text quickly and improving personalized learning experiences. Yet, these tools continue to grapple with context-sensitive translation, including that which demands cultural sensitivity or gender fidelity. The review identifies an increasing demand for incorporating MT tools into pedagogical approaches, underlining learner attitudes and cultural as well as linguistic difference challenges like gender bias. Even so, the research also necessitates better MT systems, proposing a blended approach with human post-editing to counter these weaknesses. The research identifies a wide knowledge gap in areas such as the Philippines and other underrepresented scopes, restricting the generalizability of findings to various learning settings. Consequently, overcoming biases, improving tools accuracy, and offering transparent usage instructions remains an unexplored area. Subsequent research needs to broaden the scope through a range of research designs, especially mixed methods and experimental studies, and investigating the utilization of MT tools in various learning environments. Furthermore, researchers need to enhance the incorporation of MT in language instruction, fixing biases, and refashioning these tools for more inclusive, sensitive applications. Lastly, more research needs to examine the sociolinguistic effects of gender bias in MT and AI tools for a variety of gendered and low resource languages to promote an ideal and productive learning environment.

This study evaluated university students' perceptions of machine translation (MT) tools, such as Microsoft Translator and Google Translate, in the language industry. Specifically, it examines awareness, use, and trust in machine translation (MT) tools in higher education in Pakistan. Drawing on the Technology Acceptance Model (TAM) and the Trust in Technology Framework, a quantitative approach was used, and a survey questionnaire was designed. The data were collected through convenience sampling from 50 translation studies students at three universities in the Punjab (e.g., the University of the Punjab, the University of Gujrat, and the National University for Modern Languages). The findings showed that MT tools became a regular part of students' academic routine, especially in assignment writing, vocabulary learning, and reading comprehension. To a large extent, students found these tools helpful and highly appreciated the convenience of MT tools. Concerns remained about their accuracy, particularly in the use of idiomatic expressions and technical terms. This study identified that the presence of the MT tools in students' learning practices is increasingly embedded. However, the way students use them is conditioned not only by trust and training, but also by contextual needs and perceived reliability.

English occupies a central position in scholarly publishing, but using a lingua franca for scholarly publishing has consequences for scholars, science, and society. For instance, non Anglophone researchers may need longer to read and write in English and may face more manuscript revisions and rejections, potentially leading to a lower volume of research output, which could negatively affect career advancement. To what extent can machine translation (MT) tools (e.g., Google Translate) help to support a more multilingual scholarly publishing ecosystem? To find out, we undertook a scoping review of the literature to investigate how MT tools are being used for multilingual scholarly publishing. Following a multilingual search in nine bibliographic databases, 875 papers were retrieved and screened, and 39 were included for closer investigation. Analysis reveals that MT tools are being actively developed, tested, applied, and evaluated in the context of scholarly publishing. However, at present, these tools are not displacing English from its central position; the main use of MT tools currently is to reduce the burden of publishing in English for scholars with limited English proficiency. This suggests that technology alone cannot create or sustain a multilingual scholarly publishing ecosystem. Hence, meaningful policies, in addition to improved MT tools and language resources, are needed to create a more linguistically diverse and equitable scholarly publishing landscape.

New technologies have had a substantial impact on L2 learners' writing processes. Given the continuous nature of technological evolution, more work is needed to document L2 writers' learner‐initiated technology use, particularly their use of machine translation (MT) tools. This need is further solidified by recent calls for new pedagogical approaches to better prepare learners to use MT critically. The current study uses screen recordings, retrospective recall, and interviews to document what online tools L2 writers' use, how they use them, and what factors influence this use. Findings reveal that participants overwhelmingly rely on MT tools while writing. Moreover, they engage in complex actions with MT tools, which are influenced by language knowledge, beliefs about online tools, their own perceived roles in the writing process, and classroom policies. The paper ends with a consideration of the struggles that emerge to consider their pedagogical implications for supporting critical online tool use.

Despite the increasing prominence of machine translation (MT) tools in academic settings, few studies have explored how EFL students balance the use of translation techniques with digital assistance while also enhancing their writing skills. This research investigates how fourth-semester EFL students translate and articulate their views on utilizing MT tools to support academic writing. The fourth semester students enrolling translation course in an undergraduate program in a public university in Indonesia participated in this mixed method research. Data were gathered from documents, questionnaire, and interview which then analyzed with qualitative method and descriptive statistical analysis. The findings reveal that borrowing and literal translation were the most frequently used techniques, followed by transposition and adaptation. It indicates students’ reliance on direct linguistic transfer and their emerging grammatical flexibility and cultural awareness. Although the students acknowledged the limitations of MT tools and the need for critical post-editing, they had positive attitudes toward MT tools usage. The study concludes that students are in a transitional phase of translation competence. Hence, pedagogical interventions—emphasizing writing conventions, post-editing skills, and MT literacy including etiquette—are highly required for endorsing the development of both translation and academic writing skills in EFL academic settings.

The rapid evolution of real-time machine translation (MT) tools, especially AI-based systems such as Google Translate, DeepL, and ChatGPT-powered translators, has transformed the landscape of foreign language pedagogy. Over the last decade, learners have increasingly relied on MT technologies to complete tasks, interpret texts, and overcome linguistic barriers. This article presents a comparative pedagogical study investigating the effectiveness of real-time translation tools in foreign language teaching, with particular focus on comprehension, vocabulary acquisition, learner autonomy, and translation accuracy. Drawing on recent research, classroom-based observations, and comparative linguistic analysis, the article evaluates the benefits and limitations of integrating MT tools into foreign language instruction. The study finds that while real-time translation tools support rapid comprehension and expand learner independence, inappropriate or unmonitored use may hinder deep linguistic processing and reduce the development of communicative competences. The article proposes methodological recommendations for balanced pedagogical integration of MT tools in language classrooms.

The use of machine translation (MT) tools remains controversial among language instructors, with limited integration into classroom practices. While much of the existing research into MT and language education has explored instructor perceptions, less is known about how students actually use MT or how student use compares to instructor beliefs and expectations. In response to this gap, the current article explores how students use MT while writing and how this use compares to instructor perceptions via two studies: a computer-tracking study of how 49 second semester-level language learners (French, Spanish) use MT and a qualitative survey of 165 US-based second language educators’ beliefs about MT. Findings highlight important areas of alignment (e.g., MT input at word level) and divergence (e.g., MT output analysis strategies) between student use and instructor perceptions as well as layered tensions in what mediates student use of MT tools. The article concludes with calls for more research on student use and an outline for how to approach MT tools in language education in ways that support existing student practices.

Translation of medical consultation summaries is essential for equitable health care communication in culturally and linguistically diverse populations. While machine translation (MT) tools and large language models (LLMs) are widely accessible, their feasibility and safety for health care contexts remain underexplored. This pilot study investigates the feasibility and limitations of using LLMs and traditional MT tools to translate medical consultation summaries from English into the most common languages other than English spoken in Australia-Arabic, Chinese (simplified written form), and Vietnamese. Two simulated summaries-a simple patient-facing summary and a complex clinician-oriented interprofessional letter-were translated using 3 LLMs (GPT-4o, Llama-3.1, and Gemma-2) and 3 MT tools (Google Translate, Microsoft Bing Translator, and DeepL). Translations were benchmarked against professional third-party interpreter translations using Bilingual Evaluation Understudy, Character-level F-score, and Metric for Evaluation of Translation with Explicit Ordering metrics. The translation performance varied across languages, tools, and summary complexity when assessed using automatic evaluation metrics. Traditional MT tools outperformed LLMs on surface-level metrics, while LLMs showed relative strengths in semantic similarity for Vietnamese and Chinese. Arabic translations improved with complex input, suggesting morphological advantages. The metric-based evaluation highlighted feasibility but also risks, particularly in Chinese clinical contexts. This pilot study provides formative evidence of opportunities and limitations in applying artificial intelligence translation for health care communication. Findings underscore the importance of human oversight; domain-specific evaluation metrics; and further formative and clinical research to guide the safe, equitable use of artificial intelligence translation tools.

The recent advent of more powerful machine translation (MT) tools has significant implications for foreign language teaching and learning. While current research and pedagogical guidelines on MT in the foreign and second language classroom have focused primarily on survey data, less is known about how students actually use MT tools. The current article chronicles a computer tracking study of novice learners of French as a Foreign Language (n=26) as a way to inform this field of research. Drawing on diverse data sources (screen-recorded observations, retrospective recalls, post-interviews) and a Critical Incident Technique analytical approach, the article showcases the actions and cognitive processes related to MT that both supported and hindered student participants in their foreign language writing. Supportive behaviors and mindsets centered on a specific awareness of MT tool limitations paired with appropriate action while detrimental behaviors and mindsets centered on inappropriate input, lack of output analysis, and time. Suggestions for how these use-based findings might inform how MT is discussed in the language classroom conclude the article.

BackgroundChinese is the second most common language spoken by limited English proficiency individuals in the United States, yet there are few public health materials available in Chinese. Previous studies have indicated that use of machine translation plus postediting by bilingual translators generated quality translations in a lower time and at a lower cost than human translations.ObjectiveThe purpose of this study was to investigate the feasibility of using machine translation (MT) tools (eg, Google Translate) followed by human postediting (PE) to produce quality Chinese translations of public health materials.MethodsFrom state and national public health websites, we collected 60 health promotion documents that had been translated from English to Chinese through human translation. The English version of the documents were then translated to Chinese using Google Translate. The MTs were analyzed for translation errors. A subset of the MT documents was postedited by native Chinese speakers with health backgrounds. Postediting time was measured. Postedited versions were then blindly compared against human translations by bilingual native Chinese quality raters.ResultsThe most common machine translation errors were errors of word sense (40%) and word order (22%). Posteditors corrected the MTs at a rate of approximately 41 characters per minute. Raters, blinded to the source of translation, consistently selected the human translation over the MT+PE. Initial investigation to determine the reasons for the lower quality of MT+PE indicate that poor MT quality, lack of posteditor expertise, and insufficient posteditor instructions can be barriers to producing quality Chinese translations.ConclusionsOur results revealed problems with using MT tools plus human postediting for translating public health materials from English to Chinese. Additional work is needed to improve MT and to carefully design postediting processes before the MT+PE approach can be used routinely in public health practice for a variety of language pairs.

The use of machine translation (MT) tools in language learning classroom is now omnipresent, which raises a dilemma for instructors because of two issues, language proficiency and academic integrity, caused by that fact. However, with the unstoppable development and irresistible use of MT in language learning, rather than entangling with using it or banning it, it is more significant to figure out why learners turn to MT in spite of the prohibition from their instructors and how can instructors guide learners to use it appropriately. Consequently, this paper reviews articles with regard to the reason why learners turn to MT, the practical use of MT in learners’ writing, and some pedagogical solutions for making peace with MT in language learning classroom respectively. Implications can be garnered like that a course for learners of how to use MT tools properly should be included in the curriculum design, and simultaneously, the holistic understanding of these overwhelmingly fast-developed technology tools for instructors should be a part of teachers’ self-development, since instructors without knowledge said technology tools can not fully motivate language learners and implement the pedagogical solutions offered.

The present study explores English as a foreign language (EFL) learners’ processes and strategies when using machine translation (MT) tools in academic abstract writing. Eight EFL graduate students were introduced to translation-friendly writing strategies using Google Translate and were required to produce an English abstract with the aid of a machine translation tool. The study used qualitative and quantitative approaches in data collection and analysis. A triangulation process was developed and implemented, including think-aloud protocols during the writing session, surveys, and individual interviews after the writing session. The findings suggested that the translation-friendly writing strategies introduced to the participants were useful in enhancing the quality of their writing. Each participant demonstrated individual strategic uses of MT. Among the various strategies reported, back translation was the most commonly adopted one; that is, they first composed an abstract in Chinese (L1) and engaged in multiple rounds of translation between Chinese and English using MT; when problems were identified in the English abstract, they modified the Chinese abstract using translation-friendly writing strategies to enhance the quality of MT translation output. Most of the translation problems identified by the participants were related to non-academic expressions. While participants were satisfied with the quality of the abstracts produced with the aid of MT, they raised ethical concerns regarding the use of MT in academic writing. These findings suggest that MT has fundamentally changed the process of academic writing in English and call for the re-examination of the purpose of academic writing instruction and the approaches employed.

BackgroundThe rise in artificial intelligence tools, especially those competent at language interpretation and translation, enables opportunities to enhance patient-centered care. One might be the ability to rapidly and inexpensively create accurate translations of English language patient-reported outcome measures (PROMs) to facilitate global uptake. Currently, it is unclear if machine translation (MT) tools can produce sufficient translation quality for this purpose.MethodologyWe used Generative Pretrained Transformer (GPT)-4, GPT-3.5, and Google Translate to translate the English versions of selected scales from the Breast-Q and Face-Q, two widely used PROMs assessing outcomes following breast and face reconstructive surgery, respectively. We used MT to forward and back translate the scales from English into Arabic, Vietnamese, Italian, Hungarian, Malay, and Dutch. We compared translation quality using the Metrics for Evaluation of Translation with Explicit Ordering (METEOR). We compared the scores between different translation versions using the Kruskal-Wallis test or analysis of variance as appropriate.ResultsIn forward translations, the METEOR scores significantly varied depending on target languages for all MT tools (p < 0.001), with GPT-4 having the highest scores in most languages. We detected significantly different scores among translators for all languages (p < .05), except for Italian (p = 0.59). In backward translations, MTs (GPT-4: 0.81 ± 0.10; GPT-3.5: 0.78 ± 0.12; Google Translate: 0.80 ± 0.06) received higher or compatible scores to human translations (0.76 ± 0.11) for all languages. The differences in backward translation scores by different forward translators were significant for all languages (p < 0.01; except for Italian, p = 0.2). The scores between different languages were also significantly different for all translators (p < 0.001).ConclusionsOur findings suggest that large language models provide high-quality PROM translations to support human translations to reduce costs. However, substituting human translation with MT is not advisable at the current stage.

Numerous studies have explored to what extent and in what ways second language (L2) learning or L2 writing quality is impacted by the use of machine translation (MT) tools or post-editing the MT output, as well as how L2 learners perceive the effectiveness of MT tools. However, the potential of MT, more specifically post-editing machine-translated texts, to improve L2 learners’ translation skills remains unexplored. This study reports on how the MT post-editing behavior of undergraduate students of English as a foreign language (EFL) differs across two genres – short story and research article. Turkish learners of L2 English (n = 25) who were enrolled in a second-year online translation course post-edited Google-translated short stories and sections of research articles (from Turkish to English) for better translation. Using the Wilcoxon signed-rank test, we compared their post-edits in the two tasks for overall frequencies, linguistic levels of the post-edits (morphological, syntactic, semantic, and pragmatic), the success of the post-edits, and the types of post-edits (addition, deletion, substitution, reorder, rewrite). Our findings revealed significant genre effects on several aspects, with the short story genre promoting more student engagement with the post-editing task than the research article genre. Pedagogical implications are discussed.