Embracing machine translation in L2 education: Bridging theory and practice in the AI Age

Chapter 12 Embracing machine translation in L2 education: Bridging theory and practice in the AI Age Atsushi Mizumoto Kansai University, Japan This chapter examines the evolving role of machine translation (MT) in second language (L2) education. It first summarises recent research on MT’s pedagogical applications, synthesising findings from systematic reviews that highlight MT’s effectiveness in L2 writing when used appropriately. The chapter explores learner and teacher perceptions, noting a generally positive attitude among students but a more divided stance among educators. It presents a comprehensive framework for understanding factors influencing MT use in L2 writing, encompassing linguistic, personal, contextual, and ideological dimensions. The chapter introduces the concept of “MT as Augmented L2 Competence,” offering a new paradigm for integrating MT into language learning. It discusses various models of MT instruction, including the Guided Use of MT (GUMT) model. Finally, the chapter proposes the Metacognitive Resource Use (MRU) framework, which positions learners as metacognitive agents capable of strategically utilising a wide range of language resources, including MT and generative AI (GenAI) tools. This integrated approach aims to foster autonomous learners who can effectively navigate the complex landscape of digital language learning resources. 1 Current status of MT use in L2 education 1.1 Pedagogical applications As evidenced by the recent increase in publications on MT use for L2 learning and teaching, a wealth of research reports various pedagogical applications for MT in Atsushi Mizumoto. 2026. Embracing machine translation in L2 education: Bridging theory and practice in the AI Age. In JC Penet, Joss Moorkens & Masaru Yamada (eds.), Teaching translation in the age of generative AI: New paradigm, new learning?, 233–248. Berlin: Language Science Press. DOI: 10.5281/zenodo.17641087 Atsushi Mizumoto L2 education. These applications include using MT as a resource for vocabulary acquisition (Lo 2025, 2024) and grammar practice (Lee & Kang 2024), fostering metalinguistic awareness through comparative analysis of original and machinetranslated texts (Tsai 2019), and enhancing learner autonomy by promoting selfdirected learning (Lee 2020). Indeed, the proliferation of MT-related research over the past two decades has led to the emergence of several systematic reviews (Jolley & Maimone 2022, Klimova et al. 2022, Lee 2023, Ohashi 2024). These comprehensive analyses provide valuable insights into the current state of MT use in L2 education and highlight key trends and findings across multiple studies. Jolley & Maimone (2022) reviewed MT studies from 2000 to 2019, focusing on MT accuracy, educational applications, and perceptions of learners and teachers. They noted the improvement in MT quality over time and its effectiveness in L2 writing, while also highlighting uncertainties about its long-term impact on language learning. Klimova et al. (2022) analyzed 13 studies published between 2018 and 2021, specifically examining the impact of neural machine translation (NMT) in foreign language education. Their review found that NMT can be beneficial for language learning, particularly for advanced learners, but also identified a lack of teacher training in this area. Lee (2023) conducted a systematic review of 87 studies from 2000 to 2019, investigating the effectiveness of MT in foreign language education. The review highlighted MT’s usefulness in L2 writing, mixed perceptions among learners, and discrepancies between teacher and student perceptions. It also emphasised the need for further research on long-term learning effects and appropriate usage methods. Lee also conducted a meta-analysis with 12 studies that satisfied predefined criteria. It revealed that utilising MT had a positive impact on L2 learning with a small effect size (g= 0.345, 95% CI [0.201, 0.489]). The analysis of subcategories showed that using MT was effective in various linguistic areas, including lexical accuracy (g= 0.616, 95% CI [0.243, 0.676]), syntactic accuracy (g= 0.562, 95% CI [0.371, 0.754]), syntactic complexity (g= 0.453, 95% CI [0.181, 0.726]), orthography (g= 0.741, 95% CI [0.424, 1.059]), and overall writing quality (g= 0.768, 95% CI [0.547, 0.990]). However, the effect on lexical complexity was not statistically significant (g= 0.253, 95% CI [-0.058, 0.564]). These findings provide quantitative evidence for the positive impact of MT on various aspects of language learning, particularly in writing skills. This additional information strengthens the overall narrative by providing specific quantitative evidence of MT’s effectiveness in various aspects of language learning. It supports the general consensus among the reviews that MT can be beneficial for L2 writing when 234 12 Embracing machine translation in L2 education: used appropriately, while also highlighting areas where its impact may be less pronounced (such as lexical complexity). Ohashi (2024) examined 14 studies published between 2020 and 2022, reporting on the latest trends and key findings in MT research. This review emphasised that MT can be a good starting point for language learning, but its effectiveness varies depending on learner proficiency and target language. It also stressed the importance of communication between teachers and learners and the need for teacher training in MT use. The systematic reviews collectively indicate that MT can be an effective tool for language learning when used appropriately. They emphasise that MT is particularly beneficial for L2 writing, helping students reduce lexico-grammatical errors and focus more on content (Klimova et al. 2022, Lee 2023). However, the reviews also point out that the effectiveness of MT use may vary depending on factors such as students’ proficiency levels, target language pairs, and specific learning tasks (Ohashi 2024). Furthermore, these reviews underscore the importance of proper guidance and training for both students and teachers in effectively using MT for language learning. They highlight a gap between teachers’ perceptions and students’ actual practices, emphasising the need for better communication and mutual understanding (Jolley & Maimone 2022, Lee 2023). The reviews also call for more research on the long-term effects of MT use on language acquisition and the development of appropriate pedagogical strategies to integrate MT into language curricula effectively. 1.2 Learner and teacher perceptions As reported above, MT use is widespread among language learners, who utilise these tools for a variety of purposes. Studies have found that a significant percentage of students, particularly those in higher education settings, report frequently using MT tools like Google Translate for language learning tasks, including writing and translation (Lee 2020, Tsai 2019). This widespread use persists despite learners often being aware of MT’s limitations, particularly regarding accuracy and the potential for grammatical errors (Jolley & Maimone 2022). Learners primarily view MT as a convenient and beneficial aid for language learning, using it for tasks such as looking up words and phrases, facilitating translation, and enhancing the revision process. While some learners express concerns about over-reliance on MT and its potential impact on learning, research indicates a generally positive attitude towards MT’s role in supporting language acquisition (Chung & Ahn 2022). 235 Atsushi Mizumoto In contrast to learner perspectives, the literature highlights a more divided stance among teachers regarding the use of MT in L2 education. While many teachers continue to express concerns about MT’s potential to hinder language acquisition and facilitate academic dishonesty, others recognise its pedagogical potential as a valuable learning resource (Ducar & Schocket 2018). The reluctance among teachers to embrace MT stems from several factors. Firstly, the historical association of MT with inaccurate translations, often referred to as a “bad model,” and its perceived threat to traditional teaching methods contributes to skepticism (Lo 2025). Concerns about academic integrity, with MT being viewed as a form of cheating, also contribute to this reluctance. This often leads teachers to focus on detecting and preventing MT use rather than exploring its potential benefits (Jolley & Maimone 2022). However, there is a growing movement among researchers and practitioners to integrate MT into language classrooms in a meaningful and responsible manner (Hellmich 2023, Lee 2020). This shift is driven by the understanding that MT use is now ubiquitous and cannot be effectively banned (Ducar & Schocket 2018). Instead, advocates of MT integration call for instructing learners about its strengths and weaknesses, equipping them to use it critically and ethically as a tool for language learning (Klimova et al. 2022). 1.3 Factors and views influencing MT use The scoping review by Jiang et al. (2024), which synthesises 29 MT studies in L2 writing from 2009 to 2023, examines the factors influencing MT use in L2 writing, focusing on four main categories: linguistic, person/individual, contextual, and ideological factors, framed through cognitive, sociocultural, and critical theoretical perspectives. • Linguistic factors (cognitive perspective): The review analyzes MT’s role as a linguistic processor, exploring how the cognitive processing of MT impacts L2 writing. It explores the effects of different writing tasks on MT utilisation and assesses how the quality of input and output from MT systems affects the resulting text complexity and structure. These considerations are crucial in determining how both teachers and students perceive and employ MT, shaping its integration into educational practices. • Person/individual factors (sociocultural perspective): MT is viewed as a mediational artifact/tool within social contexts, incorporating factors such as students’ beliefs about MT’s effectiveness, their writing strategies, L2 236 12 Embracing machine translation in L2 education: proficiency, and digital literacy. Additionally, teacher-related factors include their attitudes towards MT, pedagogical integration of MT, and ability to detect its use. These individual factors critically influence educational approaches and the effectiveness of MT. • Contextual factors (sociocultural perspective): The role of MT is influenced by the dynamics within educational environments, such as peer interactions, the presence of MT training or scaffolding in instructional settings, and institutional policies that may either support or restrict MT usage. These contextual factors are pivotal in shaping the operational and situational environments for MT, impacting its perception and efficacy in educational settings. • Ideological factors (critical perspective): Ideological considerations focus on language-related ideologies, including monolingual biases, standard language ideologies, and translanguaging stances, and how these influence the use and perception of MT. Such ideologies challenge conventional language education practices and influence how MT is integrated and impacts linguistic hierarchies within educational contexts. Jiang et al. (2024) propose a layered conceptual framework to understand the factors influencing MT use in L2 writing. With ideological factors as the outermost layer, contextual factors encompass both person/individual and linguistic factors, with the latter being positioned at the innermost core. These layers interact to shape the core activities involved in the cognitive processing of linguistic factors. The model effectively illustrates the complex interplay from broad societal and ideological influences to specific individual and cognitive interactions, highlighting the necessity of considering a range of factors from societal ideologies to individual cognitive processes when integrating MT into L2 education. Building on the comprehensive framework proposed by Jiang et al. (2024), we can see how ideological factors form the outermost layer influencing MT use in L2 education. This ideological dimension is particularly evident in two recent studies that highlight contrasting perspectives on MT use in academic settings. Grieve et al. (2024) conducted a qualitative study exploring the ethical views of nursing and midwifery students on using AI machine translation software for university assignments. Their findings reveal a complex interplay of factors influencing students’ ethical decision-making, including ownership of ideas, fairness and respect, and personal growth. Importantly, the study identified a tension between deficit-oriented and translanguaging perspectives among students, with 237 Atsushi Mizumoto the latter referring to an approach that views learners’ use of multiple languages as a resource rather than a problem. The deficit-oriented perspective, often aligned with traditional language teaching methodologies, perceives L1 or MT use as detrimental to L2 acquisition. This perspective typically leads to policies discouraging or prohibiting MT use, viewing it as an impediment to authentic language learning. With this perspective, it is believed that that MT use may hinder the development of critical thinking skills in the target language and interfere with immersive language experiences. Conversely, the translanguaging view adopts a more inclusive approach to language learning. This perspective values learners’ entire linguistic repertoire as a resource (Wei 2018). Advocates of translanguaging argue that MT can be a valuable tool for accessing and leveraging learners’ full range of linguistic knowledge, potentially enhancing both language awareness and learning outcomes. This view aligns with contemporary understandings of bilingualism and multilingualism, which conceptualise languages as part of an integrated communication system rather than as separate entities. These contrasting perspectives on MT use in language education are further exemplified in the approaches educators and institutions take when addressing student use of MT. Jolley and Maimone’s (2022) comprehensive review of three decades of MT research in language teaching and learning highlights two distinct approaches: the “MT as Cheating” approach, which leads to a Detect-ReactPrevent Response, and the “MT as Resource” approach, which encourages an Integrate-Educate-Model strategy. The “MT as Cheating” perspective, aligned with the deficit-oriented view, treats MT use as a form of academic dishonesty. This approach focuses on strategies to detect unauthorised MT use, react punitively, and prevent future occurrences. Proponents of this view recommend implementing clear syllabus policies against MT use, designing assignments resistant to MT use, and educating students about the pitfalls of relying on MT. This perspective often leads to policies that ban MT use outright, viewing it as incompatible with language learning goals. In contrast, the “MT as Resource” approach, more closely aligned with the translanguaging view, sees MT as a potential tool for language learning. This perspective advocates integrating MT into the curriculum, educating students on its appropriate use, and modeling effective strategies for leveraging MT in language learning. Researchers like Stapleton & Kin (2019) and Niño (2020) argue for accepting the reality of MT use and finding ways to incorporate it meaningfully into language education. This approach acknowledges the ubiquity of MT in modern life and seeks to prepare students to use it critically and effectively. 238 12 Embracing machine translation in L2 education: The shift from the Detect-React-Prevent mindset to the Integrate-EducateModel approach reflects a growing recognition of the inevitability of MT use in language learning contexts. As Ducar & Schocket (2018) note, the key question is no longer whether teachers can prevent learners from using MT, but rather how to help them use it ethically and effectively as part of their language learning journey. These contrasting approaches to MT use in language education exemplify the broader ideological tensions identified in Grieve et al. (2024) and reflect the outermost layer of ideological factors in Jiang et al.’s (2024) framework. They demonstrate how deeply held beliefs about language acquisition and the role of technology can shape educational policies, pedagogical practices, and ultimately, students’ engagement with and perceptions of MT in their L2 development process. This interplay between ideological stances and practical approaches underscores the complexity of integrating MT into language education and highlights the need for context-sensitive strategies that consider both the potential benefits and challenges of MT use in L2 learning and teaching. 2 Integrating MT into L2 education: A new paradigm 2.1 MT as augmented L2 competence The translanguaging perspective and “MT as Resource” approach, implemented through the Integrate-Educate-Model strategy, provide a theoretical and practical foundation for incorporating MT into L2 education. Building upon these concepts, we can further conceptualise MT use in language learning through the lens of “MT as Augmented L2 Competence.” This model offers a visual representation of how MT can enhance learners’ language abilities, particularly in bridging the gap between receptive and productive skills. By viewing MT as a tool for augmenting competence rather than replacing language learning, we align with the translanguaging idea of fluid language practices and the “MT as Resource” approach. The Integrate-Educate-Model strategy can then be applied to help learners effectively utilise MT to expand their augmented competence zone, while simultaneously developing their own language skills. This integrated perspective not only justifies the use of MT in language learning but also provides a framework for understanding its role in enhancing overall L2 proficiency. The concept of MT as augmented L2 competence is illustrated in Figure 1, which provides a visual representation of how MT, and also GenAI such as ChatGPT, can enhance language learners’ abilities. 239 Atsushi Mizumoto Augmented competence with MT (AI) Productive competence Receptive competence Figure 1: The concept of MT as augmented L2 competence The figure demonstrates the relationship between receptive competence, productive competence, and the potential for augmented competence through MT use. Here is a breakdown of the key elements: • Receptive Competence: This is represented by the larger, outer oval. It refers to the ability to understand the target language (L2), which is typically more developed than productive skills. For most L2 English language learners, their capacity to comprehend English exceeds their ability to produce it. • Productive Competence: Shown as the smaller, inner oval, this represents the learner’s ability to actively use the language. It is generally more limited than receptive competence, which aligns with theories like Swain’s output hypothesis (1985), emphasising the importance of language production in second language acquisition. • Augmented Competence with MT (AI): This is depicted by the dark gray area extending beyond the productive competence oval. It illustrates how MT can bridge the gap between what learners can recognise as correct (receptive knowledge) and what they can produce on their own. Figure 1 suggests that MT can serve as a tool to augment learners’ competence, particularly in areas where they can recognise correctness by sight but struggle to produce it accurately. This augmentation is especially beneficial for more proficient learners, as supported by previous studies (Klimova et al. 2022, Ohashi 240 12 Embracing machine translation in L2 education: 2024). Higher proficiency learners tend to have a larger gap between their receptive and productive skills, providing more room for MT to assist in bridging this divide. Importantly, this model underscores that there remains a strong rationale for studying English (or any L2). The augmented competence provided by MT is built upon the foundation of the learner’s own language skills. Without developing one’s own receptive and productive competencies, the benefits of MT augmentation would be limited. Furthermore, as learners’ proficiency increases, they become better equipped to effectively utilise MT, maximising its potential as a learning tool. This conceptualisation of MT as augmented L2 competence aligns with the findings from systematic reviews (Jolley & Maimone 2022, Lee 2023) that highlight MT’s effectiveness when used appropriately, particularly for more advanced learners. It also supports the need for proper guidance and training in MT use, as the tool’s effectiveness is contingent upon the learner’s ability to critically evaluate and apply its output. In sum, the model presented here provides a framework for understanding how MT can be integrated into language learning processes. It emphasises that MT is not a replacement for language study, but rather a tool that can enhance and extend learners’ existing competencies, potentially accelerating their progress towards higher levels of language proficiency. 2.2 MT instruction for L2 learning Niño (2009) proposed four models of MT use in L2 education: a “bad model”, a “good model”, vocational applications (particularly in translation-related fields), and as a computer-assisted language learning (CALL) tool. Initially, MT was employed as a “bad model”, where students identified and corrected errors through post-editing, a process necessitated by the limited accuracy of early systems. In contrast, the “good model” involved using MT outputs as exemplars for students. These models, reflecting the evolution of MT technology and its pedagogical applications, illustrate a significant shift in focus. As MT technology has advanced, its primary use has transitioned to serving as a CALL tool, where it facilitates student engagement in solving language problems independently, as evidenced in recent studies (Lee 2020, Stapleton & Kin 2019, Tsai 2019). Lee (2023) recommends that teachers should provide guidelines for using MT and explicitly teach effective strategies to students prior to using it, which leads to enhancing student performance, as supported by O’Neill (2016). 241 Atsushi Mizumoto Ryu, Jieun, Young Ae Kim, Seojin Park, Seungmin Eum, Sojung Chun & Sunyoung Yang. 2022. Exploring foreign language students’ perceptions of the guided use of machine translation (GUMT) model for Korean writing. L2 Journal 14(1). 136–165. DOI: 10.5070/L214151759. Stapleton, Paul & Becky Leung Ka Kin. 2019. Assessing the accuracy and teachers’ impressions of Google Translate: A study of primary L2 writers in Hong Kong. English for Specific Purposes 56. 18–34. DOI: 10.1016/j.esp.2019.07.001. Swain, Merrill. 1985. Communicative competence: Some roles of comprehensible input and comprehensible output in its development. In Susan Gass & Carolyn Madden (eds.), Input in second language acquisition, 235–253. Newbury House. Tsai, Shu-Chiao. 2019. Using Google Translate in EFL drafts: A preliminary investigation. Computer Assisted Language Learning 32(5-6). 510–526. DOI: 10.1080/ 09588221.2018.1527361. Wei, Li. 2018. Translanguaging as a practical theory of language. Applied Linguistics 39(1). 9–30. DOI: 10.1093/applin/amx039. 248