Pub Date : 2026-01-30DOI: 10.1016/j.rmal.2026.100302
Jueyu Lu, John Rogers
Fluency is a central dimension of L2 oral proficiency. Further, fluency assessment is important for many applied contexts, including pedagogical and assessment purposes. Yet, the measurement of fluency using manual annotation is labor-intensive, which limits its broad application and scalability. We evaluate two automated tools — an acoustic-based tool (de Jong et al., 2021) and a machine-learning tool (Matsuura et al., 2025) — using data from L1-Chinese learners of English. Accuracy was assessed for three metrics, articulation rate (AR), pause ratio (PR), and mean pause duration (MPD), via Pearson correlations with manual annotation. We compared two automated tools and tested whether targeted manual post-processing (TextGrid checks and transcript adjustments) improves metric extraction using Steiger’s test. Results from our sample indicated that de Jong et al. (2021) yielded higher accuracy for silence-based metrics (PR, MPD). However, text-dependent metrics (syllable number after removing disfluency words in AR) benefited from corrected TextGrids (for the acoustic tool) or corrected transcripts (for the machine-learning tool). These findings suggest a scalable division of labor: use an acoustic-based tool for silence-driven metrics, and apply corrected transcripts with a machine-learning tool when extracting text-sensitive metrics.
流利性是第二语言口语熟练程度的一个核心维度。此外，流利度评估在许多应用环境中都很重要，包括教学和评估目的。然而，使用手工标注来测量语言流畅性是一项劳动密集型的工作，这限制了其广泛的应用和可扩展性。我们评估了两种自动化工具——一种基于声学的工具（de Jong et al., 2021）和一种机器学习工具（Matsuura et al., 2025）——使用来自L1-Chinese英语学习者的数据。通过与手动注释的Pearson相关性，评估三个指标的准确性，发音率（AR），暂停率（PR）和平均暂停时间（MPD）。我们比较了两种自动化工具，并使用Steiger测试测试了是否有针对性的手动后处理（TextGrid检查和文本调整）改善了度量提取。我们的样本结果表明，de Jong等人（2021）对基于沉默的指标（PR， MPD）的准确性更高。然而，文本依赖指标（在AR中去除不流畅单词后的音节数）受益于纠正的textgrid（用于声学工具）或纠正的转录本（用于机器学习工具）。这些发现表明了一种可扩展的分工：使用基于声学的工具来获取沉默驱动的指标，并在提取文本敏感指标时使用机器学习工具应用纠正的转录本。
{"title":"Evaluating and enhancing the accuracy of automated fluency annotation tools in L2 research","authors":"Jueyu Lu, John Rogers","doi":"10.1016/j.rmal.2026.100302","DOIUrl":"10.1016/j.rmal.2026.100302","url":null,"abstract":"<div><div>Fluency is a central dimension of L2 oral proficiency. Further, fluency assessment is important for many applied contexts, including pedagogical and assessment purposes. Yet, the measurement of fluency using manual annotation is labor-intensive, which limits its broad application and scalability. We evaluate two automated tools — an acoustic-based tool (de Jong et al., 2021) and a machine-learning tool (Matsuura et al., 2025) — using data from L1-Chinese learners of English. Accuracy was assessed for three metrics, articulation rate (AR), pause ratio (PR), and mean pause duration (MPD), via Pearson correlations with manual annotation. We compared two automated tools and tested whether targeted manual post-processing (TextGrid checks and transcript adjustments) improves metric extraction using Steiger’s test. Results from our sample indicated that de Jong et al. (2021) yielded higher accuracy for silence-based metrics (PR, MPD). However, text-dependent metrics (syllable number after removing disfluency words in AR) benefited from corrected TextGrids (for the acoustic tool) or corrected transcripts (for the machine-learning tool). These findings suggest a scalable division of labor: use an acoustic-based tool for silence-driven metrics, and apply corrected transcripts with a machine-learning tool when extracting text-sensitive metrics.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"5 1","pages":"Article 100302"},"PeriodicalIF":0.0,"publicationDate":"2026-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1016/j.rmal.2026.100301
Sergei Sikorskii, María Luisa Carrió-Pastor
This study investigates language economy strategies in Spanish political discourse on X, analysing how users optimize communication. Through analysis of posts, we identify patterns of linguistic adaptation across syntactic and morphological dimensions. The research combines computational linguistics with traditional discourse analysis to examine strategy distribution, comprehensibility, and effectiveness. Results reveal a preference for syntactic strategies over morphological modifications. Message comprehensibility remains high despite substantial compression, challenging assumptions about the economy-clarity trade-off. Thread depth analysis shows peak strategy diversity at moderate depths, suggesting an optimal complexity point in digital political discourse. The study extends platform vernacular theory by demonstrating how political actors adapt linguistic strategies while maintaining effectiveness. These findings contribute to understanding how languages adapt to digital environments and have implications for political communication strategies, platform design, and digital literacy education.
{"title":"Mapping language economy strategies in Spanish political discourse on X","authors":"Sergei Sikorskii, María Luisa Carrió-Pastor","doi":"10.1016/j.rmal.2026.100301","DOIUrl":"10.1016/j.rmal.2026.100301","url":null,"abstract":"<div><div>This study investigates language economy strategies in Spanish political discourse on X, analysing how users optimize communication. Through analysis of posts, we identify patterns of linguistic adaptation across syntactic and morphological dimensions. The research combines computational linguistics with traditional discourse analysis to examine strategy distribution, comprehensibility, and effectiveness. Results reveal a preference for syntactic strategies over morphological modifications. Message comprehensibility remains high despite substantial compression, challenging assumptions about the economy-clarity trade-off. Thread depth analysis shows peak strategy diversity at moderate depths, suggesting an optimal complexity point in digital political discourse. The study extends platform vernacular theory by demonstrating how political actors adapt linguistic strategies while maintaining effectiveness. These findings contribute to understanding how languages adapt to digital environments and have implications for political communication strategies, platform design, and digital literacy education.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"5 1","pages":"Article 100301"},"PeriodicalIF":0.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1016/j.rmal.2026.100300
Chao Han
Human-generated translation and interpreting (T&I) are routinely evaluated in domains such as language education and professional certification. While artificial intelligence (AI) is increasingly used for automatic assessment, little research has examined its application to human T&I. Drawing on rigorous database search and screening, this systematic review attempts to close this gap. Based on a curated corpus of 69 studies, we identify important trends in assessment design, model architecture, and validation practice. The data analysis shows a marked increase in research since 2020, with a dominant focus on English-Chinese T&I, primarily within educational contexts. Most studies employed feature-based machine learning models or repurposed machine translation metrics for scoring, while only a minority explored end-to-end large language models. Benchmark construction was found to be inconsistently reported, with many studies omitting key information about rater qualification, training, reliability, and scoring criteria. Validation practices primarily relied on correlations with human benchmark scores, with limited evidence of convergent validity or cross-condition generalizability. Notably, post-hoc explainability, a crucial step for ensuring transparency in opaque AI systems, was rarely implemented. Overall, this review highlights both progress and persistent challenges in AI-based T&I assessment. While AI holds promise for enhancing assessment efficiency and scalability, methodological limitations and transparency gaps currently constrain its responsible use. We recommend improved reporting standards, multi-pronged validation strategies, development of large annotated benchmark datasets, and greater attention to model interpretability and explainability. These steps are essential for building robust, trustworthy AI systems for automatic T&I assessment.
{"title":"Artificial intelligence-based automatic evaluation of human translation and interpreting: A systematic review of assessment and validation practices","authors":"Chao Han","doi":"10.1016/j.rmal.2026.100300","DOIUrl":"10.1016/j.rmal.2026.100300","url":null,"abstract":"<div><div>Human-generated translation and interpreting (T&I) are routinely evaluated in domains such as language education and professional certification. While artificial intelligence (AI) is increasingly used for automatic assessment, little research has examined its application to human T&I. Drawing on rigorous database search and screening, this systematic review attempts to close this gap. Based on a curated corpus of 69 studies, we identify important trends in assessment design, model architecture, and validation practice. The data analysis shows a marked increase in research since 2020, with a dominant focus on English-Chinese T&I, primarily within educational contexts. Most studies employed feature-based machine learning models or repurposed machine translation metrics for scoring, while only a minority explored end-to-end large language models. Benchmark construction was found to be inconsistently reported, with many studies omitting key information about rater qualification, training, reliability, and scoring criteria. Validation practices primarily relied on correlations with human benchmark scores, with limited evidence of convergent validity or cross-condition generalizability. Notably, post-hoc explainability, a crucial step for ensuring transparency in opaque AI systems, was rarely implemented. Overall, this review highlights both progress and persistent challenges in AI-based T&I assessment. While AI holds promise for enhancing assessment efficiency and scalability, methodological limitations and transparency gaps currently constrain its responsible use. We recommend improved reporting standards, multi-pronged validation strategies, development of large annotated benchmark datasets, and greater attention to model interpretability and explainability. These steps are essential for building robust, trustworthy AI systems for automatic T&I assessment.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"5 1","pages":"Article 100300"},"PeriodicalIF":0.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1016/j.rmal.2026.100296
Huiying Cai , Yan Tang , Xun Yan
This tutorial introduces the application of unsupervised Gaussian Mixture Model (GMM) clustering to identify second language (L2) performance profiles. GMM employs a probabilistic clustering technique that accommodates overlapping profile membership and provides a flexible method for analyzing high-dimensional performance data commonly encountered in L2 research. Using L2 writing assessment data from a local English placement test, we present a step-by-step analytical pipeline, covering data preparation, dimensionality reduction, model selection, visualization, and interpretation. This approach is adaptable to other performance modalities (e.g, speaking) and can be enriched with additional performance features to support a more comprehensive understanding of L2 performance and underlying language ability.
{"title":"A tutorial on unsupervised Gaussian mixture model for performance clustering in second language research","authors":"Huiying Cai , Yan Tang , Xun Yan","doi":"10.1016/j.rmal.2026.100296","DOIUrl":"10.1016/j.rmal.2026.100296","url":null,"abstract":"<div><div>This tutorial introduces the application of unsupervised Gaussian Mixture Model (GMM) clustering to identify second language (L2) performance profiles. GMM employs a probabilistic clustering technique that accommodates overlapping profile membership and provides a flexible method for analyzing high-dimensional performance data commonly encountered in L2 research. Using L2 writing assessment data from a local English placement test, we present a step-by-step analytical pipeline, covering data preparation, dimensionality reduction, model selection, visualization, and interpretation. This approach is adaptable to other performance modalities (e.g, speaking) and can be enriched with additional performance features to support a more comprehensive understanding of L2 performance and underlying language ability.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"5 1","pages":"Article 100296"},"PeriodicalIF":0.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-21DOI: 10.1016/j.rmal.2026.100298
Shinya Uekusa , Sally Carlton , Sylvia Nissen , Fernanda Fernandez Zimmermann , Jay Marlowe , Fareeha Ali , Wondyrad A. Asres , Ginj Chang , Rami Elsayed , Jia Geng , D.H.P.S. Gunasekara , Jean Hur , Rika Maeno , Minh Tran , Wahida Zahedi , Stephen May , Tyron Love
This paper explores the tensions between challenging and unintentionally reinforcing linguicism in a large-scale research project on multilingual crisis communication during the COVID-19 pandemic in Aotearoa New Zealand. Our study involved a multilingual and multicultural research team conducting interviews in 14 different languages, with a methodological commitment to linguistic justice and inclusive research. Using collective self-reflection, we critically examined how our positionalities, language practices and research design, though intended to be counterhegemonic, sometimes reproduced dominant language ideologies. In this paper, we explore three key tensions: 1) the paradoxical privilege and power of bi-/multilingual researchers; 2) the internalisation of linguicism among participants; and 3) the challenges of translating emotional and cultural nuances. These findings reveal the complexity and paradoxes inherent in inclusive multilingual research, demonstrating how even well-intentioned practices can reproduce symbolic violence and linguicism. We argue for deeper reflexivity, methodological humility, and structurally transformative approaches that centre epistemic justice and critically challenge the institutional and ideological roots of linguicism. This paper contributes to critical language studies, disaster research and decolonising methodologies, providing both theoretical insights and practical guidance for researchers working with linguistic minorities.
{"title":"Overcoming and reinforcing linguicism? Language, power and critical reflexivity in a large multilingual research team","authors":"Shinya Uekusa , Sally Carlton , Sylvia Nissen , Fernanda Fernandez Zimmermann , Jay Marlowe , Fareeha Ali , Wondyrad A. Asres , Ginj Chang , Rami Elsayed , Jia Geng , D.H.P.S. Gunasekara , Jean Hur , Rika Maeno , Minh Tran , Wahida Zahedi , Stephen May , Tyron Love","doi":"10.1016/j.rmal.2026.100298","DOIUrl":"10.1016/j.rmal.2026.100298","url":null,"abstract":"<div><div>This paper explores the tensions between challenging and unintentionally reinforcing linguicism in a large-scale research project on multilingual crisis communication during the COVID-19 pandemic in Aotearoa New Zealand. Our study involved a multilingual and multicultural research team conducting interviews in 14 different languages, with a methodological commitment to linguistic justice and inclusive research. Using collective self-reflection, we critically examined how our positionalities, language practices and research design, though intended to be counterhegemonic, sometimes reproduced dominant language ideologies. In this paper, we explore three key tensions: 1) the paradoxical privilege and power of bi-/multilingual researchers; 2) the internalisation of linguicism among participants; and 3) the challenges of translating emotional and cultural nuances. These findings reveal the complexity and paradoxes inherent in inclusive multilingual research, demonstrating how even well-intentioned practices can reproduce symbolic violence and linguicism. We argue for deeper reflexivity, methodological humility, and structurally transformative approaches that centre epistemic justice and critically challenge the institutional and ideological roots of linguicism. This paper contributes to critical language studies, disaster research and decolonising methodologies, providing both theoretical insights and practical guidance for researchers working with linguistic minorities.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"5 1","pages":"Article 100298"},"PeriodicalIF":0.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-16DOI: 10.1016/j.rmal.2026.100299
Farahnaz Faez , Michael Karas , Ata Ghaderi
Language teacher beliefs are one of the main strands of teacher education research, and numerous studies explore how teacher education programs affect the development of such beliefs through the enacted program. There is a paucity of research, however, on the methodological design of these studies and what characterizes their initiatives. The aim of this research synthesis was to review and map out the methodological arrangements of the studies and illustrate how they implement their desired program. A comprehensive search was done in the three databases of Web of Science, Scopus, and Google Scholar with keywords related to language teacher beliefs and cognitions. A total number of 104 studies were identified and coded in 10 sections, which include such factors as (a) methodology, (b) theoretical framework, (c) data collection instruments, (d) number of participants, and (e) participants’ career stage. The results indicate an overall lack of clarity with the ontological framework of the studies, which is especially pronounced in light of the prevalence of qualitative designs within the corpus. Further, there is often a misalignment between the studies’ ontological paradigm and the methodological choices made. The findings call for greater ontological transparency, a higher degree of alignment between the theoretical framework and the methodological blueprint of research studies, and a broader and more versatile toolkit in identifying, examining, and transforming language teacher beliefs. The synthesis provides recommendations for advancing research on teacher beliefs through the methodological apparatus in this strand.
语言教师信念是教师教育研究的主要方向之一，许多研究探讨了教师教育计划如何通过制定的计划影响这种信念的发展。然而，关于这些研究的方法设计以及其主动性的特征的研究很少。本研究综合的目的是回顾和绘制出研究的方法安排，并说明它们如何实施预期的计划。在Web of Science、Scopus和谷歌Scholar三个数据库中全面检索与语言教师信念和认知相关的关键词。总共有104项研究被确定并编码为10个部分，其中包括(A)方法，(b)理论框架，(c)数据收集工具，(d)参与者人数，(e)参与者的职业阶段等因素。结果表明，研究的本体论框架总体上缺乏清晰度，这在语料库中普遍存在定性设计的情况下尤为明显。此外，在研究的本体论范式和所做的方法选择之间经常存在不一致。研究结果要求提高本体论的透明度，提高理论框架与研究方法蓝图之间的一致性，以及在识别、检查和转变语言教师信念方面使用更广泛、更通用的工具包。综合提供了建议，以推进研究的教师信念通过这一链的方法设备。
{"title":"Language teacher beliefs and teacher education programs: A 25-year methodological synthesis (2000-2024)","authors":"Farahnaz Faez , Michael Karas , Ata Ghaderi","doi":"10.1016/j.rmal.2026.100299","DOIUrl":"10.1016/j.rmal.2026.100299","url":null,"abstract":"<div><div>Language teacher beliefs are one of the main strands of teacher education research, and numerous studies explore how teacher education programs affect the development of such beliefs through the enacted program. There is a paucity of research, however, on the methodological design of these studies and what characterizes their initiatives. The aim of this research synthesis was to review and map out the methodological arrangements of the studies and illustrate how they implement their desired program. A comprehensive search was done in the three databases of Web of Science, Scopus, and Google Scholar with keywords related to language teacher beliefs and cognitions. A total number of 104 studies were identified and coded in 10 sections, which include such factors as (a) methodology, (b) theoretical framework, (c) data collection instruments, (d) number of participants, and (e) participants’ career stage. The results indicate an overall lack of clarity with the ontological framework of the studies, which is especially pronounced in light of the prevalence of qualitative designs within the corpus. Further, there is often a misalignment between the studies’ ontological paradigm and the methodological choices made. The findings call for greater ontological transparency, a higher degree of alignment between the theoretical framework and the methodological blueprint of research studies, and a broader and more versatile toolkit in identifying, examining, and transforming language teacher beliefs. The synthesis provides recommendations for advancing research on teacher beliefs through the methodological apparatus in this strand.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"5 1","pages":"Article 100299"},"PeriodicalIF":0.0,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-13DOI: 10.1016/j.rmal.2026.100297
Miaoru Lin, Dingjia Liu
The vast knowledge and great efficiency of Large Language Models have made it essential to demystify the language written by human beings and that generated by LLMs, particularly their interactional capability. As a pivotal genre in academic discourse, “abstract” serves both the informative and the communicative functions. This study aims to explore intersubjective discourse markers manifested in human-written abstracts and LLM-generated ones as well as their similarities and differences across disciplines. The results show that human writers employ a wider range of stance and engagement markers to facilitate intersubjective positioning. Human-written abstracts exhibit a more sophisticated linguistic realization of intersubjectivity through lexical resources, patterned phrases, and syntactic structures. The correspondence analysis reveals that human writers emphasize disciplinary distinctions, while LLMs adopt a convergent approach to achieving writer-reader interaction among different disciplines. These findings underscore human writers’ superiority in navigating complex writer-reader interaction in abstract writing. Though LLMs have demonstrated some potential in emulating intersubjective communication, their interactional capability falls short of that of human writers. The findings offer significant implications for deepening our understanding of the nature of LLMs and contributing to LLM-assisted EAP studies and EAP teaching across disciplines.
{"title":"Intersubjectivity as the distinguishing feature or common ground: A contrastive study between human-written abstracts and LLM-generated abstracts","authors":"Miaoru Lin, Dingjia Liu","doi":"10.1016/j.rmal.2026.100297","DOIUrl":"10.1016/j.rmal.2026.100297","url":null,"abstract":"<div><div>The vast knowledge and great efficiency of Large Language Models have made it essential to demystify the language written by human beings and that generated by LLMs, particularly their interactional capability. As a pivotal genre in academic discourse, “abstract” serves both the informative and the communicative functions. This study aims to explore intersubjective discourse markers manifested in human-written abstracts and LLM-generated ones as well as their similarities and differences across disciplines. The results show that human writers employ a wider range of stance and engagement markers to facilitate intersubjective positioning. Human-written abstracts exhibit a more sophisticated linguistic realization of intersubjectivity through lexical resources, patterned phrases, and syntactic structures. The correspondence analysis reveals that human writers emphasize disciplinary distinctions, while LLMs adopt a convergent approach to achieving writer-reader interaction among different disciplines. These findings underscore human writers’ superiority in navigating complex writer-reader interaction in abstract writing. Though LLMs have demonstrated some potential in emulating intersubjective communication, their interactional capability falls short of that of human writers. The findings offer significant implications for deepening our understanding of the nature of LLMs and contributing to LLM-assisted EAP studies and EAP teaching across disciplines.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"5 1","pages":"Article 100297"},"PeriodicalIF":0.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145976502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-09DOI: 10.1016/j.rmal.2026.100295
Benjamin Luke Moorhouse , Sal Consoli , Samantha M. Curle
{"title":"Research methods and generative artificial intelligence in applied linguistics","authors":"Benjamin Luke Moorhouse , Sal Consoli , Samantha M. Curle","doi":"10.1016/j.rmal.2026.100295","DOIUrl":"10.1016/j.rmal.2026.100295","url":null,"abstract":"","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"5 1","pages":"Article 100295"},"PeriodicalIF":0.0,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-03DOI: 10.1016/j.rmal.2025.100294
Xiaojing Zhao, Emmanuele Chersoni, Chu-Ren Huang, Han Xu
This study employs emotion analysis, a natural language processing technique, to examine how language models handle emotional content compared to human translators in video game localization. The analysis is based on a corpus consisting of Chinese subtitles from Black Myth: Wukong, their official English translations, and translations generated by a language model. The findings reveal that, despite similarities between humans and the language model in their translation of emotions, differences exist. Human translators often neutralize emotions through context-dependent strategies, such as omission, addition, and substitution, to address cultural sensitivities and enhance player engagement. In contrast, the language model relies on direct translation to preserve diverse emotions, including negative ones. Such an approach may risk misalignment with the preferences of target audiences due to limited adaptation of tone and cultural nuances. In addition, occasional mistranslation and hallucination were also found. This study highlights the promise of integrating language models into localization workflows and demonstrates the potential of emotion analysis for assessing translation accuracy.
{"title":"How do language models handle emotional content in video game localization? A computational linguistics approach","authors":"Xiaojing Zhao, Emmanuele Chersoni, Chu-Ren Huang, Han Xu","doi":"10.1016/j.rmal.2025.100294","DOIUrl":"10.1016/j.rmal.2025.100294","url":null,"abstract":"<div><div>This study employs emotion analysis, a natural language processing technique, to examine how language models handle emotional content compared to human translators in video game localization. The analysis is based on a corpus consisting of Chinese subtitles from <em>Black Myth: Wukong</em>, their official English translations, and translations generated by a language model. The findings reveal that, despite similarities between humans and the language model in their translation of emotions, differences exist. Human translators often neutralize emotions through context-dependent strategies, such as omission, addition, and substitution, to address cultural sensitivities and enhance player engagement. In contrast, the language model relies on direct translation to preserve diverse emotions, including negative ones. Such an approach may risk misalignment with the preferences of target audiences due to limited adaptation of tone and cultural nuances. In addition, occasional mistranslation and hallucination were also found. This study highlights the promise of integrating language models into localization workflows and demonstrates the potential of emotion analysis for assessing translation accuracy.</div></div>","PeriodicalId":101075,"journal":{"name":"Research Methods in Applied Linguistics","volume":"5 1","pages":"Article 100294"},"PeriodicalIF":0.0,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145884274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-22DOI: 10.1016/j.rmal.2025.100293
Yohei Nakanishi , Osamu Takeuchi
This study aimed to adapt an L2 engagement scale—originally developed by Teravainen-Goff (2023) for secondary school students in the United Kingdom—for young language learners (YLLs) in the Japanese EFL context, with particular attention to age-appropriateness throughout the questionnaire adaptation process. The present study implemented a rigorous six-step process to adapt the scale for YLLs and assessed its validity and reliability. Three hundred ninety-nine elementary school students in a Japanese EFL context completed the adapted L2 engagement scale. The exploratory factor analysis identified four key factors of L2 engagement, including “perceived quality of engagement with peers,” “perceived quality of engagement with teachers,” “intensity of effort in learning,” and “perceived quality of engagement with teaching content.” The validity and reliability of the adapted L2 engagement scale were further confirmed through confirmatory factor analysis. This study provides a detailed account of the questionnaire adaptation process to ensure methodological rigor and transparency. Our findings not only contribute to a better understanding of YLLs’ engagement in EFL classrooms but also establish methodologically sound questionnaire-adaptation procedures for under-researched populations in the field of applied linguistics.
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