11.2022
Special Issue on Teaching and Teacher Development in Technology-enhanced Language Learning
As applied linguists specialising in vocabulary studies, we wish to redress this imbalance and explore vocabulary teaching and learning in EMI settings.
In this special topic issue, we want to explore how English language teaching and learning is being transformed in multilingual contexts in response to global developments, including but not limited to the pandemic. Specifically, we are interested in qualitative studies that critically examine how opportunities for English teaching and learning have changed in response to increasingly diverse contexts and under unprecedented conditions (pandemic, geopolitical tensions, and migration).
05.2022
International Journal of TESOL Studies (IJTS) is seeking proposals from prospective guest editors for the 2024 special topic issue.
This article addresses the ergodicity problem in Second Language Learning and Teaching (SLLT), emphasizing the need for methodological advancements that account for the dynamic, nonlinear nature of language development. Traditional research methods in SLLT, often based on aggregated group data, assume ergodicity—a concept that equates group-level averages with individual-level processes, a premise that fails to capture the complexity of individual learners’ trajectories. The article advocates for a shift toward intensive longitudinal data (ILD) collection and Dynamic Structural Equation Modeling (DSEM) to model the inherent non-ergodic nature of language acquisition. These methods facilitate a bottom-up generalization approach, where individual trajectories are examined for recurring dynamic patterns before testing these patterns across multiple cases to uncover shared mechanisms. By focusing on intra-individual dynamics, ILD and DSEM provide a nuanced understanding of language learning processes, revealing how emotional and cognitive factors fluctuate within individuals over time. This bottom-up approach contrasts with traditional top-down methodologies and offers a more accurate and personalized representation of language learning, enabling both the identification of common trends across individuals and the retention of individual variability. Despite challenges like sample size requirements and data complexity, the integration of ILD and DSEM is positioned as a transformative methodology for SLLT research, offering practical insights for future research.
Generative artificial intelligence (AI) has unsettled a long-standing premise of second language (L2) writing research: that the language through which a writer’s voice is realised originates with the learner. Voice (what a text conveys about its writer) and psychological ownership (whether writers feel the text is theirs) capture different aspects of a writer’s relationship to a text, yet have been theorised separately and rarely examined together. Using a sequential explanatory mixedmethods design, this study operationalised voice and ownership together in one sample of Saudi EFL undergraduates, most in their first year and new to AI, a population rarely examined in identityfocused research on AI-assisted writing. A questionnaire was completed by 178 students; 17 were interviewed using maximum variation sampling. Data were analysed using descriptive and inferential statistics and reflexive thematic analysis. AI use was assistance-oriented rather than generative, supportive use exceeding full-text generation (d = 0.86). Perceived voice was near the midpoint while ownership fell significantly below it; the two were strongly related (r = .608) yet not redundant. Supportive use was the strongest predictor of both perceptions, whereas generative use predicted ownership but not voice, and proficiency made a small, tentative contribution to voice only. Interviews showed ownership to be a graded judgement turning on transformation and accountability, and voice to rest on recognisability and level-match; full-text generation occupied the authorship boundary. These findings suggest pedagogy and assessment should focus less on whether AI is present than on whether learners understand, transform, and authorise its contribution.
Despite the increasing use of artificial intelligence (AI) tools in informal English-speaking practice, there is still limited understanding of multilingual learners’ subjective perceptions and engagement patterns in this context. To fill this gap, the current study aimed to identify core subjective factors and explore how learners with different first language (L1) backgrounds and English proficiency levels differ in their perception and engagement patterns. The study replicated the Q-methodology of Guo and Xia (2025), who explored learners’ perception of AI-mediated informal digital multilingual learning, in investigating perception of AI-assisted informal speaking practice (AI-IDESP) among multilingual undergraduate students in Malaysia. Using the Extended Unified Theory of Technology Acceptance and Use (UTAUT2), and a Q-Methodology, the present study surveyed and interviewed 30 multilingual undergraduate students in a public Malaysian university who had engaged in AI-IDESP and use at least one other language in daily communication. The findings show that performanceoriented use of AI, emotion-driven motivation and efficiency-driven but anxiety dependence influenced learners’ behavioural intentions and actual engagement with AI-IDESP. Students’ different English proficiencies were more likely to align with these different factors. Overall, by deepening our understanding of how learners’ AI-IDESP is shaped by performance orientation, emotional sensitivity, and efficiency-related anxiety, the study offers useful implications for learner-centred AI tool design in multilingual contexts.
Despite the growing uptake of Critical Content Analysis (CCA) in EFL education research, its methodological enactment remains insufficiently explicated. Many studies invoke CCA as an analytic framework yet offer limited transparency regarding the recursive, non-linear processes through which critical interpretations are developed. Addressing this methodological gap, this article clarifies the theoretical underpinnings and analytic procedures of CCA and proposes accessible yet theoretically flexible guidelines for conducting rigorous analysis in EFL research. The paper delineates the key phases of CCA and illustrates their implementation through examples drawn from EFL education. To render the analytic process visible, we reexamine two published EFL studies and unpack the recursive and iterative moves that shaped their analyses—processes that are rarely documented in CCA-based scholarship. The first exemplar demonstrates how recursive engagement with data and theory prompted the integration of additional theoretical perspectives following initial thematic patterns, leading to a re-interpretation of findings. The second study illustrates the iterative nature of thematic development, detailing how multiple cycles of categorization were negotiated before analytically robust patterns emerged. By foregrounding the methodological logic and recursive analytic practices of CCA in EFL contexts, this article contributes to greater procedural transparency and offers practical guidance for researchers committed to advocating more equitable and socially just English language education.
This study investigates how task-based language teaching (TBLT) enacted in a metaverse environment supports Thai EFL undergraduates’ oral communication and how students experience such learning. Addressing concerns about Thai graduates’ low English proficiency and limited opportunities for meaningful speaking practice, the study embeds TBLT cycles in gamified metaverse-based environment. An explanatory sequential mixed-methods design was employed with 67 non–English-major undergraduates from Thai public universities selected through purposive volunteer sampling. Metaverse-based oral tasks were implemented for four weeks, with oral communication tests to assess range, accuracy, fluency, coherence, and pronunciation. The tests were administered before and after the intervention, followed by semi-structured interviews with 33 students. Paired-sample t-tests showed significant gains in overall oral communication with improvements across all five dimensions and the largest effect for lexical range. Thematic analysis indicated that the tasks in the gamified metaverse environment promoted genuine language use and reflection. In addition, the avatar-mediated “safe space” reduced anxiety and built confidence, and cross-institution interaction together with promoting authentic collaboration, although the issues of platform stability, connectivity, and screen fatigue posed some challenges. The findings suggest that carefully designed gamified metaverse-based TBLT can enhance both linguistic performance and affective readiness for communication. Future research should employ larger and more diverse samples, longer interventions with delayed post-tests, and comparative designs, such as metaverse vs. non-metaverse TBLT or gamified vs. non-gamified tasks, to clarify boundary conditions and support wider implementation.
This paper argues that generative artificial intelligence (AI) is becoming an active partner in English language teaching (ELT) for lesson planning, communicative practice, feedback, and assessment support. However, classroom use often depends on ad hoc prompts that yield inconsistent pedagogical fit. Because language tasks are shaped by proficiency, skill targets, genre/ register expectations, and sociocultural conditions, ELT needs prompting structures that function as instructional specifications rather than generic technical instructions. In response, the paper proposes CLARIFIES+, a contextualized prompting framework for teaching, learning, and assessment that systematizes ELT-relevant task conditions and boundaries. After reviewing major prompting approaches (e.g., zero-shot, few-shot, chain-of-thought, and contextual prompting) and prominent prompting frameworks, the paper identifies a gap between technical prompt guidance and ELTcentered variables, evaluative alignment, and ethical constraints. CLARIFIES+ addresses this gap through nine core components, namely Context, Limitations, Audience, Role, Intent, Format, Inputs, End product, and Style, plus an adaptive “+” layer for process controls, follow-up clarification, tool use, and safeguards that support accuracy, integrity, and learner safety. The paper illustrates how the framework can support differentiated instruction, learner prompt literacy and self-regulation, and more transparent, consistent AI-supported formative and summative assessment. It concludes by noting limitations, implementation challenges, and future research directions.
Immersive virtual reality (IVR) has the potential to foster L2 speaking development by simulating communication, enabling embodied practice, and delivering multimodal feedback. This study investigates how a commercial IVR application supports public speaking practice in the target language when app-generated feedback is paired with instructor facilitation. Intermediate-high Korean learners in a pre-capstone course used IVR to rehearse speeches for a public speaking contest. The app offered real-time feedback and performance analytics, while the instructor provided guidance informed by these analytics and their own observation. Drawing on pre/post surveys, IVR session recordings, and exit interviews, we analyze changes in learners’ confidence, skill growth, and engagement with multimodal and multisource feedback. Framed by Activity Theory, our findings illustrate how VR simulations, when combined with tailored personalized feedback, cultivate holistic public speaking skills and surface implementation challenges. The study provides empirical insights into how a commercial IVR application can be pedagogically integrated in language education and offers design implications for building engaging, data-informed learning environments that provide individualized support for L2 speaking development.
This study examines the intricate connection between teachers’ agency and their levels of artificial intelligence (AI) literacy. Utilizing qualitative data from two teachers, the analysis showed that Christine’s proactive stance led to positive changes, while Sam’s reluctance reflected the limitations resulting from insufficient guidance and support. The results highlighted the influence of institutional assistance, peer collaboration, and individual beliefs on teachers’ engagement with AI in their classrooms. The paper emphasizes the need to cultivate a supportive environment for AI literacy among teachers, allowing them to adapt to technological changes while managing the challenges of their professional roles.
The integration of generative AI tools such as ChatGPT has transformed English as a Foreign Language (EFL) education, offering new opportunities for supporting writing, research, and critical inquiry. However, unguided use of AI may foster cognitive passivity and over-reliance, highlighting the need for targeted pedagogical interventions. Grounded in experiential learning theory, this quasiexperimental study, employing a pretest–posttest control group design, evaluated the effectiveness of a 90-minute workshop, the Critical AI Engagement Cycle, in enhancing EFL learners’ critical thinking skills when using ChatGPT. Despite the short duration, the workshop included multiple scaffolded activities designed to stimulate immediate critical reflection. Seventy-two undergraduate and graduate students at a Vietnamese public university participated, with 38 assigned to the experimental group and 34 to the control group. Participants were selected using convenience sampling based on course enrollment and availability. Pre- and post-test results demonstrated statistically significant improvements in overall critical thinking and each of the four subdomains— analytical skills, logical reasoning, evidence evaluation, and open-mindedness—among participants in the experimental group. Notably, the consistent and large effect sizes across all critical thinking subdomains (Cohen’s d = 0.94 to 1.23) underscore the robust impact of the intervention. The experimental group significantly outperformed the control group in post-intervention critical thinking scores, even after controlling for pretest scores, gender, prior AI knowledge, and AI skill level, as confirmed by ANCOVA analyses. The results suggest that even brief, theoretically grounded interventions can significantly enhance critical thinking skills in AI-mediated EFL environments. These findings underscore the importance of evidence-informed practices and highlight the need for explicit critical thinking training to ensure sustainable and responsible educational practices in the age of generative AI.
Neuroscience is gaining increasing attention in English language teaching as recent research seeks to provide new insights into learning and second language acquisition. However, understandings from neuroscience have yet to inform English language teacher learning. This article addresses this gap by reporting on a 2.5-year longitudinal research project in which seven Japanese university English language teachers learned about neuroscience by initially participating in a 15-week teacher professional learning approach, namely, Learning Study. To enable accessible and applicable learning of neuroscience principles for our participants, teacher learning was focused on specific brain-based principles generally considered to be important in English language teaching (e.g., memory storage and retrieval, and the brain-body connection). Data were triangulated through focus group meetings and pre-, immediate post-, and delayed postLearning Study interviews, enabling an exploration of teacher-participants’ developing practices and cognitions (i.e., beliefs and knowledge) about brain-based principles. Findings revealed substantial development of participants’ practices and cognitions about brain-based principles with each teacher-participant focusing on a different area of interest intertwined with facilitating and impeding factors. This paper offers novel insights into the use and development of sustainable teacher-professional learning.
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This study examined the defensibility of using GPT-4 for automated essay scoring, using a ManyFacet Rasch Model analysis. Forty English for academic purposes student essays were rated by GPT- 4 and four trained educators to assess nuances in rubric application, severity, leniency, and bias. Findings suggest that while GPT-4 tended to avoid the use of extreme scores, exhibiting a moderate central tendency rating, it does show a high level of consistency in its scoring behavior. This study contributes to understanding the extensions and limitations of using Generative AI tools in scoring essays, and provides insights into the use of AI tools in assessing writing.
How research limitations are acknowledged can influence the perceived value of a study. However, limitation statements have received limited attention in EAP writing, especially in master’s dissertations, where student writers must navigate both genre conventions and the specific constraints they encountered during the research process. To fill this gap, this study aims to explore the discourse practices underlying the construction of the Limitations section in master’s dissertations. Based on a corpus containing 85 Limitations sections from exemplar dissertations recognized by the ELT Master’s Dissertation Award, this study examines the rhetorical structure, evaluative focus, and linguistic realization of this part-genre within the move analysis framework. The findings show that the Limitations section follows a five-move rhetorical structure that allows for variation, serving both reflective and persuasive purposes. Among the four evaluative focuses identified, limitations involving the evaluation of research design and analysis are the most frequently acknowledged, and this type of limitation is typically addressed using the most complex rhetorical strategies, allowing writers to justify, mitigate, or reframe their research constraints. The study also finds that transitions from limitation statements to other communicative functions are often marked by overt linguistic signals. These findings have pedagogical implications for EAP instruction, particularly in raising students’ genre awareness and offer suggestions for genre-based pedagogy.
To capture Vietnamese high school English teachers’ driving forces and barriers in enacting the Competency-Based English Teaching Curriculum (CBETC), this study presents the method of designing and developing a questionnaire. It also validates its structure and reliability when used as a survey in the classroom. The relevant theories were analyzed to construct their content and form. Two techniques, Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA), were utilized to assess the construct validity and reliability, while internal consistency was evaluated using Cronbach’s alpha (α). The results showed that the questionnaire identified dimensions of impetus through three distinct factors: English curriculum-related, teacher-related, and teaching method-related. Similarly, hindrances were measured through three corresponding factors: English curriculum-related, teacher-related, and institution-related aspects. EFA and CFA results confirmed the construct validity of the instrument, with the impetus scale comprising 10 items across three factors (KMO = 0.77; p=0.000; Chi-square/df = 2.066, GFI = 0.976, CFI = 0.982, RMSEA = 0.045; PCLOSE = 0.665; α = 0.79; and CR=0.825), and the hindrance scale comprising 12 items also grouped into three factors (KMO = 0.84; p<0.001; Chi-square/df = 1.912; GFI = 0.969; CFI = 0.982; RMSEA = 0.042; PCLOSE = 0.845; α = 0.84; and CR=0.845). These findings indicate that the instrument demonstrates acceptable levels of validity and reliability, supporting its use in future research on teachers’ motivational and contextual factors influencing CBETC implementation.
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