Gamification is increasingly viewed as a promising approach for supporting student motivation and emotional well-being in crisis-affected educational contexts. This study examines the impact of gamified blended English language learning environments in higher education during wartime in Ukraine. Using a quasi-experimental design, the study involved 84 undergraduate students from a Ukrainian university who were divided into experimental (gamified instruction) and control (traditional instruction) groups over a 12-week period. Data were collected through pre- and post-intervention questionnaires measuring learning motivation (5-point Likert scale) and situational anxiety (STAI-S), supplemented by academic performance indicators and platform activity data. The data were analyzed using a mixed-design ANOVA to examine changes over time and differences between groups. The results suggest an increase in students’ motivation and a decrease in situational anxiety, with more pronounced changes observed in the experimental group. The findings indicate that gamification may contribute to supporting student engagement and emotional well-being in blended learning environments under conditions of instability. These results highlight the potential of gamified approaches for maintaining learning continuity in crisis contexts.
Writing in English as a foreign language remains a persistent challenge for learners in many instructional contexts where classroom practices are often individual and product-oriented. Although collaborative writing instruction (CWI) has received increasing attention, evidence regarding its association with writing motivation and creativity-related writing performance in underrepresented contexts remains limited. This study compared CWI and individual writing instruction (IWI) among first-year EFL students. An exploratory classroom comparison with an embedded mixed-methods design was conducted using two intact classes (N = 104) over 12 weeks. Data were collected through paragraph writing tests, a writing motivation questionnaire, and post-intervention student reflections. Quantitative data were analyzed using pretest-adjusted regression models equivalent to ANCOVA, while qualitative data were analyzed thematically to contextualize the quantitative data. The findings indicated that students in the CWI class reported higher posttest writing motivation after controlling for baseline differences. However, no statistically significant adjusted difference was observed in creativity-related writing performance. Student reflections showed perceptions of increased engagement, confidence, and idea sharing, alongside reported challenges related to unequal participation and time constraints. Overall, the findings suggest that CWI may be more strongly associated with writing motivation than with creativity-related writing performance in this context.
The study explores the differential effects of AI-generated, teacher, and peer feedback on improving students’ writing development with digital proficiency as the factorial variable. A quasi-experimental mixed-methods approach was administered to 150 Indonesian university undergraduate English education students divided into high, moderate, and low digital proficiency levels. Pre- and post-tests, a digital proficiency survey, and semi-structured interviews were used to collect data. Statistical analyses, e.g., paired-sample t-tests, Generalized Linear Mixed Model (GLMM), and independent t-tests, established that teacher feedback generated the largest improvements on content (d = 0.41, p = 0.006) and grammar (d = 0.45, p = 0.002) but also that AI-distributed feedback had its most significant effect on the grammar (d = 0.34, p = 0.025). Peer feedback helped in content generation (d = 0.36, p = 0.013) but had little influence on structural accuracy. GLMM results indicated a significant interaction between feedback type and digital proficiency (F(2, 143) = 3.410, p = .036), demonstrating that the effectiveness of the feedback modality was moderated by the student’s technological readiness. Specifically, students with higher digital proficiency scores showed a more significant positive response to AI-generated feedback, leveraging its immediacy for structural revisions. Qualitative findings indicated that high digital proficiency learners valued AI feedback for being quick and transparent while low digital proficiency students found teacher feedback more structured and comprehensible. The findings identify the importance of including diversified types of feedback in line with students’ digital proficiency for writing improvement in EFL contexts.
Critical incidents (CIs) are unexpected events that shape early-career language teachers’ professional development. Although research has explored how CIs impact teachers’ beliefs and practices, less attention has focused on agency—how teachers narrate, respond to, and position themselves in relation to these events—in shaping how CIs are integrated into their professional identities. This study draws on complex dynamic systems theory and Hiver and Dörnyei’s (2017) language teacher immunity framework to examine how seven early-career English language teachers interpret and respond to CIs, and the role agency plays in shaping their emerging professional identities. Analysing interview data, we trace participants’ responses across the four phases of Hiver and Dörnyei’s framework: triggering, linking, realignment, and stabilisation. Findings show that teachers’ practices either adapted into productive, stable states of growth and resilience by leveraging contextual and social affordances or transformed into maladaptive states of resistance. We interpret outcome differences based on variations in how teachers assert agency in response to initial conditions such as their backgrounds, experiences, and environments. The findings contribute to understanding the interplay between agency and context in identity construction and underscore the importance of communities of practice and supportive environments that help early-career TESOL professionals grow from CIs.
Artificial intelligence (AI) has rapidly expanded its presence in applied linguistics, transforming the ways languages are taught, assessed, and researched. The edited volume by Carol Anne Chapelle, Gulbahar Hajira Beckett, and Jim Ranalli (2024) offers a timely and balanced examination of how recent developments in AI—particularly generative AI systems such as large language models (LLMs)—are reshaping both theoretical discussions and practical applications within the field. This collection brings together 15 chapters organized into 4 major sections that address topics such as the historical development of AI in linguistics, AI-enhanced assessment, implications for linguistic research, and directions for future inquiry. Rather than promoting uncritical enthusiasm, the editors encourage thoughtful reflection on the affordances and limitations of AI-driven approaches, emphasizing responsible innovation and pedagogical alignment. The chapters collectively argue that AI reshapes core applied linguistics concerns, including learner agency, teacher roles, and methodological validity, while also questioning ethics and transparency issues in data-driven research. [First paragraph of review]
George Yule’s The Study of Language is one of the most widely adopted introductory linguistic textbooks for undergraduate students. Written for students with limited or no prior knowledge, the book is widely praised for its clarity, accessibility, and systematic presentation of key areas of linguistics. The current eighth edition maintains this commitment, while adding updated linguistic data and expanding its coverage of recent developments in linguistic research. In addition, beyond its value as an introductory linguistic text, Yule’s book offers a succinct overview of the language system, making it an excellent reference tool for second language teachers seeking to understand and assess second language learners’ developmental stages. [First paragraph of review]
Since the introduction of ChatGPT in late 2022, AI chatbots have become common tools in language learning. While they provide convenient access to input and feedback, questions remain about their ability to support pragmatic competence—the capacity to use language appropriately in social and cultural contexts. A semi-structured interview with polyglot Todd Hochstatter, who learned Arabic prior to the rise of AI-based tools, reveals how immersion, tutoring, and sustained interaction with native speakers shaped his pragmatic competence. Three themes emerged: the initial reserve of native speakers to use Arabic with him, exposure to conflicting language use among speakers, and sensitivity to variation across Arabic dialects. The findings highlight the importance of diverse, context-rich human interaction in developing pragmatic competence and raise questions about how such experiences are supported in AI-mediated language learning.
Language teachers often need weekly routines that are workable and consistent rather than one-off activities that feel engaging but do not hold up across a course. This creates a practical problem as teachers still have to decide what deserves regular class time, and how to justify those choices when time and attention are limited (Nassaji, 2012; Medgyes, 2017). In The Twenty Most Effective Language Teaching Techniques, Paul Nation presents twenty techniques organised within a coherent, strand-based framework for planning and evaluating classroom work. The promise for busy teachers is practical and immediate since the framework links day-to-day choices to measurable learning outcomes. From the outset, the author asks readers to consider who is engaged, what is processed, how much language is handled, and how deeply learners work with it, a stance that helps avoid lively tasks that generate little learning (Hiver et al., 2024). [First paragraph of review]
While research examining foreign language fluency development in timed writing has shown its potential to bring about improvement in language proficiency, it remains unclear how different groups of learners can benefit from this activity. To investigate the role of proficiency, this study examined the writing produced by 35 Japanese English as a foreign language university students from low- and higher-level classes. 422 samples of timed writing were collected over 13 weeks and analyzed for development in complexity, accuracy, lexis, and fluency (CALF). Results showed significant improvement in writing fluency for both groups. However, the higher-level group made steady gains across the semester, while growth in the lower-level group was limited to the initial weeks of the study. Furthermore, examination of the developmental trajectories of the CALF measures suggested the higher-level group was able to attend to both fluency and complexity, but the lower-level group was unable to do so. Both groups showed a great deal of variation in lexis and accuracy. The results suggest that learners need to pass a proficiency threshold in order for timed writing to benefit fluency development. Finally, although both groups reacted positively to the activity, differences were also seen in their reasoning.
As learning is in part driven by individual interest (Hidi & Renninger, 2019), including language learning (Tin, 2013), it is crucial that teachers support students’ interests in the classroom. However, since students’ interests invariably differ with some having very specific, niche interests, and others having popular ones, how can teachers bridge the gap to incorporate diverse student interests into the classroom? [First paragraph of review]
Note-taking in English as a foreign language (EFL) contexts has transformed significantly from traditional pen and paper formats to encompass more digital applications, including a wide array of Artificial Intelligence (AI) note-taking tools. As AI note-taking becomes a more prominent strategy for EFL learners, its rapid growth introduces new pedagogical and ethical complexities that educators must address. We conceptualize AI note-taking applications as mediational tools that shape and extend learners’ cognitive and linguistic development. By providing real-time transcription, translation, and contextual definitions of difficult and unfamiliar words or phrases, AI tools scaffold listening comprehension and reduce the cognitive load of manual note-taking, which allows learners to focus more on active listening and comprehension. This article proposes a four-phase, instructional model grounded in Sociocultural Theory for implementing AI-augmented note-taking with EFL learners in higher education. We conclude by discussing pedagogical implications for integrating AI note-taking to sustain active participation and foster learners’ movement toward greater autonomous note-taking and comprehension.
Plagiarism remains a persistent ethical and academic issue despite the widespread use of automated detection tools such as Turnitin. Students continue to develop ways to hide plagiarism through linguistic changes to circumvent the detection mechanisms of the software, making it difficult for educators to identify these acts of dishonesty. This study employed a linguistic approach to examine instances of undetected plagiarism in postgraduate academic papers from two universities in the Philippines. Anchored in Celce-Murcia and Larsen-Freeman’s (1999) grammatical metalanguage and Sousa-Silva’s (2014) framework of plagiarism deception strategies, the study analyzed thirty papers that scored at least ten percent similarity in Turnitin reports. All unflagged sections of each paper were manually cross-referenced with identified online sources and examined at the subsentential, sentential, and suprasentential levels to determine how linguistic modifications disguise copied material. Results revealed that the most frequent alteration type was subsentential manipulation (86.94%), particularly through word insertion and substitution, followed by word reordering and paraphrasing. These linguistic modifications successfully disrupted Turnitin’s string-matching algorithms, which made the possible plagiarized content be undetectable. The findings demonstrate the limits of extrinsic plagiarism detection systems and highlight the necessity of integrating linguistic analysis into plagiarism review procedures. The study recommends developing hybrid detection frameworks combining computational and forensic linguistic methods to enhance academic integrity monitoring in higher education institutions.
This quasi-experimental study investigates the impact of a program combining Extensive Reading (ER) and Intensive Reading (IR) on vocabulary acquisition, reading fluency, and proficiency in the reading, listening, and writing skills, in an Asian EFL context. Despite global evidence supporting ER’s benefits, its implementation in EFL contexts — especially in Asian countries — remains limited due to institutional resistance, misconceptions about ER principles, and lack of teacher training. This study involved four groups of second-year English majors (n = 117), randomly assigned to treatment and control groups at the class level. During each of the 15 weekly 150-minute sessions, both the control and treatment groups engaged in IR for 100 minutes; afterward, the two control groups continued with IR while the two treatment groups engaged in ER using level-appropriate graded readers. Statistical analyses revealed that the treatment groups made significantly greater gains across all measures compared to controls. These findings highlight ER’s pedagogical value in developing multiple dimensions of language competence in under-resourced EFL contexts and emphasize the need for broader integration of ER into English language curricula.
There are undeniably some contributions of AI to language learning, yet it is still unclear how such technology should be used with known language learning techniques to improve instruction. To address this gap in our understanding, a survey concerning metacognitive, cognitive, and socio-affective learning strategies was given to 511 Chinese EFL learners, along with another survey about using AI for different parts of the writing process (brainstorming, research, outlining, writing, and revision). Results were then correlated using the Spearman rho formula. Findings revealed that metacognitive strategies were linked to a reduction of AI for preliminary planning of writing (brainstorming and research). Specific metacognitive strategies related to goal setting were also associated with less use of AI for outlining. In contrast, cognitive language learning strategies were more closely associated with AI use throughout the writing process, which included finding research, writing an outline, and writing essays or homework assignments. Socio-affective learning strategies appeared to reduce the amount of AI used to revise writing content. Overall, results appear to suggest that targeted metacognitive strategies can help to promote individual autonomy and reduce overreliance on AI. Limiting AI to revision may also promote metacognitive strategy development during other stages of the writing process.
Visual aids are commonly used in language learning; however, empirical evidence on their benefits for vocabulary learning is mixed. As the effectiveness of illustrations could depend on their characteristics, the impact of different illustration features (e.g., clarity and complexity—simple vs. detailed images) as well as other potential moderating factors on intentional vocabulary learning was assessed in this study. Two experiments were conducted for this purpose, respectively adopting vocabulary lists and flashcards. In both experiments, Japanese EFL university students learned unfamiliar words in one of three conditions—no-illustration, simple illustrations, or complex illustrations—and their learning was assessed via pretests, immediate posttests, and delayed posttests. The results showed that while no significant effects of illustrations were found for list learning, with flashcards, the effects of illustrations were moderated by word concreteness. Specifically, both simple and complex illustrations facilitated the learning of concrete words, whereas simple illustrations hindered the learning of abstract words.
This study examines how pre-service English teachers (PSETs) integrate the Sustainable Development Goals (SDGs) into language teaching. Grounded in Education for Sustainable Development (ESD) and Content and Language Integrated Learning (CLIL), it investigates which SDGs are prioritized, how learning outcomes reflect sustainability competencies, and how pedagogical tasks support sustainability-oriented thinking. The study was conducted in a compulsory “Teaching Language Skills” course at a Turkish public university. Following a workshop on ESD and the SDGs, 102 PSETs worked in groups to design 45-minute lesson plans for young adult learners. A qualitative document analysis of 27 lesson plans examined thematic focus, competency alignment, and pedagogical design. Environmental themes were most common, particularly SDG 13 and SDG 12, while SDGs 2, 4, and 15 were rarely addressed. Learning outcomes most frequently emphasized critical thinking and normative competencies, whereas anticipatory competency appeared only minimally. Although relatively few in-class outcomes explicitly reflected ESD pedagogies, homework tasks often extended lessons through action-oriented learning. Overall, the findings suggest that PSETs can incorporate sustainability themes into language teaching, but teacher education programs may need to place greater emphasis on future-oriented thinking within ESD.
Despite the inclusion of the Genre-based Approach (GBA) in Indonesian educational curricula, GBA studies remain limited, particularly when a Generative Artificial Intelligence (GenAI) tool is integrated as a feedback agent. This study investigated the implementation of GBA with GenAI feedback in teaching exposition writing to undergraduate EFL students in Indonesia through an embedded single-case study. Fifteen students’ diagnostic tests, three drafts with GenAI feedback, and a final test were scored. Writing scores were analyzed using repeated-measures procedures across overall quality, schematic structure, and linguistic features based on Systemic Functional Linguistics (SFL) metafunctions. Students’ experiences with GenAI feedback were analyzed through thematic analysis of students’ notes and interviews. The results indicate that students’ writing development appears to be associated with explicit instruction and teacher scaffolding alone, as development was observed only from the diagnostic test to the first draft. Despite students’ appreciation of GenAI feedback for its immediacy and comprehensive coverage, no development was observed during revision and editing with the feedback. Students also reported challenges related to prompting, feedback language, and feedback volume. These findings suggest that integrating GenAI feedback into GBA requires critical consideration. Pedagogical implications and study limitations are briefly discussed.
Generative artificial intelligence (GenAI) is often portrayed as the next emerging technology that is about to revolutionize education. There is even a current book titled, “Brave new words: How AI will revolutionize education (and why that’s a good thing)” (Khan, 2024), authored by the founder of Khan Academy, an educational platform that has a global user base of more than 137 million registered users across 190 countries. Similar claims of educational innovations have historically emerged with each new technology, whether radio, television, or social media platforms, or, at present, with GenAI (Pegrum, 2025). When a revolutionary new educational tool is introduced, the older system becomes reframed as “traditional” (despite having once been a revolutionary innovation itself), and the pros vs. cons of the new system are weighed against it. Since GenAI’s emergence within academic spheres, peer-reviewed journal articles and books dedicated to exploring GenAI’s educational and societal impact have been widely discussed by the academic community. Unlike other book titles on GenAI (e.g., Bowen & Watson, 2024; Khan, 2024), the book under review titled Artificial intelligence, real teaching: A guide to AI in ELT, is a guide for using AI for foreign language instruction, particularly for those who teach English as an additional language. The book authors, Joshua Paiz, Rachel Toncelli, and Ilka Kostka claim that they co-authored this book to “build a robust foundation for English language teachers seeking to understand the nuances of AI, both in terms of risks and rewards, as well as strategies for meaning and ethical integration into professional practice” (p. 76). The entire book is filled with real-world scenarios drawn from their everyday teaching experiences. [First paragraph of review]
Recent geo-political upheavals have increased migration to English-speaking countries, creating significant challenges for K-12 educators. The percentage of English as an additional language (EAL) students in U.S. public schools, for example, increased from 9.4 percent in 2011 to 10.6 percent in 2021 (National Center of Education Statistics, 2024). Similar trends have been reported in Canada, with the number of EAL students in Canadian K-12 schools continuing to increase (e.g., Le Pinchon et al., 2024). Supporting these students effectively in their academic pursuits is essential to helping them develop the skills they need in order to participate fully in society. Because many scholars (e.g., Grapin & Lee, 2022; Santibañez & Gándara, 2018) suggest that secondary teachers often fall short of the requisite knowledge to support these students, Joanna Kolota’s book Empowering EAL Learners in Secondary Schools: A Practical Resource to Support the Language Development of Multilingual Learners is a timely and relevant resource to address the status quo. The focus of this book is pedagogical strategies to help secondary (EAL) learners develop English language skills. [First paragraph of review]
Recently, I have noticed that a number of articles in this volume of TESL-EJ have referenced a specific book when they probably did not have access to it, nor would most of the TESL-EJ readership.
