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]
