Abstract Generative artificial intelligence (AI) is transforming the teaching and learning of second language (L2) writing. Despite AI's affordances in assisting L2 writing, improvements in writing skills and learning outcomes that extend beyond producing polished texts largely depend on students' active AI engagement, typically involving AI‐generated content and AI tools. This study adopts a qualitative, classroom‐based inquiry to examine how 56 L2 postgraduate students engage with AI in an authentic academic writing class. Framing engagement as a multidimensional meta‐construct, we identify a contextualized pattern of engagement characterized by students' agential role in shaping human–AI interactions across behavioral, cognitive, emotional, and social dimensions. Specifically, behavioral engagement occurs at compositional and operational levels, whereas cognitive engagement involves cognitive autonomy and metacognitive awareness. Students' emotional engagement involves appreciation and relief, as well as caution and emotional distance. Social engagement intersects with other dimensions, potentially shaping how students act, think, and feel in relation to AI through collaborative reflection. The findings inform a reconceptualization of engagement as a fluid, situated process, moving beyond a simple engagement–disengagement dichotomy toward a dynamic continuum shaped by the ongoing interplay across its multiple dimensions, encompassing both synergies and tensions. Implications for L2 writing and learning are also discussed.
With the rapid growth of research in English-medium instruction (EMI) in higher education, much research has been conducted through qualitative and quantitative approaches. Yet, little attention was paid to the use of mixed-methods research (MMR) in this field. Therefore, this systematic review aims to examine previous literature that adopted MMR in EMI research, specifically evaluating the methodology across three aspects: epistemological orientation and purpose, MMR question formulation, and MMR design. Among 136 studies identified, substantial methodological gaps were found across all three aspects. First, only seven studies explicitly stated an epistemological orientation whereas 27 studies provided explicit purpose terminology. Second, only six studies explicitly presented an integrative MMR question, while 112 studies provided qualitative and/or quantitative research questions, and 18 studies lacked explicit research questions. Finally, MMR design characteristics were rarely described: (1) nearly half of the studies did not specify a design, (2) no study used specific sampling procedure terminology, and (3) questionnaires and interviews were the most frequently used instruments. Based on these methodological gaps, this study offers recommendations for strengthening future EMI studies that employ a mixed-methods approach.
The rapid advancement of artificial intelligence (AI) has brought both opportunities and challenges to the field of education. Within this evolving landscape, the emergence of the AI divide has become a pressing concern, particularly in teacher education and EFL instruction. This study developed a three-level three-factor conceptual framework to explore the AI divide through the dimensions of AI access, AI capability, and AI outcomes, while also considering technological, personal, and institutional influences. Using this framework, the study employed a comparative research design to examine how novice and veteran EFL teachers integrated AI differently in their teaching practices. The findings revealed that the AI divide between novice and veteran EFL teachers mostly occurred at the second level (AI capability) and third level (AI outcomes), rather than the first level (AI access). These disparities were shaped by a combination of technological, personal, and institutional factors, including perceived ease of use of AI, educational background, social support, and access to AI-related professional development. This study extends the concept of AI divide into the field of EFL education and offers valuable theoretical insights and practical implications for more equitable AI integration in EFL teaching.
Amid growing interest in multilingual pedagogies, translanguaging has increasingly been positioned as a response to monolingual teaching norms. Yet empirical accounts of what pedagogical translanguaging looks like in everyday classrooms-particularly in teaching English to young learners-remain limited. At the same time, although storytelling occupies a central place in young learner pedagogy, little research has examined how teachers mobilize linguistic and semiotic resources during storytelling lessons. Addressing both gaps, this study draws on the pedagogical translanguaging framework and the Dynamic Model of Multilingualism to investigate the evolving practices of eight preservice English teachers (PSTs) in Turkish state primary schools. The data set comprises 32 video-recorded storytelling lessons over 8 weeks and follow-up in-depth interviews. Findings show that PSTs initially drew on shared linguistic repertoires to manage classroom interaction and scaffold story comprehension. Over time, however, these practices shifted, and translanguaging use decreased as PSTs increasingly employed multimodal strategies, including gesture, visual supports, and realia. Findings suggest how teachers' mediational practices became increasingly adaptive, with verbal translanguaging becoming more selective alongside increased use of multimodal mediation. The study contributes classroom-based evidence of how translanguaging evolves over time and offers implications for multilingual pedagogy and teacher education in young learner contexts.
