As AI-driven tools become increasingly integrated into educational contexts, GenAI like ChatGPT, is gaining prominence in diverse classroom tasks. However, its effectiveness in supporting writing remains underexplored. This study explores ChatGPT’s role in supporting EFL essay writing, focusing on how learners formulate prompts and utilise AI-generated feedback. Twenty-eight participants, aged 20 and above, from a one-year essay writing course were examined through multiple data sources: (1) learner-AI interactions during first-draft revisions, (2) revised drafts incorporating AI feedback, (3) final drafts evaluated using a rubric on linguistic accuracy, coherence, and overall appropriateness, and (4) learners’ qualitative reflections on AI use. By employing a mixed-methods approach that combines quantitative error analyses and qualitative thematic coding of learner-AI interactions and questionnaires, this study provides insights into the effectiveness of AI-driven writing support. Preliminary findings indicate that while AI-based feedback reduced mechanical errors, several learners struggled to integrate context-specific feedback (e.g., aligning arguments with local academic conventions), highlighting the need for deeper critical engagement. Overall, this study offers a deeper understanding of how GenAI shapes learning experiences and contributes to ongoing discussions on how technology is transforming writing pedagogy.

The emergence of AI, while establishing MT's role in translation classrooms, presents an existential challenge for translators and trainers concerning trainee engagement and subjectivity. In response, this paper investigates trainees' agency and their 'dance of agency' (i.e., resistance and accommodation) during an undergraduate AI-assisted translation course.In contrast to typical machine translation post-editing studies that prioritize productivity, this project adopts an empowerment-focused design. It observes and documents students' decision-making processes to reveal their agency when collaborating with AI. Participants undertake MT-assisted tasks involving three levels of cognitive complexity (MT error annotation, MT with post-editing, and controlled authoring with MT and post-editing) across three text types (informative, expressive, and operative). Preliminary findings indicate that when trainees are fully empowered to annotate MT errors and pre-edit source texts to enhance MT output, they demonstrate greater awareness of potential MT errors and limitations. Consequently, they exhibit increased willingness and confidence in editing MT suggestions. Drawing upon Rammert's (2008) model of agency levels, the results suggest that this approach shifts students' engagement with machine translation, gradually moving them from 'passive' or 'semi-active' roles in MT training towards 'pro-active' and 'co-operative' ones.

This paper examines the AI interactions of students through the lens of Critical Digital Literacy (Darvin &Hafner, 2022; Darvin, 2017; Pangrazio, 2016) to gain insight into the ways students negotiate notions of power structures, agency, ideologies and inequalities of resources. While previous research on AI and academic practices have addressed the tensions and challenges associated with AI mediated writing, little attention has gone out to how this collaboration with AI as part of students’ academic practices reveals how students conceptualize critical aspects of their AI use. Instead of taking either student or AI as the focus of investigation, this paper focuses on how students conceive of power, agency, ideology and inequalities as mediated through the interaction with AI. Analyses of reflective video diaries were combined with focus group interviews with 20 undergraduate students in China to gain insight into their subject positions toward AI. The findings reveal a complex negotiation of agency, trust, and authorship, as students critically assess AI outputs, and concede to or reject power structures and ideologies invoked by the output of AI. These student-informed inferences of critical conceptions of ‘AI literacies’ provide important insights for pedagogical design and curriculum development.

A persistent obstacle for Japanese EFL learners is the lack of real-world speaking opportunities, while instructors face challenges providing personalized feedback for spoken language tasks. This presentation introduces Tevo, a web-based AI chatbot app designed to facilitate role-based conversation and generate automatic feedback.

We report on a two-week pilot study conducted in a Japanese university speaking course, exploring how students engaged with Tevo, and what they perceived as its strengths and limitations. We’ll share qualitative and quantitative insights, including how the app impacted confidence, fluency, and classroom participation. The talk concludes with reflections on how generative AI can support—not replace—teachers by freeing up time for lesson design and targeted support.

This session explores the technology and pedagogy behind MiMi, an AI-powered chatbot introduced in over 50 universities. We examine the accuracy and effectiveness of ChatGPT and Google Gemini models, the pedagogical principles driving their impact, and their role in boosting student motivation and language assessment. The presentation includes key insights from deployments with over 15,000 students in Japan this past year, analyzing motivation trends and alignment with CEFR-based ‘CAN-DO’ benchmarks.

This presentation describes findings from a study that integrated analog and digital tools in collaborative projects among university student groups in an intra-institutional setting. The research explores how combining instant photography and AI-supported communication analysis can enhance student engagement and collaboration across institutions and cultures. Relying solely on digital platforms can feel impersonal or isolating for students. To foster deeper connections, participants used Kodak Mini Shot 3 instant film cameras to capture scenes from their university life both on and off campus. These physical photographs served as prompts for creating captions, audio recordings, and posters, adding a tangible, human element to group exchanges beyond the classroom. The digital component used ChatGPT to analyze a large digital archive of student-created content. This analysis revealed participation patterns, communication trends, common linguistic errors, and student preferences. These insights helped instructors better understand student communication styles and offered both targeted feedback as well as relevant teaching content. Findings suggest that physical media can strengthen interpersonal connection in digital learning environments, while AI tools provide scalable methods for supporting and evaluating collaboration. This multimodal approach offers practical insights and methodology for educators designing intercultural learning experiences that are both data-informed and personally meaningful.

This presentation explores the development and impact of an academic podcast focused on teaching and technology. As digital media increasingly shapes education, podcasts offer an accessible and engaging platform for disseminating research and best practices. This project highlights key successes by the authors in conceptualizing, producing, and distributing the JALTCALL Podcast that bridges the gaps between researchers and practitioners. The presentation will discuss strategies that were used for selecting relevant topics, engaging expert guests, and ensuring scholarly rigor while maintaining audience accessibility. Additionally, it will address technical considerations (distribution channels), audience engagement metrics (data analytics), and comments from listeners (feedback). Results suggest that the podcast can enhance knowledge dissemination (15 countries in the first 10 weeks), encourage professional dialogue (social media engagement), and support language educators (by creating a platform to present) in integrating CALL methodologies effectively. By sharing best practices and lessons learned, this session aims to provide insights for academics interested in leveraging podcasting as a tool for scholarly communication and professional development.

The integration of digital platforms in language teaching has significantly transformed how learners engage and participate. This study explores the use of ClassDojo as a computer-assisted language learning (CALL) tool to enhance student motivation and English language use among Chinese EFL learners in a Sino-Foreign educational setting. Through qualitative interviews with 10 participants, the study examines learners’ experiences with ClassDojo, focusing on its impact on participation, engagement, effectiveness, and the role of the Sino-Foreign educational context on student interaction with CALL tools. The findings reveal that gamification elements, such as points and badges, promoted active participation, while real-time feedback increased classroom interaction, and peer influence cultivated a collaborative learning environment. Learners reported that digital tracking significantly enhanced their motivation, ClassDojo’s interactive features made learning engaging, and the platform’s structured support helped build language confidence. Furthermore, the Sino-Foreign educational context notably influenced CALL experiences, as learners navigated adaptation challenges and digital literacy issues, responded to cultural perceptions of technology in education, and depended on institutional support for CALL integration to benefit from digital learning tools. These findings provide valuable insights for educators seeking to optimize CALL platforms like ClassDojo to create more interactive, engaging, and culturally responsive EFL learning environments.

Traditional English language instruction often follows a one-size-fits-all approach, limiting opportunities for learners to engage with content that matches their individual needs, interests, and proficiency levels. This interactive workshop explores how ChatGPT can support customized learning by helping educators design adaptive lessons, tailored practice activities, and responsive feedback mechanisms that cater to diverse learners.

Participants will discover how ChatGPT can be used to create flexible study plans, scaffold content for different proficiency levels, and provide targeted language support. Through hands-on demonstrations and collaborative activities, attendees will explore strategies for integrating AI into differentiated instruction, autonomous study, and skill-based language development. The session will also address best practices, limitations, and ethical considerations in AI-assisted learning.

By the end of this workshop, participants will leave with practical tools and strategies for using ChatGPT to enhance student engagement, scaffold learning, and foster greater learner autonomy. This session is ideal for educators seeking to move beyond standardized instruction and create more personalized, student-centered learning experiences.

This presentation examines how audio editing in L2 podcast production sparks language acquisition beyond traditional speaking practice. Drawing on open-ended reflections from 103 university students in an EMI media studies course, we demonstrate how technical editing decisions become opportunities for language hypothesis testing.

Students answered four reflection prompts after producing podcast episodes. Using inductive thematic analysis (Braun & Clarke, 2006), three teacher-researchers, working in pairs, independently coded responses by question. Based on preliminary codes, we collaboratively developed a codebook with defined categories (Nowell et al., 2017) and maintained reflexive audit trails. Average interrater agreement was 74.6%, with discrepancies resolved through discussion.

Emerging themes showed relationships between editing difficulties, collaboration, and perceived utility for English learning. As students determined whether recordings “sounded right,” they engaged in critical listening and gained awareness of how to improve their English. Technical challenges increased motivation and investment through authentic purpose, editorial autonomy, and the desire to produce polished work.

Findings echo Kuhn (2015) on collaboration and Azizi et al. (2022) on podcasting’s value for linguistic output. Podcast editing fosters metacognitive awareness and supports language refinement, offering a meaningful context for L2 development. Implications for integrating technical skills into language learning curricula will be discussed.

Japanese learners of English as a foreign language (EFL) often struggle with the pronunciation of specific American English (AE) vowels, particularly the mid and low vowels /æ/, /ɑ/, /ʌ/, /ɔ/, and /ɝ/, which tend to be perceptually assimilated into native vowel categories. This study explores the impact of a five-week, technology-enhanced vowel space training program that integrates dual-mode input—auditory and visual—to facilitate more accurate second language (L2) vowel acquisition. The participants were first-year Japanese undergraduates at a private university in Tokyo. The experimental group (N=30) received targeted training via an interactive, vowel-space mapping tool that employs dynamic color coding to represent vowel quality, providing multimodal reinforcement. In contrast, the control group (N=30) underwent conventional monomodal auditory training. To evaluate learning outcomes, both groups completed pre- and post-training production and identification tasks. The study’s findings are analyzed in the context of CALL-based phonetic training, addressing the pedagogical potential of dual-modality input in reshaping L2 vowel identification and production. Implications for technology-mediated pronunciation training and its role in optimizing L2 phonemic awareness and phonological development in EFL learners are discussed.

Language educators increasingly focus on developing communicative competence in language learners, but assessing speaking skills in large student groups remains a challenge. This study explores how AI and automatic speech recognition can enhance speaking skills assessment through chatbots built on Azure Speech Services and Microsoft OpenAI Services.

The AI chatbot engages learners in conversations on various topics, evaluating pronunciation accuracy, fluency, and completeness using advanced algorithms. It also provides immediate feedback and scores on both pronunciation and content, helping students identify their strengths and areas for improvement.

We tested this service with a group of 20 EFL students from a university in Taiwan and gathered their feedback through post-session surveys and interviews. We collected data after a 30-minute chatbot session, focusing on learners' interaction experiences with the chatbot across a range of tasks.

Students responded positively to the AI chatbot, particularly noting its accuracy in assessing pronunciation. However, they observed that its evaluation of content was less precise. While the system excels in pronunciation assessment, its ability to evaluate learner-produced content requires further refinement.

This innovative approach highlights the potential of AI-powered tools in language education, offering a promising solution for efficient and effective speaking skills assessment in ESL/EFL contexts.

While AI translation technologies offer efficiency and support in managing translation tasks, they also present challenges in preserving linguistic elements, cultural fidelity, and literary style. This study explores how peer review helps students analyze and post-edit AI translations by identifying issues, enhancing language sensitivity, and refining editing skills. Fourth-year university students in Taiwan will work with AI-generated translations, engaging in post-editing, peer review, and guided reflection. Through structured peer review, they will identify linguistic errors, cultural misinterpretations, and stylistic issues. Using Google Docs comments and Peer Review Parameters, they will assess AI quality, provide feedback, and refine translations. A final reflection will help them evaluate their editing strategies, AI effectiveness, and peer feedback experience. This study investigates whether peer collaboration enhances students’ ability to make informed judgments about AI translations, identify and correct AI errors, and develop critical evaluation skills. Using quantitative assessment and proofreading data, it assesses the pedagogical benefits of integrating AI-assisted translation with post-editing and peer review. Findings will offer insights into how peer review can help students critically assess AI translations, refine their editing skills, and balance AI efficiency with human judgment—particularly in literary translation, where cultural references and linguistic precision are essential.