This classroom-based study examines whether the use of generative AI for self-editing, combined with explicit instruction, influences CEFR A1–B1 Japanese EFL learners’ writing development and affective factors related to English learning. Participants were 50 first-year students enrolled in two compulsory English courses at a national university in Japan during the second semester of the 2024 academic year. One class was assigned to an experimental group, in which students were required to use ChatGPT for self-editing and received explicit instruction on its effective use, while the control group was allowed optional AI use without explicit guidance. Writing development was measured using pre- and post-test essays assessed by word count and a six-category analytic rubric, and affective factors were examined through questionnaires. A significant increase in word count was observed over time in both groups; however, no significant differences were found between the groups in word count/rubric scores. Increased motivation and self-efficacy were reported in both groups. These findings suggest that over a single semester, writing development among lower-proficiency learners may be influenced more by increased opportunities for learner output than by individualized AI-generated feedback alone. The study discusses implications for CALL-oriented instructional design that leverages AI to promote productive output.

Many educators utilize AI-tools to generate classroom materials quickly and efficiently. AI-created readings, worksheets, and listening tasks can provide level-appropriate input rich in target grammar and vocabulary, particularly in ESL contexts where teachers often adapt informational texts for learners. However, the increasing use of such materials raises an important question: how trustworthy is AI-generated educational content - and has the quality changed with recent updates? Research shows exposure to inaccurate information can lead learners to internalize false knowledge through processes such as source confusion and the illusory truth effect. If AI-generated materials contain subtle errors, students may unintentionally learn misinformation - particularly problematic for medical and science students. This study revisits and extends earlier work on the value of AI-generated classroom materials. Subject-matter experts, including doctors and professors, first identified topics within their disciplines, after which reading texts were generated by multiple AI models and evaluated. The consultants assessed the materials for factual accuracy, pedagogical suitability, and potential risk for use as simplified informational texts for language learners. This follow-up study offers insights into when AI-generated materials support learning - and when they undermine it. The findings aim to help educators use AI tools more critically when developing instructional materials.

Recent research has explored the use of generative AI for writing assessment, yet evidence regarding its reliability and validity remains mixed. This study examines whether ChatGPT (GPT-4o) can function as an analytic assessment tool for source-based academic essays written by postgraduate research students. A dataset of 122 essays, originally scored by two experienced human raters, was reevaluated by ChatGPT using a standardized analytic rubric (e.g., Idea Presentation, Academic Style, Citation, and Mechanics) and a zero-shot prompting approach. Non-parametric analyses and descriptive statistics were used to examine score alignment, ranking patterns, and domain-specific differences.

Results show that while human and AI scores occupied a similar overall range, ChatGPT consistently awarded higher scores and did not rank essays in ways that aligned with human judgment. Significant differences emerged across most rubric domains: human raters scored higher on idea presentation, whereas ChatGPT assigned higher scores for academic style and citation practices; no significant difference was found for mechanics. Repeated AI scoring demonstrated high internal consistency, with variability concentrated in meaning-dependent domains such as argument clarity and source integration. Overall, the findings indicate that generative AI shows promise for reliable form-focused assessment but remains limited in evaluating rhetorical and conceptual quality.

This study examines ChatGPT as a supportive tool for an ESP lecturer teaching first-year business students (B2-C1 proficiency, majors in Banking and Finance, Accounting, Business Administration, Logistics and Supply Chain Management) at Foreign Trade University, Vietnam. The tech-supported ESP course was delivered primarily via Microsoft Teams. The mixed-methods case study explores opportunities and challenges of integrating ChatGPT in teaching methods and assessment design (e.g., diverse question generation, formative feedback), alongside its potential influence on student outcomes in CALL contexts. Data were collected from 81 students, including Microsoft Teams interaction logs and materials, lecturer observations and reflective notes, student reflections, an open online quiz (n=50), and a final exam (n=50). Descriptive statistics showed quiz mean scores of 18.86/30 improving to 37.46/50 on the exam (average 37.2% gain). Qualitative findings highlighted ChatGPT's role in bridging content gaps, fostering critical thinking, and enabling personalized support, but also concerns regarding overreliance and ethical issues such as balanced use and academic integrity. While preliminary results suggest thoughtful integration of ChatGPT can enhance ESP instruction, the absence of control groups limits causal claims about learner progress. Future iterations will incorporate comparative cohorts. This case offers practical insights for practitioners navigating AI's opportunities and limitations in classrooms.

This presentation reflects on a semester-long implementation framework for embedding three custom-built AI tools into SPA001, the entry-level Spanish module for absolute beginners at Xi’an Jiaotong-Liverpool University. TAR-íA (a practice generator), AI-migo (a virtual tutor responding to queries and providing writing feedback), and XiSPA (an AI-driven conversational partner) were introduced from Week 3 and updated throughout the semester to align with curricular progression. Building on an earlier iteration where the tools were introduced only at semester’s end, this implementation embedded them systematically throughout the course, positioning AI as an integral component of classroom instruction rather than as isolated classroom activities or supplements for autonomous practice. Following a brief tool overview, the presentation details the rationale and sequencing for introducing each tool, the main activity types conducted, and how tool functionality was aligned with curriculum and beginner-level constraints. It offers critical practitioner reflection on challenges encountered—including time constraints and managing occasional inaccuracies—alongside observations from classroom practice and areas for refinement. This presentation documents and reflects on practice-based implementation decisions to inform future iterations and offer educators transferable insights into embedding custom AI tools as integrated components of beginner language curricula.

Technology-enhanced education enables more interactive, personalised language learning. This project examined how GenAI (e.g., ChatGPT-4o) supported university students without a tourism background in an ESP Tourism English course. Although academically strong, they were novice to tourism discourse and workplace routines, creating a mismatch between course expectations (service-oriented interactional competence) and prior textbook-driven learning with limited oral rehearsal. This gap lowered perceived relevance, increased speaking anxiety, and led to uneven role-play participation. Therefore, the intervention targeted motivation, learner autonomy, and listening–speaking performance for tourism-service encounters. Over 18 weeks, students completed AI-supported brainstorming, itinerary planning, collaborative tasks, and scenario-based conversational practice in workplace simulations, producing recordings and task outputs. The design provided scalable, repeatable interaction practice with immediate language support beyond what teacher feedback alone can offer in limited class time.

Qualitative triangulation of student artefacts, recordings, AI dialogue logs, and reflections indicated improvements in four areas. Students demonstrated stronger oral expression and situational responsiveness, greater appropriacy in service interactions, higher motivation due to reduced speaking anxiety, and improved AI literacy for revising language and organising information. Student feedback also noted vocabulary gaps and the need for clearer AI-use guidance and a better balance between AI practice and human interaction.

How can computing devices develop communication? College students of English need to understand “communication” as spoken exchange on equal terms. Integrating devices can promote language-learning. Learning environments need diversifying for the particular set of students, course goals and personal expectations (Stockwell 2019). Course components include computer access, smart phones, editing programs, and meaningful locations. Mobile-phone video-shooting begins the process. A set of basic technological skills is a pre-requisite. Devising scenarios to generate communication requires co-operation and spontaneous interaction. Whatever locations are available to an instructor are potential learning environments. In this case, not only places on campus, but also locations within easy reach became dynamic motivators. To activate students' passive abilities they need motivating to communicate about each other’s immediate knowledge and experience in English. After in-class discussion and the preparation needed to achieve a productive atmosphere, various samples of the videos produced at locations in and around their campus will be shown to discuss the necessary steps and contents for success. Students become more proficient with proper editing skills, as well as captioning with AI or self-captioning to preserve valuable English interactions and enrich their learning experience. The choice is customizable to individual circumstances, but the template is universal.

Literacy has rapidly expanded and radically transformed in the time of digital society. Language literacy should extend beyond reading to embrace human-AI collaboration as a design process of meaning-making across diverse forms. This presentation shares the research-based practice to implement human-AI collaboration in English literacy. Sixty-nine undergraduate students from one Teacher Education program participated in the mixed-methods study to develop a multimodal project in their teaching disciplines. The project adopted Thai Basic Core Curriculum Strands and Standards to design locally relevant teaching materials for the lesson design, teaching practice, and virtual poster exhibition. Given the collaboration among AI-generated responses, peers, and teacher-guided feedbacks, they created storyboards, verified AI suggestions, and edited changes. Results from the reading assessment and digital literacy inventory led to envision AI-mediated literacy for enhancing language learning and preparing real teaching. Students significantly improved both reading and digital literacy. They reflected the adaptation of AI technologies on the inventory and student narratives from user logs and feedbacks over four dimensions, namely agency and interactive dialogue, critical evaluation and accountability, multimodal and creative synthesis, and self-awareness and framing. Implications addressed challenges and concerns for leveraging human-AI classroom practices through literacy experiences with discipline connectivity in language teacher education.

This presentation compares student writing produced before the availability of generative AI tools with writing produced in the current AI-integrated classroom, drawing on samples from the same intermediate-level English writing course at a Japanese university. The study examines two comparable cohorts: approximately 30 students in 2022 (pre-AI) and in 2025 (post-AI). Both groups used the same textbook, writing tasks, and instructional approach. In 2025, “AI support” referred primarily to guided use of ChatGPT for language refinement with clear classroom policies on responsible use. A comparison of selected writing samples shows a reduction in grammatical error frequency per 100 words and improved readability scores in the AI-supported cohort. Before AI integration, instructors devoted substantial time to correcting surface-level errors, limiting attention to organization, argumentation, and writer voice. With AI-assisted drafts, grammatical accuracy improved sufficiently to allow more sustained focus on higher-order concerns, although challenges in coherence and rhetorical effectiveness remain. By presenting anonymized samples from both periods, this study demonstrates how AI reshapes instructional priorities. Rather than replacing writing instruction, AI mediates it, enabling a shift from error correction toward content development and rhetorical awareness. Attendees will gain practical strategies for designing AI-integrated writing classes that prioritize strengthening ideas and structure over sentence-level correction.

Virtual worlds are often used in second language (L2) teaching to visualize motion events and support learning of verbs such as come and go in Japanese. However, most designs rely heavily on visual input, limiting accessibility for visually impaired learners. In light of Title II of the Americans with Disabilities Act (ADA), which mandates that public institutions ensure equal access to digital instructional materials and services, accessible lesson design has become both a legal and pedagogical priority. This presentation introduces a prototype lesson design that enhances visual representations of motion with systematic auditory cues. In this model, changes in sound—such as increasing volume, directionality, and foreground/background contrast—work alongside visual input to represent movement toward or away from a speaker. The session demonstrates how these cues are carefully mapped onto motion meanings and embedded into a screen-reader-compatible virtual lesson segment. Rather than reporting a completed experimental study, the focus is on instructional design principles and practical implementation. Designed for language teachers, CALL practitioners, and TESOL graduate students, this session offers concrete strategies for creating more accessible technology-enhanced lessons and provides an adaptable framework for incorporating multimodal input into virtual language learning environments.

Technology is advancing rapidly and is increasingly encouraged for use in language teaching and learning. This study aims to design a Corpus- and AI-aided self-directed English speaking training programme, examine its effectiveness, and explore learners’ attitudes toward this approach. Thirty-one undergraduate EFL learners participated. They completed a pre-test, a five-session Corpus- and AI-supported speaking training, a post-test, a learning portfolio, a survey, and a follow-up interview. Findings showed that participants’ overall speaking performance improved after the training, along with gains in four subskills: lexical resource (LR), grammatical range and accuracy (GA), fluency and coherence (FC), and pronunciation (PN). Among these, the greatest progress occurred in LR, while PN showed the least improvement. Learners agreed that the inclusion of a spoken corpus enabled them to identify linguistic features influencing oral proficiency, and that AI tools were effective in supporting speaking practice. The AI-enhanced environment also promoted interactive, self-directed learning. Most participants expressed positive attitudes toward the combined Corpus and AI-aided approach and demonstrated willingness to continue interacting with AI tools despite some limitations. However, a few learners reported their insufficient AI literacy and concerns about AI feedback accuracy. Overall, the study provides theoretical and pedagogical insights for enhancing English speaking instruction.

This poster explores joyful teaching as a CALL-informed approach to EFL instruction, addressing declining learner motivation caused by grammar-heavy, teacher-centered, and exam-driven practices. While such methods improve short-term test scores, they often lead to fatigue, anxiety, and negative attitudes toward English. For example, a class spends an hour teaching only grammar, students get bored, and they also be forced to focus on grammar study for the Common Test for University Admissions.Drawing on experiences in English literacy courses, the poster presents joyful teaching practices, including video-based tasks, digital and analog games, skits, and role-playing. These activities foster natural grammar and vocabulary acquisition through interaction, collaboration, and emotional engagement, without lowering academic rigor. Observations show increased student participation, engagement, and confidence.The poster also discusses “not joyful” examples, like unclear digital game instructions causing confusion or role-plays that stress shy learners or overemphasize competition. They show even well-intentioned activities can fail if they do not match student readiness or classroom dynamics. Joyful teaching requires careful planning, attention to learner needs, varied materials, and emotional scaffolding, supported by institutional measures like reducing teacher workload. Attendees gain practical ideas for implementing engaging, CALL-informed activities that balance enjoyment with　meaningful learning in EFL classrooms.

This study examined the effectiveness of a business English presentation course enrolling 24 participants, including 18 undergraduate students and 6 full-time employees from a retailing company. A 7-week workshop alternated weekly between (1) conventionally prepared presentations created through manual drafting and visual design, and (2) GenAI-enhanced presentations developed with tools such as Google NotebookLM and Napkin AI. The study explored whether learners’ performance varied across these formats and how their perceptions of presentation learning shifted over time. Data sources included performance assessments conducted by an industry HR manager and the instructor using a shared rubric, pre- and post-course questionnaires on confidence and perceived learning, and post-course interviews on the strengths and challenges of each preparation mode. The findings indicate limited differences in overall performance, with English proficiency serving as the strongest predictor. GenAI-enhanced presentations offered notable advantages in efficiency and visual quality, while conventionally prepared work better supported idea development, negotiation, and collaboration. Rather than competing approaches, the two formats functioned as complementary pedagogical pathways that cultivate presentation literacy, workplace problem-solving, and authentic communication skills.

This study examined how three instructional methods influenced the development of independent learning skills and business presentation performance among 18 college EFL learners with mixed English proficiency levels. Over a nine-week Business Presentation in English workshop, students engaged in one of three practice conditions: (1) an AI-mediated practice model using generative AI tools, including HeyGen, to create avatar-based presentation simulations; (2) a self-recorded video practice model in which learners uploaded and reviewed their own rehearsal videos; and (3) no additional training beyond standard instruction. Three research questions guided the inquiry: (1) Which instructional method produced the strongest presentation performance? (2) Which practice mode proved most suitable for learners at different proficiency levels? (3) How did students perceive the benefits and challenges of each method? Instructor and HR-specialist evaluations indicated that the AI-mediated practice model yielded the strongest overall presentation performance, particularly in organization and delivery. The findings further showed that generative AI tools, which created avatar-based presentation simulations, functioned as highly valued models that students followed to refine their own performance. However, individual differences—including English fluency, level of commitment, and degree of prior preparation—also substantially shaped learning outcomes. Survey and interview data revealed generally positive perceptions of AI-supported rehearsal, especially among lower- and intermediate-proficiency learners.

Ed-Venturers in Action is a four-theme instructional model for a Grade 4 EFL class in Thailand (N≈40) integrating CLIL and PBL, with CALL as supportive practice. It was developed to address a common pedagogical need in primary EFL: grammar lessons can feel repetitive, class time is limited, and learners often fail to transfer target forms into meaningful writing and speaking. Over eight 50-minute periods, each theme follows a two-period cycle. Period 1 uses a short CLIL text (city, country, continent, global issues) to introduce one grammar target, followed by brief digital game micro-practice (Wordwall, Quiz.zep.us, Baamboozle) to sustain attention; similar games may be assigned for home review. Period 2 guides students with sentence frames to write a 70–90 word theme summary, draft a short presentation script, and rehearse speaking. The sequence culminates in a three-panel Environmental Trifold Poster (Lampang–Thailand–World) and a group presentation. Using a one-group pre–post design, data include a 30-item grammar test administered via Google Forms on iPads, writing and speaking rubric scores, a brief SEL/GCED survey, engagement checklists, and game logs (accuracy, attempts, time). The session shares classroom evidence and ready-to-use materials for teachers seeking a practical, low-prep model.

Under the theme “Prevail or Fail?”, this presentation reports on a classroom-based intervention examining how scaffolded AI-supported rehearsal tasks may influence learner confidence among ground staff students at a Japanese aviation college. The project involves one intact aviation communication class of approximately 35–40 students meeting once per week for two consecutive sessions over six weeks. Traditional communication instruction continues in the first session, while AI-supported rehearsal activities are implemented in the second. In customer-facing aviation contexts, learners often experience communicative anxiety despite possessing adequate procedural knowledge. This project explores three guiding questions: (1) under what instructional conditions AI-supported rehearsal appears to influence confidence in handling unpredictable passenger interactions; (2) how students experience AI when framed as a rehearsal partner rather than an evaluative authority; and (3) what unintended patterns emerge, including over-reliance or heightened perfectionism. Drawing on self-regulated learning (Zimmerman, 2002) and sociocultural scaffolding (Vygotsky, 1978), AI is positioned as a structured rehearsal scaffold. Tasks include simulated passenger service scenarios (e.g., delay explanations and complaint handling) followed by guided reflection. Data consist of anonymised learner reflections, classroom observation, and instructor field notes, enabling analysis of emerging themes related to confidence development and risk.

Generative artificial intelligence (GenAI) is rapidly transforming English language learning by offering personalized, interactive support for vocabulary development (Moorhouse et al., 2025). Yet, while linguistic outcomes are increasingly studied, less is known about how learners experience these tools (Zhang, 2025). This study investigates the ways university‑level ESL students interact with generative AI and how this interaction influences their motivation, confidence, and sense of autonomy.

87 undergraduate ESL learners, mainly L1 Japanese speakers with some Chinese and Korean participants, used ChatGPT-4o in a medical vocabulary course. Two groups completed identical tasks: one received English-only feedback, while the other utilized their first language (L1) for feedback and prompt adaptation (Moorhouse et al., 2025; Yang & Lin, 2025). Data from surveys and interviews captured perceptions, usability challenges, and learning outcomes.

Findings indicate that GenAI boosts motivation through immediate, contextualized vocabulary explanations (Xiao & Zhi, 2023). However, learners faced limitations like repetitive feedback (Yang et al., 2025) and unreliable voice recognition, which affected trust (Mompean, 2024).

The study highlights integrating GenAI within pedagogically scaffolded instruction (Park & Kim, 2025). When guided effectively, AI tools promote reflective learning, critical engagement, and greater learner autonomy (Park & Kim, 2025). These insights help TESOL practitioners understand how GenAI can be meaningfully incorporated into communicative vocabulary teaching.

Digital technologies are increasingly used in language classrooms to support learner reflection; however, less attention has been paid to how reflective meaning is constructed in technology-mediated environments compared to traditional pen-and-paper formats. This qualitative study examines 100 self-reflection posts written by 100 individual students across three CALL-supported EFL classes, each submitted on Padlet at the end of the course. Unlike conventional handwritten reflections, which are typically private and linear, Padlet-based reflections enabled peer interaction through comments, multimodal expression through emojis, and easy access across devices. These affordances introduced a more socially mediated and affectively expressive reflective space. All reflections were analysed using reflexive thematic analysis, with reflective segments serving as the unit of analysis. Drawing on reflection as meaning-making, the study identifies recurring patterns in how students articulated learning challenges, evaluated strategies, and developed awareness of their progress and learning needs. Rather than merely reporting task completion, students engaged in critical self-examination by acknowledging difficulties, reassessing assumptions, and identifying areas for improvement. The findings contribute to CALL research by demonstrating how interactive digital platforms can extend reflective practice beyond individual written reporting toward socially situated reflective meaning-making.

This practice-oriented presentation examines the use of AI-generated illustration prompts in a Japanese university EFL writing course to support the development of descriptive vocabulary and adjective use. While AI tools are often discussed in terms of concerns about student misuse or overreliance, this project demonstrates how guided AI use can encourage active language production and lexical precision. Over the course of a semester, students wrote original fairy tales as part of a creative writing project. After drafting their stories, students generated AI illustrations by composing detailed prompts describing characters, settings, and actions. Initially students failed to get the images they wanted, so to achieve their intended visual outcomes, students were required to revise and refine their language, paying close attention to adjective choice. In this way, AI functioned not as a shortcut for writing, but as a constraint that prompted deeper engagement with vocabulary and descriptive language. The presentation will describe scaffolding techniques used to support effective AI integration and examples of student writing and illustration prompts, will be shared to illustrate how learners’ use of descriptive language developed throughout the semester. The session will offer practical guidance for educators interested in incorporating AI-supported creative writing tasks into EFL classrooms.

Can interactive narrative stories effectively improve young learners' pronunciation of challenging English vowel patterns? This study investigates R-controlled vowels pronunciation development among 72 Grade 3 students at Laor-utis Demonstration School, Lampang through teacher-created interactive animation stories. The research employs a pre-experimental one-group pretest-posttest design during the second semester of 2025. Over eight weeks, students engage with four animated story episodes where they complete missions requiring reading aloud specific R-controlled vowel words. The teacher facilitates learning by encouraging participation, building confidence, and providing immediate corrective feedback within the story context. This approach addresses a critical literacy gap: R-controlled vowels represent complex phonetic patterns that confuse young learners. Unlike traditional drill-based practice, narrative-driven animations create meaningful contexts where students read aloud to advance stories, transforming pronunciation from isolated skill practice into purposeful communication. The teacher's facilitation role—motivating students, reducing speaking anxiety, and providing real-time correction—proves essential. The study measures pronunciation accuracy and classroom participation through pre-post testing and behavioral observation, demonstrating whether this teacher-mediated, story-based approach significantly improves pronunciation skills while fostering engagement and confidence in oral reading.

This classroom-based inquiry explores the use of generative AI to support, rather than replace, the writing process in a first-year university reading and writing class. While AI tools are often criticized for collapsing planning, drafting, and revision into a single output, this project examined the question: How does restricting AI to specific roles within the writing process influence student engagement with planning, drafting, and editing? Over a four-lesson newspaper project, students created three texts using role-restricted AI functions: generating and outlining ideas (planning), modeling genre (drafting), and improving language (editing). Students documented prompts, AI outputs, revisions, and reflections throughout the project. Teacher observations, reflections, and informal student feedback were considered to identify patterns in AI use and writing development. The outcomes suggested that role-restricted AI use supported idea development, genre awareness, and revision depth. However, challenges included over-reliance on AI-generated phrasing and limited critical evaluation of edits. These findings offer practical guidance for integrating AI into writing instruction while maintaining learner agency, transparency, and process-oriented pedagogy.

EiTake is an online English learning and teaching application designed for use in Elementary School and Junior High School classrooms. It is a library of browser games and tools that appeal to students through colorful layouts, pop culture references, and reward feedback loops while aligning with standard classroom learning materials. The software is free and available for anyone to use, and is meant to act as a supplementary resource for use alongside standard English curriculum textbooks.

This project theorizes that students engage and react more positively when early English learning materials are interactive and provide some immediate form of feedback. The introduction of individual tablets in public schools has created the opportunity to expand on the use of interactive digital teaching materials. EiTake is an example of how regular teachers can create community resources for English learners and other teachers, and hopes to further measure the affects of using digital tools and games on long term English learning. EiTake also aims to provide more freedom to teachers in designing and running their lessons by removing the need to create complex interactive materials themselves.

Graded language materials can be highly beneficial to EFL students, however, creating them can be time-consuming and challenging. This may explain why there is a relative lack of extensive (graded language) listening materials in many EFL teaching and learning contexts. This classroom-practice presentation reports on AI-generated podcasts as one part of an ongoing project to create language-graded materials using generative AI. The presenters will demonstrate how Large Language Models (LLM) can be used to create graded-language scripts for podcasts, and how these scripts can be used to generate podcast-style listening materials using generative text-to-speech (TTS) platforms. The materials are currently being used with Japanese university EFL students whose English proficiency is at the CEFR B1-B2 level. The presenters will share student feedback about the podcasts, as well as the process and prompts used to create the scripts and the podcasts. This will allow presentation participants to adapt the workflow to their own teaching and learning contexts.

While many educators are familiar with using ChatGPT or similar AI tools for basic tasks such as text revision or prompt-based content generation, fewer are aware of their broader pedagogical applications. These include extracting text from scanned documents, adjusting reading levels to align with learners’ proficiency, and generating glossaries of challenging vocabulary with explanations in learners’ first language.

This workshop demonstrates how teachers can use ChatGPT or comparable tools to extract and adapt informational texts for classroom use. Participants will see how texts can be simplified to match students’ language abilities and transformed into dialogues involving a narrator and one or more students. These dialogues can be used for group practice, role-plays, or classroom performances. If time permits, the workshop will also introduce NoteGPT, focusing on podcast-creation features not currently available in ChatGPT.

Presentation slides, accessible via QR code, will provide step-by-step guidance for later reference, along with links to additional AI-supported functions, including text extraction from two-column layouts.

Participants who bring laptops will be able to create and save a text potentially useful in their own classes.

Large language models (LLMs) have created new opportunities for writing support while simultaneously challenging the integrity of text-based educational assessment. Existing authorship verification methods, such as stylometric analyses and automated classifiers, provide probabilistic judgments that may allow plausible deniability for claimed authors. However, true authors are typically familiar with their text in ways that surrogate authors are not. Building on this insight, the present study introduces the Content Restoration Authorship Familiarity Test (CRAFT), which assesses authorship by asking claimed authors to recall and reconstruct elements of texts they identify as their own.

The CRAFT battery was piloted with 60 university students in Seoul. Participants wrote a 16-sentence handwritten text in class. An LLM then generated a second text based on that content. About 30 minutes later, participants completed two CRAFT tests, one for each text. In both texts, four sentences were inserted and five words replaced with synonyms, and participants attempted to identify or restore the original wording. Responses were scored on a 14-point rubric allowing partial credit for morphologically related forms.

Descriptive analyses showed non-overlapping performance distributions between human-authored and LLM-generated conditions, suggesting authorship familiarity can provide a reliable behavioral signal for distinguishing genuine authorship from AI-assisted text generation.

In today’s AI-driven era, English Language Learners (ELLs) must navigate vast amounts of digital content, including fake news and deepfakes. Consequently, English as an Additional Language (EAL) educators need to integrate activities that foster learners’ critical thinking and digital literacy while ensuring instruction is inclusive and accessible, particularly for neurodivergent students. Grounded in the Universal Design for Learning (UDL) framework, this presentation reports findings from a qualitative case study examining the pedagogical value of project-based digital storytelling in a Japanese university communicative EAL course. The study explores how viral marketing videos, AI-supported kamishibai picture-card storytelling, and storyboarding were used to scaffold learning and prepare participants (n=14) to create collaborative, socially conscious digital narratives. These multimodal activities enabled students to critically examine local and global sociocultural issues in an authentic language-learning context. Drawing on UDL principles of multiple means of engagement, representation, and action and expression, the presentation highlights the design of instructional materials and learning tasks. Data sources included a post-project questionnaire, focus group interview, and classroom observations. Findings suggest that UDL-informed digital storytelling can enhance ELLs’ critical thinking, creativity, and digital literacies, while also revealing challenges related to time constraints and group dynamics.

The escalating demand for business graduates to communicate effectively in globalized settings underscores the need to strengthen listening and speaking skills in English for Specific Purposes (ESP). Traditional courses at Foreign Trade University HCMC Campus often favor content delivery over oral competence, but AI-driven tools integrated with multimedia present innovative opportunities. This study examines AI speech recognition, captioned business videos, digital storytelling, and podcasts to boost oral proficiency among undergraduates in Banking and Finance, Accounting, Business Administration, and Logistics majors, within ESP courses like English for Business Communication, Commercial Correspondence and International Business Contracts. Using a mixed-methods quasi-experimental design, the semester-long intervention merged authentic inputs (e.g., negotiation role-plays) with AI outputs (e.g., automated feedback on presentations). Pre- and post-tests measured quantitative gains, while reflections and interviews provided qualitative insights. Successes included marked improvements in fluency, pronunciation, and comprehension, plus enhanced motivation and confidence in workplace simulations like client meetings. Challenges included limitations in AI's recognition of accented speech, excessive dependence on the tools by learners, and early reluctance to adopt digital activities. This research advances CALL by adapting AI for business ESP, promoting learner-centered authenticity, and offering lessons for Vietnamese higher education curricula.

This presentation outlines the various difficulties educators may face when designing assessments in the age of GenAI (i.e work can be created by GenAI) and how educators may react by placing restrictions (resorting to pen and paper exams) or penalties (punishing students who use GenAI) in response to these difficulties. This presentation will argue that these approaches may act as temporary band-aids but ultimately might not be addressing the problems at hand or be beneficial for student learning.

The presentation will share experiences from redesigning assessments for an English ‘writing for communication’ course at a Hong Kong university. Given that writing tasks are most susceptible to GenAI, an alternative approach was needed to try to measure student learning from the course. This meant that changes had to be made both to the content and assessments to still make writing feel “relevant” to students in a GenAI world.

The presentation will share what these changes were and what the actual assessments were. By sharing this experience, other educators may start to think about their own approaches to assessment and whether traditional forms are still applicable. The presentation will end with broader pedagogical and practical suggestions that educators may find helpful.

Many first-year university students preparing for TOEIC report they "know the words but cannot hear them," suggesting a gap between lexical knowledge and spoken-word recognition (Field, 2008). This presentation describes how technology can be used in the classroom to help learners connect pronunciation features to listening performance.

Each week, three classes of students (n = 90) follow a short cycle (10 minutes): (1) identify segments of TOEIC audio they find hard to hear, (2) label the likely phonological source (e.g., reduction, linking, flap /t/, weak forms), (3) practise shadowing with that feature, and (4) submit recordings of themselves reading sentences with these forms using the LMS. This approach builds upon previous uses of shadowing (Hamada, 2016) by leveraging ASR-based pronunciation tools integrated into LMSs, such as Microsoft Teams Reading Progress (Molenda & Grabarczyk, 2022).

Analytics tracked include submission frequency, feature tags selected, and pre/post listening checks. Preliminary findings suggest (a) listening breakdowns can often be attributed to specific sound changes, (b) micro-shadowing routines strengthen both confidence and anticipation of connected speech patterns, and (c) the cycle is feasible without reducing core TOEIC practice time. The session will also discuss how teachers can integrate these cycles into their own classes.

Language learning doesn’t have to be boring. After all, language educators believe that learning should be both educational and entertaining for it to prevail. In this fun, creative, and easy-to-understand workshop, participants will learn how accessible, and user-friendly Canva for Education is. Canva’s flexible templates make it easy for teachers to build engaging vocabulary, grammar, reading, and speaking activities, while giving students the freedom to design their own digital creations that spark autonomy and deeper language learning. Educators will explore real classroom examples, experiment with creating their own materials, and pick up time-saving tips that support diverse learners. This workshop is designed as a highly interactive experience. Participants will engage with Canva templates and classroom-ready language activities, discovering how easily they can be adapted to different learning contexts and student needs. Through hands-on creation and shared reflection, participants will exchange ideas, build confidence in digital literacy, and leave with practical inspiration, supported by a take-home PDF guide and resources. Participants are encouraged to bring laptops or iPads. By the end, everyone will feel excited to bring Canva into their language classrooms to boost engagement, strengthen digital literacy, and empower students to shine as creative and confident language users.

Canva has become a widely used digital tool in educational contexts due to its accessibility and attractive ready-made templates. However, many teachers and students use Canva only at a surface level, relying heavily on standard designs without fully exploring its creative and pedagogical potential. This presentation introduces practical ways Canva can be used within an institution and EFL classrooms, focusing on how both teachers and students can extend their skills beyond basic template use. Examples include the creation of teaching materials, promotional resources, and social media graphics at the departmental level, as well as student-generated posters, presentations, and short videos in classroom settings. By providing targeted guidance and encouraging experimentation, Canva can become a powerful tool for fostering originality, engagement, and learner autonomy. This presentation emphasizes how small shifts in training and task design can help users move from passive template selection to active content creation. Attendees will gain ideas for helping both teachers and students to efficiently produce visually engaging materials that reflect their own creativity while supporting language learning objectives.

This poster presentation explores how Padlet was integrated into several Freshman and Sophomore English courses at a Japanese university to enhance student communication and collaboration. Padlet is a web-based collaborative platform that enables the creation, sharing, and organising of user-based content—including text, images, videos, documents, and links—in real-time or asynchronously. Students participated in activities, such as posting weekly responses to course topics, giving peer feedback, writing reflections, reviewing vocabulary, and asking questions for clarification. These tasks encouraged students to interact, share ideas, and build communication skills through both real-time and asynchronous exchanges. Using the paid version of Padlet, the presentation will discuss an overview of how Padlet was implemented, showing specific class applications and how it contributed to student participation, assessment, and project work while supporting class objectives and learning goals both in and outside the classroom. Furthermore, any challenges and limitations encountered will be discussed. By sharing these experiences, the presentation offers practical suggestions for using collaborative digital tools to increase engagement, foster reflection, and support active learning in language classrooms.

This study examines the pedagogical use of artificial intelligence (AI)–generated written corrective feedback (WCF) in a first-year university writing course at a private university in Japan. Conducted over a 14-week term with three classes of first-year students (n = 62), the study implemented an eight-week intervention in which learners completed three-minute speed writing tasks followed by AI-generated WCF provided by ChatGPT to support self-correction and revision. Using a mixed-methods design, quantitative data were drawn from analyses of learner writing samples, focusing on error types, frequency, and AI-generated corrections. Qualitative data were collected through a post-intervention survey examining learners’ perceptions of the usefulness of AI-generated WCF, their self-reported proficiency using AI tools, and attitudes toward AI integration in writing courses. The study addressed two research questions: (1) Did AI-generated WCF raise learners’ language awareness in writing? and (2) What are learners’ perceptions of AI use in university writing courses? Findings indicate that repeated interaction with AI-generated feedback can enhance learners’ noticing of linguistic form and accuracy, positioning AI as a language awareness raising tool rather than solely an error corrector. However, effectiveness depends on the development of learner AI literacy, particularly for autonomous use inside and outside the classroom.

Listening materials have long been the hardest for teachers to create or otherwise provide for their students. Teachers usually depend on textbooks, scour the internet or, perhaps more rarely, spend hours creating the listening activities that their students need (Rost, 2024, Alrawashdeh & Al-zayed 2017, Nemtchinova, 2020). This workshop gives teachers a chance to learn first hand how to use available AI apps and platforms to create listening materials from scratch that will be able to be adjusted and improved to fit the needs of their learners. In this workshop we will take a few topics, use AI to generate the scripts we need, learn to adjust the content to fit different learner needs, such as complexity, vocabulary content, delivery speed and others, to create a short listening practice for students to use to improve their listening comprehension. All software used for this workshop is freely available and will be limited to creating materials with one speaker but it provides an opportunity for teachers to learn the basics of creating listening materials that participants can then apply and improve upon as more and more advanced software becomes available.

This practice-oriented presentation reports on an exploratory investigation into integrating artificial intelligence (AI) within an English for General Academic Purposes (EGAP) program at a Japanese university. To enhance the program through AI adoption, the project examines where and how AI might provide pedagogical learner support, such as communication practice and formative feedback, while remaining aligned with the program’s objectives. The study draws on mixed data sources, including survey responses from over 1,000 undergraduate students and input from EGAP instructors, to identify perceptions related to AI use. Findings indicate student interest in AI for language support and idea development, alongside concerns about appropriate academic use and challenges in critically evaluating AI-generated output. Instructors expressed optimism regarding pedagogical efficacy and support and highlighted concerns related to student creativity and ethical use. In addition, a pilot study combining instructional videos with an AI chatbot was implemented to trial one possible integration point: extending listening and speaking practice beyond class time. While engagement levels were high, the results underscored the need for explicit AI literacy instruction, careful task design, and pedagogical scaffolding. The presentation outlines program design considerations and decision-making principles for AI integration in EGAP contexts, emphasizing teacher-led reflective engagement over technological dependence.

This session presents a design-based research (DBR) study of an AI-integrated financial literacy project in required EFL classes at a Japanese university. In the initial 2025 design cycle, students learned the basics of financial planning through a case-study approach. The initial instruction was technology-free, but students were granted unrestricted access to AI tools for the final task. Although pre- and post-survey data showed improvements in financial knowledge, behavior, and attitudes, classroom observation revealed that many students were reproducing AI-generated content without critical reflection. In response, the 2026 design cycle will include a gated AI model utilizing the PAIR framework (Prompt, Assess, Iterate, Reflect) and Task-Based Language Learning. During the pre-task phase, students will develop content and target language knowledge through a guided case study. AI is then introduced as a mediated learning tool to evaluate, revise, and justify its use in additional case studies that students will complete in small groups. This session shows how a DBR approach can support the structured integration of AI in EFL contexts to promote language development, critical thinking, and technological literacy.

As generative AI becomes increasingly present in language classrooms, AI literacy in English language education is often framed in terms of technical skills, tool use, or academic integrity, implicitly positioning AI use in terms of success or failure. This study adopts a practice-oriented perspective to examine how EFL students engage in academic writing when composing with generative AI in classroom-based CALL contexts. Drawing on qualitative data from approximately 90–100 undergraduate students, the study analyzes students’ prompts, paired non-AI and AI-assisted drafts, and observable writing decisions in introduction and conclusion tasks embedded in regular academic writing coursework. Rather than evaluating writing quality or AI performance, the analysis focuses on how students shape AI assistance, negotiate control over writing decisions, and respond when AI support aligns—or fails to align—with their writing intentions. Using an inductive–abductive analytic approach, the findings identify recurring patterns of appropriation, modification, and resistance to AI-generated content, suggesting that what counts as “successful” AI use emerges through situated writing practices rather than technological outcomes alone, with implications for CALL pedagogy and writing instruction.

Against the backdrop of social changes, including fewer language majors, increasingly useful AI tools, and the popularity of language learning apps, what will language teaching look like in 5, 10, or 25 years? Four experts provide their predictions and advice from diverse perspectives. Betsy Lavolette predicts that extrinsic motivations, such as travel or work, will no longer drive enrollments. Rather, intrinsic motivations, which cannot be fulfilled by AI tools, will dominate, and our teaching should shift focus to accommodate this. Dennis Koyama argues that AI creates a new baseline for how the value of language learning is measured. Accordingly, assessments must make non-AI competencies visible, with an emphasis on critical thinking and creative rhetorical design, evidenced through communicative agency, mediation, and collaborative competence. Noriko Hanabusa reflects on the traditional role of teachers in the classroom and considers what language educators should focus on to coexist with AI. To promote autonomous learning, teachers should devote more time to designing individualized learning and providing personalized instruction. Bruno Vannieu argues that foreign language learning will stay relevant if we can help students feel that they are exercising their brains and experiencing how languages shape the way humans think.

Integrating Global Englishes into mainstream EFL classrooms remains challenging, particularly in Japan, where textbooks and listening materials still overwhelmingly privilege native-speaker varieties. For educators, this barrier prevents student exposure to audio resources representing diverse English users in a pedagogically appropriate way. This presentation introduces a classroom-based approach using text-to-speech (TTS) technology to address this issue. Drawing on classroom practice at a Japanese university, the presenter demonstrates how Amazon Polly and Narakeet were used to create customised listening materials featuring a range of Global Englishes accents aligned with curricular and course assessment requirements. The TTS-generated audio was embedded into textbook listening activities and supported through scaffolding techniques such as guided noticing tasks and pre-listening discussion. This approach allowed lower proficiency students to engage with English varieties without increasing cognitive load. The presentation will feature example materials, classroom tasks, and student responses, highlighting how TTS can support exposure to English diversity while remaining practical and time-efficient for teachers. Participants will leave with ideas on how to use TTS tools to create listening materials in their own teaching contexts. The session concludes with a discussion of the pedagogical benefits, current limitations, and ethical considerations of using synthetic voices for Global Englishes input.

This research-based presentation examines how Augmented Reality (AR) technology enhances English reading comprehension among second-grade primary students in Thailand. Traditional reading instruction often lacks interactive elements, leaving young learners struggling to connect vocabulary with real-world contexts and maintain engagement. This study addresses these challenges by implementing AR-enhanced reading activities. Grounded in Constructivism Theory and the Simple View of Reading framework, this quasi-experimental study employs a one-group pretest-posttest design with 55 Grade 2 students at La-orutis Demonstration School, Lampang. The intervention integrates AR technology that overlays virtual objects, animated scenes, and interactive elements onto physical reading materials across four narrative passages. The eight-week program includes AR station rotations, interactive scene exploration, reading puzzles, and AR-based assessments. Reading comprehension is measured across four dimensions: vocabulary recognition, main idea identification, contextual understanding, and content interpretation. Preliminary findings suggest significant improvements in post-test scores, enhanced student motivation, and deeper comprehension through visual-textual connections. This presentation demonstrates practical CALL implementation for young learners, offering evidence-based strategies for integrating immersive technology into language education. Attendees will gain insights into AR tool selection, activity design, and assessment methods applicable to primary-level English instruction in technology-enhanced learning environments.

As contemporary communication increasingly relies on multiple semiotic modes, traditional views of communicative competence centered on linguistic proficiency require reconceptualization as multimodal communicative competence. In computer-assisted language learning (CALL), this shift calls for pedagogical designs that provide explicit instruction and systematic practice in multimodal meaning-making. This study reports on a classroom-based pedagogical innovation implemented in a university-level Content and Language Integrated Learning (CLIL) course in Taiwan, where English as a Foreign Language (EFL) students created interactive multimodal projects using the digital authoring platform ThingLink. Anchored in the United Nations Sustainable Development Goals (SDGs), the course aimed to develop students’ ability to communicate complex ideas through coordinated linguistic, visual, and auditory resources. Guided by a multiliteracies framework (Lim & Tan-Chia, 2022), instruction followed four learning processes—encountering, exploring, evaluating, and expressing—implemented through structured training activities. Data were collected from students’ multimodal artifacts as indicators of multimodal communicative competence. Findings suggest that explicit scaffolding and repeated practice enhanced visual organization and narrative flow, although challenges in intermodal cohesion persisted. The study underscores the need to redefine communicative competence in CALL as inherently multimodal and demonstrates the pedagogical value of structured multimodal literacy instruction in digital learning environments.

This study examines the outcomes of a 30-week English-Medium Instruction (EMI) program for eight undergraduate students at Globiz Professional University, conducted from April 2025 to January 2026. The curriculum employed advanced AI tools—specifically NotebookLM, Gemini, and ChatGPT—within a Content and Language Integrated Learning (CLIL) framework to enhance English proficiency, intercultural competence, and academic skills. Students engaged in diverse activities, including summarizing English Central videos, refining writing with AI, and completing X-Reading assignments. Each participant produced a 2,000-word research paper and delivered academic presentations. A unique aspect of the program was four weeks of focused, one-to-one interaction with native-speaking mentors, emphasizing global leadership, presentation skills, and research strategies. Comprehensive assessment included pre- and post-course Progos speaking tests and the CASEC computer-based test, with results demonstrating a one-level improvement in CEFR proficiency. Questionnaire responses indicated highly positive attitudes toward AI integration and strong satisfaction with AI-supported learning. Most students reported an expanded worldview and increased academic confidence. The findings suggest that this model of AI-mediated multimodal learning, combined with presentation-centred dialogue, provides an effective and scalable framework for EMI programs, equipping students with essential communicative, cognitive, and intercultural skills for global academic and professional success.

As generative AI tools become increasingly accessible, many language teachers report a common frustration: although AI can generate lesson materials quickly, the output often fails to align with pedagogical intent, learner readiness, or classroom realities. This practice-oriented presentation argues that the issue lies not in AI capability, but in a mismatch between how teachers plan instruction and how AI systems are typically prompted.

Grounded in established principles of Comprehensible Input (CI) and the Zone of Proximal Development (ZPD), the session foregrounds teacher cognition rather than technology. It examines how experienced instructors routinely make intuitive decisions about learner readiness—deciding when students are ready to understand, use, or extend language—and how time pressure makes it difficult to translate these judgments into explicit lesson plans.

Through classroom examples and a brief demonstration, the presentation illustrates how AI can function as a supervised planning assistant when teachers provide pedagogically meaningful constraints. Emphasis is placed on revision, selection, and rejection of AI output as essential professional practices. A short, optional planning routine demonstrates how AI can reduce planning friction while preserving instructional philosophy. The session contributes to CALL practice by modeling how teachers can integrate AI into existing workflows without relinquishing pedagogical control.

This study uniquely combines a Problem-Based Learning (PBL) approach with AI-supported learning strategies to examine the potential effects in business writing. The instructional design was informed by constructivist and scaffolding principles, using multimodal materials to support tone, style, and problem-solving. One action research project examines whether combining PBL with AI feedback tools can support critical thinking and strengthen EFL graduate students’ business-letter writing. Therefore, two classroom cycles were implemented: a complaint letter and a travel expense reimbursement task. Five students completed pre- and post-writing tasks and shared their learning experiences through surveys, interviews, and perception reports. Writing changes were tracked using Grammarly (accuracy/readability), ProWritingAid (clarity/style), and Voyant Tools (lexical density). In Phase One, creativity-related indicators showed larger but uneven shifts (e.g., style +122.2%; vocabulary density +12.4%), suggesting that open-ended problem-solving benefited some learners’ expressive development more than others. Phase Two prioritized clarity and produced smaller, steadier gains in writing quality (0.0–3.1%), along with clearer organization and a more professional tone. Students reported that peer and teacher feedback improved planning and revision, whereas heavy reliance on AI sometimes led to reduced attention to overall structure. Overall, these can support creative expression and analytical clarity through iterative, community-based feedback.

This presentation showcases the first major version update of ESL Speed Reading, the free speed reading application and learner management system software suite. Speed reading is an important, yet often overlooked, avenue for developing fluency (Tran, 2012). Being able to read fluently frees cognitive resources which can be allocated to higher-order cognitive skills (e.g., text comprehension). Traditional paper-based speed reading programs can be time-consuming for teachers due to administrative overhead (e.g., distributing reading materials). Technological advances have made it easy to implement speed reading digitally. A digital medium removes the administration burden, allowing teachers and learners to focus on the purpose of the activity: to develop reading fluency.

The ESL Speed Reading software suite has been rewritten to be more responsive, and new features have been added. The presenter will demonstrate these features, explain the benefits of a digital medium compared to a paper medium using action research, and describe how teachers can use the learner management system in their classes. Attendees will receive access to a teacher account so they can bring the benefits of speed reading into their classrooms.

This study examines an original computer-assisted reading model that integrates student-generated questioning (SGQ) with Classroom Response System (CRS) technology to support strategic, collaborative reading in EFL university contexts. While CRS tools are widely used in CALL environments, most applications rely on teacher-generated questions; far less is known about how CRS can effectively support SGQ activities that require deeper learner processing. To address this gap, a 16-week intervention was implemented with 67 first-year EFL learners using a multi-component framework: collaborative identification of main ideas and supporting details, creation of specific and wide questions, and whole-class CRS-mediated peer questioning via ClassPoint. A post-treatment perception survey and semi-structured interviews with nine learners revealed consistently positive evaluations of all components. CRS-mediated peer questioning generated the highest engagement and collaboration, while main-idea identification most strongly supported text comprehension. SGQ prompted deeper processing but was perceived as cognitively demanding, particularly when formulating wide questions. Findings demonstrate the pedagogical value of combining SGQ with CRS technology to enhance comprehension, metacognitive monitoring, and collaborative engagement in CALL-supported reading environments.

This study examined the effects of three types of extracurricular listening practice, generative AI-mediated, peer interaction, and video-based, on college EFL learners’ listening proficiency. Eighty-nine participants took part in twice-weekly, 10-minute listening activities over a semester and were assigned to one of three conditions: (1) a GenAI group engaging in interactive listening practice with ChatGPT via smartphones; (2) a peer interaction group conversing with other EFL learners; and (3) a video-based listening group receiving input without conversational interaction. Quantitative data were collected through standardized listening comprehension tests, complemented by semi-structured interviews. Results indicated that the GenAI group demonstrated significantly greater gains in post-test listening comprehension than the other two groups. Qualitative findings suggest that ChatGPT’s adaptive responses, role-playing functions, and sustained interactivity facilitated more engaging and contextually rich listening practice. The peer interaction group also showed improvement, which participants attributed to increased opportunities for meaning negotiation and communicative engagement, though learning outcomes were influenced by partner availability and proficiency. The video-based group exhibited steady gains supported by consistent input and flexible access; however, learners reported limited interaction and reduced engagement over time. These findings suggest that GenAI-mediated interaction offers an adaptive supplement to classroom listening instruction in extracurricular contexts.

As digital tools, multimodal communication, and AI increasingly shape the landscape of language learning, the question for educators is not simply whether technology is used, but how digital literacy is understood, enacted, and negotiated within real classroom ecologies. This presentation reports findings from Phase 1 of a multiphase mixed-methods study investigating evolving digital literacy practices in Singapore’s secondary Chinese-as-a-second-language (CSL) classrooms. Drawing on classroom observations, teacher interviews, student focus groups, and survey data, the analysis is guided by digital literacy pedagogical framework (Kurek & Hauck, 2014). Findings reveal an emerging yet uneven landscape of practice. Forward-thinking, pioneering teachers interpret digital literacy as encompassing multimodal composing, critical awareness, and AI-mediated meaning-making, yet their enactments are shaped by tensions between creative exploration and assessment pressures, structured scaffolding and learner autonomy. Students respond positively to multimodal tasks, but often engage superficially with AI tools and struggle to connect classroom digital practices with everyday digital engagements. Rather than a linear narrative of innovation, the results illustrate a dynamic interplay of affordances, constraints, and adaptive sense-making. Many challenges observed are systemic—rooted not in individual actors but in broader structures. The study contributes to ongoing CALL discussions on designing contextually grounded and multiliteracies-informed digital literacy pedagogies.

As communication increasingly involves both textual and visual literacy, language classrooms still tend to prioritize text, leaving visual communication skills underdeveloped. This presentation reports on an attempt to teach document design in a 15-week Digital Communication course. Grounded in aesthetic instructional design, theory-based document design, and agile iteration, the course progressed from learning design fundamentals to hands-on projects, with several layers of scaffolding provided along the way. Students created a document set consisting of a flyer and a promotional video for an authentic campus activity using Canva, a novice-friendly digital design tool.

After outlining the course design, the presentation reflects on the benefits and challenges that emerged during its implementation. One key finding is that working on a real-life campus activity can meaningfully engage students. At the same time, while implementing an agile approach in an EFL context is feasible, it requires efficiency in teaching foundational concepts as well as flexibility in responding to rapidly evolving tools such as Canva. The presentation concludes by arguing that, even in the age of generative AI, such coursework continues to offer valuable learning experiences whose outcomes may be more lasting and meaningful than relying on machines to produce technically impeccable design artifacts.

Prompt literacy is framed in this workshop as an emerging language and learning skill that influences whether AI use undermines learning or meaningfully supports comprehension, practice, brainstorming, and reflection. This session examines how educators and students use AI in different ways, drawing on classroom practice in Japanese public high schools, an AI-focused unit with secondary students, professional dialogue with JET ALTs, and published cross-curricular research on AI use in the classroom. The workshop is organized around contrasting classroom-based and English debate examples of ineffective and effective AI use by students and teachers, with particular attention to how learners ask questions of tools such as ChatGPT, Gemini, and Google Translate/DeepL. It emphasizes how generative AI and CALL tools can function as scaffolding for thinking, communication, and inquiry rather than substitutes for learning. Designed for educators interested in practical classroom applications, the session uses discussion and reflection to explore how task design can support responsible and productive AI use. Participants will discuss examples of AI use in their own contexts, consider how to guide students in using AI more effectively, and leave with practical, immediately usable ideas for brainstorming, writing support, and fostering communication across proficiency levels.

Listening is a critical skill in second language acquisition, yet it remains one of the most challenging for English as a Foreign Language (EFL) learners, particularly college students who are developing higher-level academic listening skills. Traditionally, studies on listening comprehension focus on identifying barriers such as fast speech, unfamiliar vocabulary, or accents that hinder understanding. While these studies provide valuable insights, they often frame listening difficulties as external obstacles, overlooking the internal cognitive and affective processes learners experience while navigating these challenges. This study shifts the focus from identifying listening barriers to examining perplexity as a cognitive–affective experience among college-level EFL learners. Perplexity refers to moments of confusion, uncertainty, or mental overload that occur when learners process spoken English.

Using a qualitative research design, the study involves undergraduate students enrolled in an English course where podcasts are used as material for observation. Data are collected through semi-structured interviews and classroom observations of podcast-based listening activities submitted as part of students’ coursework. The findings aim to provide insights for designing listening tasks and assessment practices that better support learners during moments of confusion in L2 listening.

As Generative AI (GenAI) becomes increasingly embedded in language classrooms, many EFL learners risk becoming passive consumers of algorithmic outputs, often deferring to AI’s perceived “native-like” authority. Such reliance can undermine learner agency and reinforce standard language ideologies. This study reports on a classroom-based pedagogical intervention, Speaking Back to AI, designed to help Japanese university EFL students critically engage with GenAI tools. Drawing on the APSE model of Critical AI Literacy, the intervention was implemented as a short module within a regular EFL course. About 50 Students participated in iterative “red-teaming” activities, evaluating GenAI-generated content for hallucinations, linguistic stereotyping, and pragmatic misalignment with local norms. Rather than revising their work to align with AI suggestions, students produced multimodal artifacts, such as annotated AI outputs and short reflective digital products, that corrected, resisted, or recontextualized problematic outputs. Data sources include screen-recorded red-teaming tasks capturing students’ testing and negotiation with GenAI outputs, reflective journals, and pre- and post-intervention surveys. Preliminary observations suggest increased learner awareness of AI limitations and greater willingness to assert authorial voice when negotiating with GenAI outputs. The presentation concludes with reflections on challenges and how similar Critical AI Literacy interventions can be adapted for other EFL contexts.

Many language teaching tasks work better through structured AI workflows that coordinate multiple tools rather than relying on a single AI tool. For example, generating realistic multi-character conversations, providing scaffolded feedback that adapts to student responses, or managing feedback across many students often requires a sequence of coordinated AI actions. A typical workflow might allow students to record a spoken dialogue, have the audio automatically transcribed, receive targeted feedback on vocabulary, grammar, and fluency, and then obtain an overall evaluation or grade.

This practice-oriented session presents how teachers can build such workflows using AI tools to complete multi-step pedagogical tasks. Co-presented by a language teacher with no programming background and an app developer, the session presents classroom-tested examples including generating multi-character dialogues, building personalised feedback cycles for student writing, and managing scalable homework correction.

Participants will see how chaining instructions allows teachers to guide AI behaviour more reliably than relying on a single tool. The session also demonstrates how local tools can be combined with online services to improve privacy and reduce platform lock-in.

Participants will leave with practical examples and a simple framework they can adapt to their own teaching contexts.

Scaffolding reading can be challenging, especially for busy teachers. One major challenge is providing students with enough encounters with new vocabulary across varied and meaningful contexts in order for learning to occur. One approach that has become popular in Comprehensible Input–based teaching approaches is embedded readings developed by practitioner Laurie Clarcq. These are a series of leveled texts that recycle the same target vocabulary in meaningful ways while gradually increasing complexity and difficulty. When creating embedded readings, AI can be a powerful tool to support learners in acquiring language and save teachers time. In this presentation, we will show how AI can be used to quickly create embedded readings, how teachers can guide students to use AI appropriately for this purpose, and how these tools can support language learning. We will also compare embedded reading to other reading passages created by AI, emphasizing tradeoffs. Finally, we will end by discussing possible challenges, such as AI hallucinations and maintaining effective vocabulary control and repetition.

In the 2023/2024 academic year, students in a university-level academic writing course completed a research project which included an annotated bibliography assessment. At the end of the course, 683 students responded to an open-ended survey about their use of generative AI during the assignment. Half of the students reported using AI to complete their annotated bibliography and some provided descriptions of how AI was used. The number of students who reported submitting wholly or partly AI-generated summaries raised questions about revising summary-writing tasks in future courses, including if summarizing should still be taught and assessed. This presentation will briefly look at “old” summarizing skills like reading, annotating, note-taking, paraphrasing, condensing, and citing before exploring possibilities for developing “new” summarizing skills in the age of AI (e.g., critically comparing AI-generated summaries, verifying AI content, developing students’ own voice as authors, maximizing learning with AI summarizing tools). The presentation will conclude with pedagogical implications by inviting attendees to share their thoughts on the future of summary writing in academic contexts.

This workshop introduces School AI, an innovative platform designed to help educators guide students in the appropriate use of artificial intelligence (AI). Teachers can define specific parameters for generative AI interactions by creating “Spaces” tailored to students’ needs and academic goals. Because these controls are set by the teacher, they help minimize overreliance on AI and safeguard academic integrity. The session will provide an overview of the tool, practical examples, a hands-on activity, and conclude with a Q&A session.

The overview will include a general introduction to the platform, demonstrating how navigation works and how to create an account. Real-life student examples will then be presented, and the presenter will explain how the tool can be integrated into the classroom by adding assignments to an LMS and through other methods that make the AI tool available to students. The hands-on activity will engage the audience directly. Participants will first use the tool as students to see how it functions in a classroom setting. They will then have time to create their own assignment from a teacher’s perspective, with time allotted for a few participants to share what they have created.

This study investigates EFL students’ subtitling workflows for self-produced mini documentaries, drawing on Romero-Fresco’s (2013) accessible filmmaking framework and Tardel’s (2023) subtitling model. Participants are students in an elective English course at a Japanese national university where they conduct an interview in Japanese, edit the video and create reverse subtitles in English; authentic tasks that involve multimodal literacy skills. The research employs an in-depth qualitative case study design with thick description to ensure reliability (Riege, 2003), supplemented by quantitative measures. The pilot (n=1) failed when think-aloud protocols (TAPs), the original main instrument, proved unsuitable due to scheduling constraints, TAP limitations for complex multimodal tasks, and language barriers, yielding insufficient workflow data. For the main study (n=2 focal participants), we pivoted to triangulation (Yin, 2018): home-based extended screen recordings for ecological validity and depth, in addition to field notes, logbooks, learning diaries, and interviews. These are bolstered by materials from consenting classmates (n=20) to strengthen the findings. Preliminary analysis suggests this combination overcomes pilot failures, enabling richer data on subtitling processes and demonstrating how methodological setbacks can strengthen research design.

This study examines the effects of generative AI assistance during the prewriting phase of a second-language (L2) summary writing task in a university English course. Fifty-five Japanese EFL students participated in a counterbalanced within-class design involving two summary tasks completed one week apart. In each task, learners used computers to study for 12 minutes a listening script from a homework listening completed the previous week, then wrote a handwritten summary of approximately 60 words from memory. In the AI condition, learners used generative AI tools (e.g., ChatGPT or Copilot) during prewriting; in the non-AI condition, they relied mainly on machine translation. Learners completed two listening-script summaries and switched conditions between tasks.

Summary quality was assessed for content coverage, syntactic complexity, and formal accuracy. Vocabulary learning was measured using a simplified Vocabulary Knowledge Scale administered before and after each task and on a delayed post-test. Post-task surveys and self-reported AI chat logs documented tool use. Learners used AI to identify key ideas, simplify language, generate model summaries, and check drafts, while students relying mainly on machine translation (often Google Translate) showed greater gains in key vocabulary. Full results on writing quality and relationships between AI usage strategies and outcomes will be presented.

Input is foundational to second language acquisition. With the explosion of digital media, practically unlimited resources are now available to both learners and educators. Yet in practice, while a rich ecosystem of graded readers and skill-focused textbooks support abundant reading input, resources for listening are comparatively scarce, with materials often tied to reading passages, test prep, or authentic content that may not suit a given class. This presents a challenge for educators seeking listening materials with appropriate proficiency, length, content, and language focus. To this end, recent advancements in text-to-speech technology, particularly the improved naturalness, customizable tones, and multi-speaker support, have revolutionized the ease with which educators can create highly tailored listening materials. This presentation will explore the practical integration of AI-generated audio in EFL classrooms, ranging from extensive listening to focused classroom tasks. The presenter will share his own successes and challenges with listening materials such as example dialogues using target vocabulary for fluency practice, alternative versions of reading passages for comprehension tasks, and audio prompts for test items. Survey results from first- and third/fourth-year Japanese university students on the perceived usefulness, engagement, and preferred applications of AI-audio listening tasks will also be presented.

The prevalence of Large Language Models (LLMs), such as ChatGPT, has sparked debate regarding their influence on student academic writing. While existing literature has predominantly investigated LLM usage through quantitative methods—such as word frequency statistics and probability-based detection—few studies have systematically assessed the potential impact of these tools on the linguistic characteristics of student writing. To bridge this gap, the researchers analyzed 1,000 research papers written by fourth-year students in humanities disciplines at universities in Hong Kong. Using a self-compiled corpus, this study compared papers written before and after the launch of ChatGPT, specifically examining two linguistic features: namely, the frequency of LLM-preferred vocabulary and syntactic complexity. This study uses the Tool for the Automatic Analysis of Syntactic Sophistication and Complexity (TAASSC) to calculate 14 measures of syntactic complexity, which can be categorised into five types: length of production unit, amount of subordination, amount of coordination, phrasal structures, and sentence complexity. The results indicate a significant rise in LLM-preferred lexical patterns, demonstrating the influence of generative AI on student argumentative writing. Preliminary findings also indicate that syntactic complexity declined, pointing toward simpler sentence structures. These findings provide valuable insights for educators seeking to distinguish between human and AI-generated text.

This study examines how AI-enhanced Computer-Assisted Language Learning (CALL) tools can improve English language learning among autistic students in Pakistani inclusive classrooms. The aim is to examine whether adaptive AI characteristics—such as individualized pacing, multimodal interfaces, and computer-generated feedback—can facilitate engagement, comprehension, and emotional regulation among learners with autism spectrum disorder, while also benefiting neurotypical learners. The study adopts a small-scale mixed-method intervention design conducted in an urban Pakistani ESL context with 30 participants, including autistic and neurotypical learners. AI-based language learning applications were implemented over a period of eight weeks. Quantitative data were collected through pre- and post-language assessments, while qualitative data included classroom observations, teacher interviews, and parental feedback. The design incorporated ethical considerations and cultural relevance. The findings indicate improved vocabulary retention, reduced anxiety during language activities, and greater learner independence among autistic students. Teachers also reported that AI-supported tools did not increase instructional workload and supported differentiated instruction in inclusive classrooms. This study contributes to the field of CALL by highlighting AI-driven inclusive language learning within a Global South context. It offers practical implications for designing inclusive CALL environments, policy recommendations for integrating special education within mainstream ESL classrooms.

This poster session will examine the use of activities that incorporate free, online AI applications in a Japanese, intermediate EFL, second-year university classroom. It will focus on both teacher and student experiences. The presenter will report on the students’ use of AI tools to develop reading, listening, speaking, and researching skills. Specifically, activities described include having students creating their own reading and listening texts with ChatGPT and Luvvoice.com, co-writing texts with ChatGPT, and working with AI applications to scaffold their research and planning for presentation projects. This poster session highlights challenges and successes encountered during the process of integrating these AI activities. Specifically, concerns relating to ethical issues, inaccuracies, and misinformation associated with using AI and how the students were guided in these areas. Selected teacher and student reflections will be highlighted to show key moments of success, frustration, and the pedagogical puzzles that emerged in piloting these AI-supported activities. The ultimate goal in sharing this information is to stimulate discussions with audience members so that they might reflect on their own teaching contexts, and to exchange practical ideas so that teachers can prevail, rather than fail, in their exploration of AI as an effective tool for language development.

As AI chatbots become mainstream in education, instructors in particular are wondering if these chatbots help students prevail—or fail. In English for specific purposes (ESP) environments, chatbots offer a promising solution for the speaking practice gap in which students cannot get enough feedback from an instructor. However, generic chatbots like ChatGPT are developed to be “helpful” and will autocorrect errors, “interpret” unclear responses, and ignore silences, thereby doing the cognitive work for learners. There is still a debate on whether AI helps or inhibits ESL learning. Moreover, technostress still prevails for both students navigating unfamiliar learning methods and teachers forcing chatbots to fit contexts they were not built for. Here, I present a framework for designing AI learning partners for self-directed speaking practice in a large-class, tertiary-level ESP communication course (n=117). The tasks were designed to be adaptable to different role-playing scenarios, outcome targets, and language levels. I will share lessons learned from my struggles with the frequent changes in free chatbot services and my successes in engineering a “good enough” stable AI speaking partner for my ESP context. My experiences will help educators design or refine chatbot activities to match their learners, ESP objectives, and classroom reality.

This study explores how Japanese university students used a custom GPT chatbot to support English speaking practice in a first-year Basic English Communication course. The chatbot was built using conversation scripts and target vocabulary from the course textbook (Conversations in Class by Alma publishing), allowing students to practice class content through structured AI-mediated dialogue. Participants were 14 first-year students. Over an eight-week period, two groups completed different forms of chatbot practice. One group conducted short daily conversations (3–5 minutes) using free ChatGPT accounts outside class. A second group participated in longer weekly sessions (10–15 minutes) in the instructor’s office, which were video-recorded for analysis. The chatbot provided immediate feedback through prompts, compliments, reformulations of student responses, and suggestions for more natural expressions. Quantitative data included chatbot usage logs, pre/post speaking tasks measuring fluency, and Likert-scale surveys on motivation and perceived usefulness. Qualitative data came from reflection prompts and interviews, and analysis of students’ behavior and body language during the recorded speaking sessions. Results showed that students were generally enthusiastic about using the chatbot to improve their speaking ability. However, some participants experienced difficulties interacting with the technology, which may reflect varying levels of technological literacy among students.

Japanese EFL learners frequently experience difficulty producing American English (AE) vowels, which are perceptually assimilated into native Japanese categories. While previous research has shown that auditory-based training can improve vowel identification, few studies have examined whether multimodal CALL-based training leads to measurable changes in learners’ vowel production. This study extends our earlier work using JSpace, an interactive web-based vowel-space training tool that integrates auditory input with visual representations of vowel quality and explores its role in supporting L2 phonological restructuring through multimodal input. Sixty Japanese university students were assigned to either a multimodal JSpace training group or a control group receiving traditional auditory-only identification training during a four-week period. Both groups completed identical pretest and posttest discrimination and production tasks targeting seven AE vowels presented in CVC contexts. In addition to perceptual measures, this study focuses on an acoustic analysis of learners’ vowel productions. Changes in first and second formant (F1–F2) frequencies were examined to assess pre- to post-training shifts in the acoustic vowel space. Results show that learners trained with JSpace exhibited greater shifts toward native AE vowel targets than the control group, indicating systematic restructuring of L2 vowel production. Pedagogical implications for CALL-based pronunciation instruction are discussed.

This pilot study explores how technology-mediated speaking tasks can support the development of oral summarization in a CLIL (Content and Language Integrated Learning) course at a Japanese university. Using EnglishCentral as a digital platform, first-year EFL students engaged with video content and completed repeated oral summarization tasks over the course of a semester. In addition to video viewing and vocabulary-learning activities, learners interacted with MIMI, EnglishCentral’s AI discussion chatbot, to rehearse ideas and language prior to recording spoken summaries. The project investigates how the combination of authentic video input, AI-mediated interaction, and repeated recording tasks may support students’ emerging summarization skills. Particular attention is given to learners’ use of content-specific vocabulary, complexity of spoken language, and overall comprehensibility. As this study represents an initial investigation, qualitative analysis centers on six students who completed all required summary recordings. Audio recordings and platform data are examined to identify patterns of development across time. While large-scale statistical claims are not the goal of this phase, the study aims to demonstrate how CALL tools can scaffold spoken output in cognitively demanding CLIL contexts. Findings will inform the design of a subsequent larger-scale study investigating longitudinal development of oral summarization through AI-supported CLIL tasks.

Many universities outsource placement to “off-the-shelf” tests, but AI now lets schools produce localized, tailor-made assessment. This entry-level “how-to” presentation shows how AI tools were used to simplify test design processes. The presenters share their experience producing a new 50-item multiple choice format placement test for 300 students, measuring listening, grammar, and reading skills. The presenters begin by outlining the motivations for creating the new test, continue by describing prompt engineering techniques used to generate items, and finally discuss how AI and test makers can collaborate ethically and productively. The presenters created prompts for each section, using LLMs (like ChatGPT or Claude). Items created were then human-reviewed and edited. Following a pilot test and prior to implementation, further edits were made with human-AI collaboration. Through trial and error, the new test showed improvement across multiple metrics, including item quality (ID), and test reliability (from α=0.8 to α=0.87). Items and content that did not perform well in-context were easily changed. These results were achieved in a much shorter timeframe than usual (about 50%). This project serves as a blueprint for how departments can reduce development time, increase quality, and subsequently better assess their own target student population.

This presentation reflects on the use of H5P, an open-source plugin for Moodle, to create engaging and inclusive activities for university English learners. Aimed at teachers new to H5P, it demonstrates step by step how Moodle can become an interactive and adaptive learning space through multimodal activities, supporting diverse learners.

Drawing on the presenter’s experience integrating H5P into an undergraduate English writing and oral presentation course at a Japanese university, together with insights from recent literature, the session highlights both the successes and limitations of H5P implementation. Examples, such as interactive videos and branching activities illustrate how H5P can enhance learning experiences, motivation, learner autonomy and feedback opportunities. The presentation also addresses challenges including time investment, usability constraints, and content or design overload, reflecting on how and why some implementations succeeded while others did not.

Rather than presenting H5P as a one-size fits all solution, the session offers an honest practice-based reflection on what worked, what did not and lessons learned for future CALL practice. Participants will gain practical insights into how to avoid common pitfalls and adopt H5P effectively to support more active and inclusive learning in Moodle-supported language classrooms.