Prof. Hiroko Kanoh,Yamagata University, Japan

Bio: Hiroko Kanoh is an Associate Professor at Yamagata University, Japan. Her research focuses on information education, AI literacy, cyberpsychology, and the educational use of emerging technologies such as generative AI and the metaverse. She is particularly interested in how digital technologies influence learning, well-being, social interaction, and decision-making. Her recent work has explored AI-supported learning, local large language model environments for education, misinformation and fake news, online harassment, and the design of digital support systems such as AI counselors and learning assistants. She has presented and published internationally on topics related to AI in education, educational technology, and digital society. Through both research and practice, she aims to promote responsible and meaningful uses of technology in education and to support learners’ autonomy, critical thinking, and well-being in the age of AI.

Talk title: Generative AI Adoption in Japan: Demographic and Psychological Factors and Implications for Education
Abstract: This study examines demographic and psychological factors associated with generative AI adoption in Japan and considers their implications for education and AI literacy support. Using original survey data from 1,724 respondents, the study analyzes generative AI usage frequency through descriptive statistics and multinomial logistic regression. Age, gender, approval need, and well-being were significant predictors of usage frequency, whereas fulfillment of approval need was not. Compared with almost-daily users, non-users tended to be older and lower in approval need. Descriptive analyses also showed clear disparities, with younger respondents and males reporting higher levels of use. These findings suggest that generative AI adoption is associated not only with demographic characteristics but also with underlying motivational dispositions related to social evaluation. For education, the results imply that AI literacy support should not assume uniform readiness among learners and educators; differentiated entry points, scaffolding, and benefit framing may be needed for groups with lower prior engagement. The study contributes national evidence from Japan and highlights the value of linking demographic disparities with psychological motivation when designing educational support for generative AI.

Assoc. Prof. Abu Bakar Mohamed Razali, Universiti Putra Malaysia, Malaysia
Bio: Abu Bakar Razali graduated from the Department of Teacher Education, College of Education, Michigan State University. Abu Bakar graduated with a doctoral degree of philosophy in Curriculum, Instruction, and Teacher Education, with a graduate specialization in Language and Literacy Education. Abu Bakar currently works as an associate professor at the Department of Language and Humanities Education at Faculty of Educational Studies, Universiti Putra Malaysia (UPM). Abu Bakar's research interest is primarily on the teaching and learning of English as a second language. He is particularly interested in reading and writing instruction and digital technology in the teaching and learning of English as a second language. He hopes to delve more deeply into the field of English language reading and writing instruction and digital technology especially in the teaching and learning of English as a second language.

Title: Weaving and Untangling the Web of Binaries on the Use of Digital Technology in Education

Abstract: Due to the advances of the Internet and information and communication technology (ICT), and lately Artificial Intelligence (AI), there is a big push for digital technology in education. This push is articulated in nationwide initiatives for the increased and widespread use of digital technology in education. In the case of Asian countries, there is inherently a very high press by the Asian governments for the implementation of digital technology in education, especially due to the role of technology within the globalized and digitalized society. As such, there have been numerous studies conducted to investigate the use of digital technology in education in Asia. Many research report positive attitudes about and perceptions of the use of digital technology in education to which research have also yielded positive results to teachers’ and students’ perceptions and uses of various kinds of digital technology in education. However, research also report teachers’ concerns with using digital technology in education, citing lack of access and resources and lack of training to use them, as well as low motivation, fear of technology, and concerns about changing their teaching styles and methods. In truth, there are binaries within the use of digital technology in education, and this issue of binaries of the use of digital technology in education is not special to Asia only but experienced all over the world. The binaries within the use of digital technology in education come in the forms of affordances (or benefits) vs. constraints; digital equity vs. digital divide, which are further divided into notions of access (to technology) vs. restraints (from technology); and the notions of digital natives (i.e., experts) vs. digital non-natives (i.e., non-experts). It is of utmost importance to understand these binaries in digital technology because they can be quite powerful elements in building perceptions and ensuring performance of both teachers and students in using digital technology in education. It is also very important to manage and balance these binaries to enable teachers and students to use digital technology properly to have the desired positive impacts on education.

Assoc. Prof. Dr. Sarimah Shamsudin, Universiti Teknologi Malaysia, Malaysia
Bio: Dr. Sarimah Shamsudin is an Associate Professor at the Faculty of Social Sciences and Humanities (FSSH) in Universiti Teknologi Malaysia (UTM), Kuala Lumpur (KL), Malaysia with more than 25 years of teaching experience and to date has received several Learning Innovation and Design Awards internationally, three Excellent Service Awards from UTM and six from her faculty. She also received the Writer in Indexed Journal Faculty Category Award from UTM twice, in 2020 and 2024. She held several admin posts throughout her career in UTM such as the Director (Social Sciences and Humanities) (FSSH, UTM KL), Head of Language Department (Language Academy, UTM KL), Acting Deputy Dean (Language Academy, UTM KL), Head of Language Lab and Multimedia Lab (FPPSM, UTM JB), Coordinator of Language Lab (Language Academy, UTM KL), Head of English Language Panel and Head of Testing and Evaluation or Moderation Committee (Language Academy, UTM JB & UTM KL). She obtained her Bachelors of Science degree in Computer Mathematics from Carleton University, Ottawa, Canada, Post-Graduate Diploma in Teaching English as a Second Language (TESL) from Institute Technology MARA, Malaysia, Masters of Science in Teaching English for Specific Purposes (TESP) from Aston University, Birmingham, UK and PhD in English Language Teaching (ELT) and Applied Linguistics from The University of Warwick, Coventry, UK. Her PhD thesis was on Computer-Mediated Communication (CMC) and English for Specific Purposes (ESP): An Investigation of the Use of Synchronous CMC to Meet the Needs of Computer Science Students and her Masters thesis was on Development of Web-Based Civil Engineering Materials Specialised Vocabulary List Via Corpus-Based Analysis of Civil Engineering Materials Textbooks. She has presented papers at international conferences in various countries such as the USA, Ireland, UK, Poland, UAE, Turkey, Thailand, Japan, Vietnam, Indonesia and Malaysia. She was an Invited or Keynote Speaker in several international conferences in Malaysia, Indonesia, Iran and Japan. To date, she has also published almost 100 articles in indexed and non-indexed journals as well as indexed and non-indexed conference proceedings. She has also published two original books and 13 book chapters. Apart from that, she has had experience obtaining a few millions in ringgit Malaysia of infrastructure development funding to set up and manage Digital Language Labs in UTM Johor Bahru and Kuala Lumpur and received almost half a million of ringgit Malaysia of local and international research funding as the main or co-researcher to implement research related to English language learning, teaching and learning using technology, cognitive abilities, development of corpus-based materials and corpus-based analysis of discourse. She is also continuously invited to review journal articles and conference papers both locally and abroad and has successfully graduated 16 PhD students and nine Master students from countries such as Malaysia, China, Iran, Pakistan and Sri Lanka. She has also filed thirteen (13) copyrights with her PhD and Masters graduates and examined 14 PhD candidates as Internal Examiner and eight as External Examiner. Her research interests are in the areas of Computer-Assisted Language Learning (CALL), AI-Assisted Language Learning (AI-ALL), Computer-Mediated Communication (CMC), Corpus Linguistics, English Language Teaching (ELT) and English for Specific Purpose (ESP).

Title: Supporting Non-Specialist Primary School Teachers’ English Language Development through Error Analysis and Self-Paced Digital Learning Interventions

Abstract: The purpose of this study is to enhance the English proficiency of non-specialist English language (EL) teachers in a private primary school by uncovering their errors during EL lessons conducted online and providing self-paced digital learning interventions via the Internet. Data was collected from 20 video recordings of online class sessions across five non-specialist EL teachers (four recordings per teacher). These teachers do not have formal qualification to teach English as a second language but were assigned to teach English to their primary school students due to their substantially good proficiency in English. The video recordings of classroom discourse were transcribed and analysed using Corder’s Error Analysis (EA) framework. The errors produced were classified into as phonological and grammatical errors. The data revealed that phonological errors occurred most frequently followed by grammatical errors. Based on these empirical findings, internet-based learning activities utilising specialised grammar and pronunciation software were assigned to the non-specialist teachers. Qualitative evaluation indicated that integrating error analysis with self-paced digital learning via the Internet heightened non-specialist teachers' language awareness and was perceived by participants as helpful for improving productive language accuracy.

Dr. Dongkun Han, The Chinese University of Hong Kong, Hong Kong, China

Bio: Dr. Dongkun Han is a Senior Lecturer in the Department of Mechanical and Automation Engineering at The Chinese University of Hong Kong. He received his Ph.D. in Electrical and Electronic Engineering from The University of Hong Kong, and has held research and visiting positions at the Technical University of Munich, the University of Michigan, Stanford University, and the German Institute of Science and Technology in Singapore. His educational research focuses on artificial intelligence in mathematics and general education, e-learning and experiential learning in robotics education, and professional development for engineering educators. He also co-founded the CUHK Smart Garden to promote sustainability education and Sustainable Development Goals through renewable energy and recycling initiatives. Dr. Han has served in various leadership roles for international conferences. His learner-centred teaching has been recognized by the Vice-Chancellor’s Exemplary Teaching Award, the University Education Award, and multiple faculty and general education teaching awards.

Talk title: From Learner Data to Personalized Learning: Explainable AI for Adaptive Mathematics Education
Abstract: Artificial intelligence is creating new opportunities to transform learner data into timely, personalized, and actionable educational support. However, many AI-based learning systems remain difficult for teachers and students to interpret, particularly when they are developed using small and continuously evolving classroom datasets. This invited talk presents an explainable and data-efficient approach to adaptive mathematics education based on Dynamic Distance-Weighted k-Nearest Neighbors, or DDW-kNN. Unlike conventional kNN models that rely on a fixed distance metric, DDW-kNN dynamically adjusts the relative importance of learner characteristics, assessment performance, and learning-item features as new evidence becomes available. This enables the system to identify more educationally meaningful similarities among learners and resources while preserving interpretability. Each proficiency estimate or learning recommendation can be explained through the most relevant neighboring learners, assessment patterns, and content items. The approach is implemented through a closed-loop personalized learning framework comprising iTest, which generates tailored assessments and estimates learner proficiency, and iLearn, which recommends readings and instructional videos aligned with individual mastery levels. Drawing on classroom implementation and evaluation, the talk will discuss the design principles, practical considerations, and lessons learned from integrating explainable learner modeling, adaptive assessment, and personalized content recommendation. It will also explore how transparent and data-efficient AI can support teacher decision-making, strengthen learner engagement, and contribute to responsible educational innovation across different learning contexts.