Keynote Speakers (as of June 2026)
 | Professor CHEUNG Chi-keung, Alan Title: AI in Language Education — Evidence, Paradigms, and Implications Language: English
Abstract In language education, the central question is no longer simply whether artificial intelligence (AI) should be used, but how it can be used effectively and responsibly, under what conditions, and for which learners. Drawing on three recent research reviews, this keynote provides an evidence-based synthesis of AI’s effectiveness in language education. The evidence suggests that chatbot-assisted learning produces a moderate positive effect on overall language performance, with particularly strong gains in vocabulary development and among intermediate-level learners. Studies further show that robots’ socially supportive behaviors contribute small but educationally meaningful improvements, especially when robots are positioned as tutors rather than peers. These findings point to a key message: the value of AI depends not only on the technology itself, but also on the pedagogical roles it assumes, the learners it supports, and the learning conditions in which it is embedded. To frame these choices, the keynote proposes three paradigms of AI integration in language education: AI-directed, co-directed, and agency-oriented, which together mark a shift toward more human-centered teaching and learning. The keynote concludes with practical recommendations for primary and secondary education, highlighting the importance of thoughtful pedagogical design, learner differentiation, teacher professional development, and careful attention to ethical considerations. Bio Professor Cheung currently serves as Dean of the Faculty of Education and Director of the Centre for University and School Partnership at The Chinese University of Hong Kong. He also holds a Courtesy Professorship at Johns Hopkins University. His research focuses on evidence-based reform, language education, and educational technology. He has led and participated in numerous large-scale randomized controlled trials aimed at improving educational practice and student outcomes. |
 | Professor LEE, Jang Ho Title: GenAI-Mediated Activity Theory: A Conceptual Framework for Understanding How Generative AI Is Transforming Language Teachers’ Pedagogical Practices Language: English
Abstract As generative AI (GenAI) tools and platforms are rapidly being employed in language teaching and learning, they are reshaping how teachers design instruction, interact with learners, and participate in professional communities. In response to these shifts, this keynote introduces the GenAI-Mediated Activity Theory (GMAT), a conceptual framework designed to explain how GenAI is transforming language teachers’ preparation and pedagogical practices within increasingly complex pedagogical ecosystems. Building on Engeström’s Activity Theory, the talk recontextualizes its core principles to illustrate how GenAI is reshaping the relationships among teachers, GenAI tools, lesson objectives, and learning environments. Bio Jang Ho LEE is a Professor of English Education at Chung-Ang University, Seoul, Republic of Korea. He earned his DPhil in Education from the University of Oxford. His research focuses on AI-assisted language learning, the pedagogical use of students’ first language, English-medium instruction, and second language vocabulary acquisition. He has published more than 70 journal articles and book chapters, including 50 papers in Web of Science–indexed journals, such as Applied Linguistics, The Modern Language Journal, TESOL Quarterly, ReCALL, System, Language Learning, Language Teaching Research, and Language Learning & Technology, among others. In addition to his research and teaching, he has been active internationally. He served as a Visiting Professor at The Hong Kong Polytechnic University during the 2025–2026 academic year and was also appointed Visiting Professor at King Mongkut’s University of Technology Thonburi in 2025. His work aims to bridge SLA theory, pedagogical practice, and emerging educational technologies. |
 | Professor MCENERY, Tony Title: Using Big Data and AI to Look at Small Conversations – The Value of Data in Teaching Conversational English Language: English
Conversational interaction in a second language is a demanding task – it provides little to no time to plan and speakers have to speak while dealing with competing demands for attention, such as listening to what is being said to them and performing a task. It is no surprise, then, that learners find the task of speaking challenging. Yet conversational interaction is an important skill to acquire. In this talk I will survey findings from the study of a very large dataset, the Trinity Lancaster Corpus, composed of conversations between learners of English and native speakers of English. Using this dataset of over three million words, I want to demonstrate the benefit of using computers to process and explore learner language by presenting some findings which show that, on the basis of sound evidence, we can improve the teaching of English conversational skills. I will also present associated classroom teaching materials based on those findings. The talk concludes by considering the findings presented in the light of work in AI and reflecting on the degree to which AI can, or cannot, help in the teaching of these skills. Bio Tony McEnery is Chair Professor in the Department of English and Communication, The Hong Kong Polytechnic University, Adjunct Professor at Xi’an Jiaotong University, Advisory Professor at Shanghai International Studies University and emeritus Professor at Lancaster University. He has published widely on corpus linguistics and is the author of Corpus Linguistics: Method, Theory and Practice (with Andrew Hardie, Cambridge University Press, 2011). His latest books are Learner Language, Discourse and Interaction: A Corpus-Based Analysis of Spoken English (with Isobelle Clarke and Gavin Brookes) and was published by Cambridge University Press in 2025. He is a Fellow of the Academy of Social Sciences, the Global China Academy and the Royal Society of Arts in the UK. ORCID: https://orcid.org/0000-0002-8425-6403 |