By Dr Lucas Kohnke
The Education University of Hong Kong

In today’s rapidly evolving technological landscape, Generative AI (GenAI) is reshaping the role of TESOL educators and transforming language learning. As AI continues to drive these changes, teacher education programs must adapt to equip future educators with the skills necessary to thrive in AI-enhanced classrooms. 

In this presentation, I want to focus on an essential question: How can TESOL educators redefine their core competencies in response to these emerging technologies while maintaining the human-centered pedagogies at the heart of effective language teaching? Rather than relying on outdated, traditional methods, TESOL educators must evolve to meet the demands of modern classrooms. But how do we achieve this? The answer lies in adopting sustainable strategies—approaches that allow us to integrate AI without constantly overhauling our curricula or overwhelming our teachers with new tools.

We’ll explore:

• How TESOL programs can integrate GenAI in a way that enhances learning without making teachers overly dependent on technology.

• Practical strategies for continuous adaptation and ongoing professional development—ensuring that educators are always equipped to face future challenges.

• The balance between technology and human-centered teaching maintains the core values of connection, empathy, and personalized learning essential to language instruction.

By focusing on sustainability and adaptability, TESOL educators can lead in current and future classrooms, using AI to their advantage while staying grounded in the principles that make language learning meaningful.

Join me as we explore actionable principles for integrating GenAI into TESOL teacher education, creating future-ready educators who can confidently navigate the evolving landscape of language instruction.

Biography

Dr. Lucas Kohnke’s research focuses on technology-supported teaching and learning, and teacher professional development with emerging technologies. He has contributed over 60 articles to leading journals such as Computers and Education: Artificial Intelligence, Educational Technology & Society, Education and Information Technologies, ReCALL, SYSTEM, and RELC. His diverse teaching areas include integrating technology into language classrooms, AI in education, teacher training, and English language teaching methodologies. He is a top 2% most cited scholar in education 2024.

By Dr Barry Bai
The Chinese University of Hong Kong

Learning motivation and engagement are critical factors influencing language proficiency, but previous research has primarily focused on these aspects from a monolingual perspective. This study adopted a bilingual lens to simultaneously examine motivation and engagement in learning both Chinese and English among students in Chinese-English bilingual education programs at non-traditional international schools in Mainland China. Based on data from 328 fourth and fifth graders, the study found a positive correlation between Chinese and English learning motivation and engagement, with no significant differences between the two languages across most dimensions, except the higher utility in English learning. To further explore the individual differences, Latent Profile Analysis (LPA) identified four distinct profiles: low-bilingual-motivation-and-engagement, Chinese-focused learners, English-focused learners, and high-bilingual-motivation-and-engagement. Grade and gender significantly influenced bilingual learning dynamics, with students’ interest in both languages decreasing and Chinese time investment declining with grade level, while girls showed an advantage in perceiving the utility of both languages and Chinese learning time investment. However, grade and gender did not affect profile membership. This study contributes to both theory and practice by enhancing our understanding of bilingual learning processes and informing targeted educational strategies to support balanced bilingual development.

Biography

Barry Bai is an associate professor at the Department of Curriculum and the director of Centre for Language Education and Multiliteracies Research at the Chinese University of Hong Kong. He is a recipient of 2013/2014 Faculty Exemplary Teaching Award and has secured highly competitive research funds, Hong Kong Research Grants Council (RGC) Competitive Research Funding Schemes (GRF/ECS) for 2018/2019 and 2022/2023. Currently, he serves as the president of Hong Kong Association for Applied Linguistics (HAAL) and is an associate editor of European Journal of Education and Asia Pacific Journal of Education. In 2023 and 2024, he was recognized as a top 2% most cited researcher in languages and linguistics worldwide by Stanford University. Additionally, he ranked the top 3 in the category of primary school in prior 5 years by ScholarGPS in 2024.

By Professor Guangwei Hu
The Hong Kong Polytechnic University

More and more linguistic corpora are publicly available today. They are potentially curricular resources that can provide teachers with pedagogical options, offer learners access to authentic language data, facilitate student-centered and discovery-based learning, and consequently foster learner engagement and agency. The adoption of corpus data in the language classroom as teaching and learning resources is known as data-driven learning (DDL). In direct DDL, learners engage in computer-based activities, using corpora and concordancing software to carry out their learning activities, whereas in indirect DDL learners capitalize on corpus data indirectly through corpus-informed, paper-based activities and materials prepared in advance by their teachers. In this talk, I will give an overview of research on DDL and report on a study that set out to investigate and compare the effectiveness of a direct and an indirect approach to DDL in the teaching and learning of hedges. Based on the empirical results obtained (i.e., participants’ mastery of hedges and perceptions of the two DDL approaches), I will discuss pedagogical implications for EFL classrooms and make recommendations for future DDL research.

Biography

Professor Guangwei Hu is an applied linguist by training and specializes in language and literacy education. His research consists of three related strands. The first strand centers on second language literacy education in English-as-a-foreign-language contexts and has its roots in his PhD work, which was situated at the intersection of psycholinguistics, language acquisition, and second language writing. The second strand investigates biliteracy acquisition in bilingual and English-as-a-second-language (ESL) contexts, especially such biliteracy development in the early years. His third strand of inquiry focuses on academic literacy. In this work, he has drawn on various linguistic and epistemological theories to examine how knowledge-making practices and social relations may shape the rhetorical and discourse strategies used by members of academic communities to construct and communicate scientific knowledge. His research on academic literacy aims to capture the diversity and complexity of academic literacy practices and offer pedagogical insights and strategies for enhancing the disciplinary literacy of students at various levels of education.

By Dr Svetlana Chigaeva-Heddad

Assistant Professor / English Team Leader

Technological and Higher Education Institute of Hong Kong

The main aim of this session is to engage participants in discussing the impact of genAI tools on our understanding of academic literacy and the ways we have been training students to become literate in the discourses of their chosen academic disciplines. The presentation will start by situating the conversation within the latest research on academic reading, a significant yet rather invisible and under-investigated activity that our students are expected to engage in on a regular basis. The review will cover several key conceptualisations of academic reading, ranging from reading as the mental processes needed to decode and comprehend texts to reading as a socio-political activity. The second part of the presentation will focus on the recent academic reading and research support workshops organised for undergraduate students by a self-access learning centre at a private tertiary institution in Hong Kong. The workshops introduced students to a number of AI-powered academic search assistants such as SciSpace and Consensus and AI-powered reading assistants such as Humata and Unriddle. The affordances and constraints of these tools will be then analysed with respect to the key areas highlighted in the literature review.

The session is expected to be of value to academic literacy curriculum and programme developers, managers, as well as teachers across disciplines interested in integrating AI-powered reading and research tools into their lessons.

Biography 

Dr Svetlana Chigaeva-Heddad is an Assistant Professor and English Team Leader at the Technological and Higher Education Institute of Hong Kong, where she oversees the implementation of the English language curriculum and supports her team’s professional development. Her research interests include AI-mediated academic reading, gen AI literacy development, language curriculum design, and language teacher agency.

By Dr Ricky Lam

Portfolio assessment has been widely adopted in general and English as a second language education for nearly four decades. Despite its significance and popularity, practitioners and scholars alike remain dubious about its usefulness, practicality, and reliability to be applied as an alternative in assessments. To dispel stakeholders’ ambiguities and scepticisms, this paper critically surveys four key domains of portfolio assessment, which could equip frontline practitioners and assessment researchers with additional instructional repertoires for upgrading instruction and cutting-edge research agendas for conducting portfolio-related studies, respectively. These four domains entail: (1) affordances and challenges when print-based portfolio programs transition into electronic ones within multiple settings; (2) applications of tried-and-tested strategies to facilitate portfolio integration into English curricula; (3) systematic reviews that unpack future research agendas to make original contributions to portfolio assessment scholarship; and (4) a cautionary note that alerts aspiring teachers and scholars when (e-)Portfolio assessment is utilized for teaching and research in a wider English as a second language context. The key takeaways from the paper are twofold, providing audience members with (i) a renewed perspective of how they can effectively conduct and research portfolio assessment in the post-pandemic era, and (ii) up-to-date and practical resources, for instance, blurbs on common e-Portfolio tools, portfolio application websites, selected bibliography (journal articles and monographs), and online tutorials about e-Portfolio set-up. 

Keywords: portfolio assessment; alternative assessment; print and electronic portfolios; second language learning 

Biography

Dr Ricky Lam is Associate Professor in the Department of Education Studies at Hong Kong Baptist University, Hong Kong, China. He is Associate Editor of Frontiers in Education and has served in several international editorial boards. His research interests include L2 writing assessment, digital portfolios, and language assessment literacy. His publications have appeared in Language Assessment Quarterly, System, RELC Journal, Assessing Writing, Language Testing, TESOL Quarterly, and other SSCI- and SCOPUS-indexed journals. Ricky is the sole author of Integrating e-Portfolios into L2 Classrooms: Education for future (Multilingual Matters, 2024). 

By Professor Alessandro G. Benati

Processing instruction is a type of pedagogical intervention which is effective in assisting L2 learners in the correct processing and interpretation of linguistic features in the input. The primary effects of processing instruction have been measured in several studies investigating different linguistic features, languages, processing problems across different populations, and demographics such as age, motivation, aptitude, working memory capacity, and first language of L2 learners. The main results from these studies have clearly and consistently demonstrated that L2 learners exposed to structured input activities (the main component of processing instruction) develop competence in processing input accurately, in terms of interpretation at the sentence and discourse levels, and in producing language containing the target feature both orally and in writing.  

This talk explores a new line of research into possible transfer-of primary online effects of structured input. Secondary effects are measured when two linguistic items under focus present L2 learners with a similar processing problem.  For example, word final morphology such as present tense –s and past tense –ed in English present L2 learners will be: Will learners who receive training on one type of processing strategy for one specific form appropriately transfer the use of that strategy to other forms without further instruction? Up to now, empirical research measuring secondary effects have used pencil-and-paper offline tests. However, offline tests might not provide the most appropriate measure of L2 learners’ internal language processing. Using online measurements such as self-paced reading speed or eye-tracking might provide a more valid metric to capture L2 learners’ internal in-depth processing and underlying implicit knowledge. 

About the speaker

Alessandro G. Benati is Professor and Head of the School of Education at University College Dublin in Ireland, and honorary/visiting professor at the University of York, Hong Kong, Macquarie, and Anaheim. His work has focused on language processing. Within this area, three general questions have guided his research: (i) what kinds of linguistic cues do second language learners use to understand sentences during language comprehension? (ii) how do experimental methodologies in language processing impact study outcomes and theory development? (iii) how can insights on second language processing improve instructed language learning? More recently, he has applied eye-tracking methodology to issues of processing language in instructed second language acquisition contexts. His work has appeared in Second Language Research, Language Teaching Research, System, Language Awareness, Ampersand, IRAL, ISLA, Language Teaching, Applied Psycholinguistics.  

By Dr Sean McMinn

In a world marked by technological advancement and societal transformation, practitioners of language education are experiencing challenges to how, what, and why we teach and assess languages. For example, the rapid advancement of artificial intelligence, augmented and virtual reality, not only have the potential to disrupt how we teach languages, but also how we might facilitate self-access learning. As we navigate these changes, a critical consideration arises: Should language education be value-based, competency-based, proficiency-based, or a harmonious combination of these? This fundamental question guides the pursuit of pedagogical innovation, influencing the design and delivery of educational experiences. And with these new innovations comes the need for educators to think about new processes and strategies. This talk will explore the essence of innovation in language education, transcending traditional boundaries to consider new approaches to teaching and learning, while being mindful of how technologies are also influencing self-regulatory learning strategies. A particular focus is placed on the emergence of Generative AI, a groundbreaking development that offers opportunities for creativity, personalisation, and engagement within the learning process, but also comes with ethical and equity implications. Cyber-Physical Learning (CPL) is also highlighted as a holistic design approach that combines pedagogy and technology in disruptive yet efficient ways, seamlessly integrating cyber and physical experiences.

About the speaker

Dr Sean McMinn is the Director of the Center for Education Innovation at The Hong Kong University of Science and Technology (HKUST). Prior to this role, he was an Associate Professor and the Director of the English Language Centre at The Hong Kong Polytechnic University from 2020-2022. He was an Associate Professor of Language Education and an Associate Director in the Center for Language Education at HKUST and the Co-Academic Director for the Master of Arts in International Language Education (MA-ILE) program. He won the 2016 SHSS Teaching Excellence Award and the 2007 Teaching Innovation Award for his work with podcasts and education at HKUST. He has also taken a visible role as an early-adopter of new technologies for teaching and learning purposes. This includes leading the various centers in developing AI in education initiatives, blended learning, and fully-online mode for teaching and learning. 

By Dr Qing Ma (Angel)

While the potential of corpus linguistics to enhance language teaching and learning is significant, its practical application remains largely unexplored within the professional teaching community. This workshop aims to bridge this gap by introducing participants to a selection of freely available online corpora, designed to familiarize language teachers with these valuable resources. Furthermore, participants will learn about an innovative language pedagogy – Corpus-Based Language Pedagogy (CBLP). Attendees will acquire fundamental knowledge about CBLP and learn how to utilize various corpus tools for both teaching and learning purposes. Ultimately, the goal of this workshop is to empower language teachers/educators to fully harness the potential of CBLP, resulting in more effective classroom instruction and fostering a culture of ongoing self-learning, integral to continuous professional development.

About the speaker

Dr Qing Ma (Angel) is an associate professor at the Department of Linguistics and Modern Language Studies, The Education University of Hong Kong. Her main research interests include second language vocabulary acquisition, corpus linguistics, corpus-based language pedagogy (CBLP), computer assisted language learning (CALL) and mobile assisted language learning (MALL).

She has developed the Parallel EAP Corpora (http://corpus.eduhk.hk/eap/)for research purposes and the Corpus-Aided Platform for Language Teachers (CAP) (http://corpus.eduhk.hk/cap/) for teaching purposes. She is an experimenter of corpus technology in teaching, and passionate English teacher trainer. Recently, she has established a new corpus-based language pedagogy by theorising and empirically testing it that has positively impacted many pre-service and in-service English teachers. She is the associate editor for three journals:  Computer Assisted Language Learning (CALL); International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT); The Journal of China Association for Computer-Assisted Language Learning (ChinaCALL).

by Dr Benjamin Luke Moorhouse

Transitioning from a professional career to one in academia can be daunting. This is due in part to the high yet sometimes unclear expectations, steep learning curve and ‘publish or perish’ culture of modern higher education. However, academics with previous professional careers can enrich higher education. They bring real world experiences and knowledge that can inform teaching and research. In this talk, I share my experience transitioning from schoolteacher to university lecturer and then to assistant professor (my current role). Primarily, I will focus on how I gained the knowledge and skills needed to engage and contribute to the scholarly research community leading to being named a top 2% most-cited scholar in 2022 and 2023 by Stanford University. Through sharing my own learning journey, I hope others will gain valuable insights on how they can contribute to academia.

Date: 31 January 2024 (Wednesday)
Time: 6:30 to 7:30 PM

Venue: The Hong Kong Polytechnic University (Department of English and Communication)
Room: AG434