Invited Speakers

 
Prof. Nobuyo Fukaya

Prof. Nobuyo Fukaya

Affiliation: Niigata Agro-Food University, Japan

Bio

Dr. Nobuyo Fukaya is a professor at Niigata Agro-Food University in Niigata, Japan. She previously served as a lecturer at Shibaura Institute of Technology in Tokyo. She earned her Ph.D. from Tsuda University, Japan.

Her research interests include first and second language acquisition, information structure, and discourse-pragmatic development in English. She has published studies on language acquisition using corpus-based approaches, including research on English wh-questions and dative alternation in both L1 English-speaking children and Japanese EFL learners. She is also exploring linguistic approaches to food perception and taste evaluation, examining how people describe and communicate their experiences of food.

Title

From Pronoun Frequency to Discourse Reference: Evidence from Japanese and Chinese EFL Learners

ABSTRACT

Given-before-New principle, which states that given information tends to precede new information, is observed in the English dative alternation. However, based on a corpus-based analysis, Fukaya (2026) found that Japanese English as a foreign language (EFL) students tend to follow this principle in double object constructions but tend to violate it in prepositional-dative constructions. By contrast, Zhang and Kang (2023), who investigated Chinese EFL learners’ use of the English dative alternation, established that the principle played a more prominent role in prepositional-dative constructions. This raises the question of whether Japanese and Chinese EFL learners differ in their use of personal pronouns in constructions beyond the dative alternation.

This study investigates third person-plural pronouns they and them used by Japanese and Chinese EFL learners in the International Corpus Network of Asian Learners of English and compares the frequency with that of native English speakers. A clear difference was observed between the two learner groups in the developmental patterns of them across CEFR proficiency levels. Japanese EFL learners used them much less frequently than native English speakers at all proficiency levels, whereas Chinese EFL learners displayed a gradual increase in frequency, with their use exceeding that of native English speakers at B2+. Why was such a difference observed? Accordingly, the study examined how many times the same discourse referent was referred to when them was used. The discourse-level analysis revealed that Chinese learners used them at later positions in referential chains more frequently at higher proficiency levels, whereas Japanese learners’ use remained concentrated on first and second mentions. These findings highlight the importance of addressing L2 pronoun use at both the sentence and discourse levels in English language teaching

Prof. Claudio Bendazzoli

Affiliation: University of Verona, Italy

Bio

Claudio Bendazzoli is Associate Professor of English Language, Translation and Linguistics in the Department of Foreign Languages and Literatures at the University of Verona. Between 2012 and 2024 he worked as Assistant Professor at the University of Turin and prior to that (2004-2011) he worked in the Department of Interpreting and Translation at the University of Bologna at Forlì. From that department he obtained an MA in Conference Interpreting (Italian, English, Spanish) and a PhD in Languages, Cultures and Intercultural Communication with a project on simultaneous interpreting corpora. His main research interests concern interpreter-mediated communication in different settings and with different modes, Corpus Linguistics, English lingua franca and English medium instruction. He is also affiliated with the University of Las Palmas de Gran Canaria in Spain and Dalian University of Foreign Languages in China. He is a practicing conference interpreter (A: Italian; B; English, Spanish) with extensive experience in the hairdressing industry.

TITLE

AI-generated feedback in the training of consecutive interpreters

ABSTRACT

The impact of AI is gaining momentum in education across all fields. Interpreter training is no exception, as interpreting educators and students alike are increasingly taking advantage of AI-powered tools for preparing teaching materials (e.g. speeches with specific characteristics), transcribing spoken output (both source and target speeches), generating glossaries and managing support materials (e.g. summarizing documents). In addition, according to a survey conducted in a Chinese university, interpreter trainees also use AI for getting feedback during autonomous practice sessions. This comes with clear advantages for students, as they can have access to assessment beyond the time and space constraints of traditional classroom environments. Little is known, however, on the kind of feedback generated by AI companions, its reliability and real pedagogical value. This paper aims to explore these issues and presents the results of two experiments in which interpreting students were asked to perform a consecutive interpretation and obtain AI-generated feedback by using 1) their preferred tool and prompt as they would normally do during self-practice; 2) a specific tool (i.e. Doubao) with a prompt provided by the instructor. The feedback obtained from the first experiment (n=18) was analyzed in terms of the kind of information included in each feedback report and their presentation. The results show similarities and differences in the degree of details covered, use of visual content (i.e. emoticons and icons) and pedagogical recommendations. The same results were confirmed even in the second experiment (n=24) despite the use of the same tool and prompt. However, in this experiment the recordings of students’ output were also examined to double check the identification of errors and corrective recommendations signaled in the AI-generated feedback. What emerged from this further level of analysis is that corrective feedback was sometimes based on the wrong identification of a mistake, which was not actually present in the students’ output but was (mis)recognized due to inaccuracies in automatic speech recognition. In conclusion, AI-generated feedback in interpreter education has unquestionable advantages though trainees should learn to receive it with caution and always with the supervision of their educators.

Prof.Claudio Bendazzoli

Dr. Regan Lipes

Affiliation: MacEwan University, Canada

Bio

Dr. Regan Lipes is an Assistant Professor in the Department of English, at MacEwan University in Edmonton, Canada. She specializes in Comparative Literature with a focus on the diverse Jewish world. Dr. Lipes holds a BA inf Russian Language and Literature, an MA in Slavic Languages and Literatures, and a PhD in Comparative Literature. She was the 2019 Faculty Fellowship at the Auschwitz Jewish Centre in Oswiecim, Poland in partnership with the Museum of Jewish Heritage: A Living Memorial to the Holocaust in New York. Dr. Lipes was the 2024 recipient of the Sessional Excellence Teaching Award at MacEwan University. She is delighted to return to Tokyo, where she spent much of her childhood. Her grandfather was the founder and the original editor and chief of the Tokyo Weekender, an English-language expatriate newspaper celebrated throughout Japan. Dr. Lipes has followed this path as a side hobby and is a Local Journalism Initiative Reporter with the Alberta Jewish News. 

TITLE

Japan's Conception of Jewishness: A Unique Cultural Environment Where Antisemitism Never Flourished

ABSTRACT

The global attitudes towards Jews and Israel have become so hateful and violent, that it is necessary to consider how antisemitism takes root and what social and cultural conditions allow it to flourish. Japan presents a distinctive case for a comparative study of antisemitism and the cultural construction of Jewish identity or Jewish-ness. Unlike many European societies, where centuries of religious antagonism, social ostracization, and political scapegoating established deeply rooted traditions of anti-Jewish prejudice, Japan developed its understanding of Jews largely in the context of modernization, international engagement, and the selective reception of Western ideas. The limited historical presence of Jewish communities in Japan after the 19th century, together with the absence of an indigenous tradition of Christian anti-Judaism, created a cultural environment in which perceptions of Jewish-ness evolved along a different historical trajectory. My work examines the origins, transformations, and distinctive characteristics of Japanese conceptions of Jews, asking why antisemitism, despite periodically appearing in Japanese intellectual and political discourse, did not develop into a sustained, hate fueled social movement comparable to those found across Europe, and North America.

My analysis first considers the emergence of Japanese awareness of Jews during the Meiji period (1868–1912), when encounters with Western literature, religious thought, and political discourse introduced competing images of Jewish identity. Jews came to be understood not primarily through direct social interaction, but through representations that associated them with commercial acumen, financial influence, and modernity. These imported initial images provided the foundations for both admiration and suspicion, illustrating how philosemitism and antisemitism could coexist within the same cultural framework. Even while allied with Nazi Germany, Japanese officials consistently rejected the antisemitic attitudes of their compatriots. While orchestrating their own murderous campaigns throughout Asia, Japan’s society never adopted mainstream antisemitic attitudes.

This examination explores the postwar and contemporary periods, when representations of Jews increasingly became entangled with popular publishing, economic success, international relations, and narratives of national self-understanding. Japanese interest in Jewish history and culture has frequently been accompanied by idealized or instrumentalized images of Jewish intelligence, solidarity, and economic achievement. Such representations may appear positive, yet they can reproduce essentialist assumptions and provide fertile ground for conspiracy theories.

Ultimately, this paper argues that Japan's experience invites a more nuanced understanding of the relationship between cultural distance, historical memory, and antisemitism. Rather than treating Japan as an exceptional society entirely untouched by anti-Jewish prejudice, it investigates the conditions under which antisemitic ideas remained comparatively marginal while philosemitic fascination and stereotypical representations persisted. Japan remains a safe and welcoming place for world Jewry and Israelis to travel, and in an increasingly hostile divisive world, this is significant. By examining the Japanese conception of Jewish-ness as a historically contingent cultural construction, my work contributes to broader debates about the diverse manifestations of antisemitism globally, to consider what is unique about Japanese society that has managed to keep the malignancy of antisemitism from taking hold. 

Dr. Hungche Chen

Affiliation: Chang Gung University of Science and Technology, Taiwan

Bio

Hungche Chen is an assistant professor in the Center for General Education at Chang Gung University of Science and Technology, Taiwan, where he also serves as the Director of the Honors Program. He received his Doctor of Education in Bilingual Education from Texas A&M University and previously taught in Japan and the United States. He was selected to participate in the Fulbright ESP Professional Development Program at the University of Maryland.

His research focuses on AI-enhanced language education and educational technology. He has published in international journals and actively serves as a reviewer and editor for academic conferences and journals.

TITLE

From AI Users to AI Creators: Designing Language Learning for Interaction, Agency, and Creativity

ABSTRACT

As artificial intelligence becomes increasingly capable of generating language, ideas, and feedback, its educational value depends not simply on what the technology can do, but on what learners are designed to do with it. Drawing on classroom-based research in AI-supported English learning, this talk explores a shift from positioning students primarily as users of AI-generated content toward engaging them as active participants who interact, exercise agency, and ultimately create with AI. Three principles are proposed for AI-enhanced language learning: interaction, which preserves meaningful communication; agency, encouraging learners to evaluate and adapt AI-generated content; and creativity, which moves learners toward designing and creating with AI. Together, these principles support a Learner-as-Creator perspective in which AI enhances rather than replaces learner thinking.

Dr. Hungche Chen
Dr. Siu-Lun Lee

Dr. Siu-Lun Lee

Affiliation: The Chinese University of Hong Kong, Hong Kong, China

Bio

Siu-lun Lee is Principal Lecturer at the Yale-China Chinese Language Academy, the Centre for China Studies at the Chinese University of Hong Kong. As an experienced educator and administrator of Chinese language programmes at the Chinese University of Hong Kong, he has played a key role in developing and managing language curricula. Siu-lun Lee is a trained linguist with extensive involvement in linguistic and pedagogical research. His academic interests include applied linguistics, Cantonese studies, Chinese linguistics, multilingualism, sociolinguistics, and language teaching pedagogy. He has co-authored several scholarly articles, including A socio-historical approach to multilingualism in Hong Kong (2020), Linguistic landscapes and language contact (2020), Societal multilingualism in Hong Kong (2020), and Researching Hong Kong’s multilingual landscape (2025).

His research on language teaching pedagogy has been published in indexed journals. His contributions include studies such as Revisiting role-playing activities in foreign language teaching and learning (2015), E-Learning readiness in language learning (2016), Modular approaches in e-learning design (2018), Aligning peer tutoring systems in language education (2024), and Positioning pop songs in the Chinese language education curriculum (2026). His contributions to Cantonese education include the publication of Modern Cantonese (Volumes 1–3, Routledge, 2023) and The Learning and Teaching of Cantonese as a Second Language (Routledge, 2023).

TITLE

Language Education in the AI Era: Opportunities and Future Directions

ABSTRACT

Artificial intelligence (AI) is rapidly transforming language education by reshaping how languages are taught, learned, and assessed. From AI-powered feedback and adaptive learning systems to large language models that support communication, language educators are encountering new possibilities and challenges. This presentation examines the impact of AI on language pedagogy, learner development, assessment practices, and teacher roles. Drawing on recent research, it highlights the potential of AI to enhance learning while emphasising the importance of AI literacy, critical thinking, ethical awareness, and human-centred pedagogy. The presentation concludes by proposing strategies for integrating AI responsibly into language education to prepare learners for an increasingly digital and multilingual world.

Assoc. Prof. Hongguo Xue

Affiliation: Northwestern Polytechnical University, China

Bio

Hongguo Xue is an Associate Professor of English in the School of Foreign Studies at Northwestern Polytechnical University, China. Her main research interests concern applied linguistics and translation. She has published more than 20 papers, authored three textbooks, served as principal investigator for three provincial-level research projects, received five Excellent Teaching Achievement Awards, and obtained the Most Satisfactory Teacher award twice.

TITLE

Collaborative Feedback from GenAI and Human Instructors: Effects on Graduate Students’ Academic Emotions and Feedback Engagement in English Writing

ABSTRACT

This study aims to reveal the impact of collaborative feedback from generative artificial intelligence (GenAI) and teacher on the academic emotions and feedback engagement of non-English major graduate students in English writing. Employing a mixed research methodology, 96 students were selected as participants and provided with both GenAI and teacher feedback for their four argumentative essay writing assignments. Through questionnaire survey data on academic emotions and feedback engagement before and after the four rounds of feedback, as well as semi-structured interviews with four students, the results indicated that collaborative feedback between GenAI and teachers significantly enhanced students’ enjoyment, accompanied by increased deep engagement and higher-order cognitive processing. Students’ anxiety slightly increased, and there was a significant positive impact on emotional, cognitive, and behavioral feedback engagement (P<0.001). In addition, cognitive engagement was most strongly correlated with enjoyment (r=0.722), while emotional engagement and behavioral engagement showed the highest degree of synergy (r=0.770). Qualitative analysis also revealed that under collaborative feedback, GenAI lowered the threshold for revision with its immediate and non-judgmental characteristics, while teachers’ feedback compensated for the deficiencies in GenAI feedback with their professional and subject-adaptive guidance. This study provides insights for optimizing collaborative feedback between GenAI and teachers, thereby enhancing the effectiveness of writing feedback, students’ emotions, and their feedback engagement.

Assoc. Prof.Hongguo Xue
Asst. Prof. Hambalee Jehma

Asst. Prof. Hambalee Jehma

Affiliation: Prince of Songkla University, Thailand

Bio

Dr. Hambalee Jehma is a distinguished academic and administrator at Prince of Songkla University International College, Hatyai Campus, Thailand, where she currently serves as Assistant Professor and Head of General Education (University International Program). He holds a Ph.D. in Policy and Leadership in Higher Education from Chulalongkorn University, Thailand. He is also a UKPSF (Advance HE) Senior Fellow. Dr. Jehma's research interests encompass educational leadership in ELT, policy and management in higher education, media and technology in teaching and learning, and autonomous learning, with particular expertise in Second Life, virtual reality, mobile-assisted language learning, and AI integration in education. His administrative leadership is extensive, having served as Assistant Dean for Academic Service and Special Affairs, Assistant Dean for Research Innovation and Academic Service, and Assistant Director for Student Affairs, where he has managed international collaborations with universities across the UK, Germany, South Korea, Taiwan, China, Malaysia, Brunei, Bahrain, and Indonesia.

A prolific scholar, Dr. Jehma has published extensively in leading international journals, with recent work appearing in the International Journal of Interdisciplinary Educational Studies, REFLECTIONS, and the LEARN Journal. He has been honored with multiple awards, including the PSU Model Lecturer Award, the 100-Year Anniversary Chulalongkorn University Scholar Award, and the Distinguished Scholar Award from SEAMEO RELC, Singapore. His innovative contributions to hybrid learning, virtual feedback, and English language pedagogy continue to shape contemporary educational practices.

TITLE

From Acceptance to Agency: Developing and Testing the AIALL–Agency Integration Model among University Students in ASEAN

ABSTRACT

This study develops and tests the AIALL–Agency Integration Model (AAIM), synthesising the OECD/European Commission AI Literacy Framework with Vygotsky’s sociocultural theory and self-determination theory. It explains how university students in three ASEAN countries develop agentic, critical, and ethical uses of AI-assisted language learning (AIALL).

An explanatory sequential mixed-methods design was used. The quantitative phase surveyed 600 university students in Indonesia, Thailand, and Vietnam using validated scales of AI literacy, autonomous motivation, and self-regulated AIALL use; data were analysed with structural equation modelling and multi-group invariance testing. The qualitative phase involved semi-structured interviews with 30 purposively selected students, analysed through reflexive thematic analysis.

AI literacy significantly predicted autonomous motivation (β = .44, p < .001) and self-regulated AIALL use (β = .52, p < .001). The four AI literacy domains formed a hierarchical structure: Engage → Create → Manage → Shape. Vietnamese students scored higher than Indonesian students on Manage (β = .15, p < .001) and Shape (β = .12, p < .001). Qualitative analysis revealed three themes: efficiency and integrity, scaffolding and dependency, and cultural mediation.

The AAIM offers a portable, culturally grounded alternative to technology acceptance models, reframing AIALL as developmental, motivational, and ethical. It provides a diagnostic framework for educators and policymakers designing AIALL environments that foster learner agency rather than dependency and contributes the first comparative mixed-methods evidence on AIALL and AI literacy among ASEAN university students. Limitations include the focus on three ASEAN countries and language-learning contexts; generalisability requires further validation.

Prof. Seongeun Cho

Affiliation: Sungkyul University, South Korea

Bio

Dr. Seongeun Cho is a Professor in the Department of English Language and Literature at Sungkyul University, Korea. She holds a Ph.D. in Translation Studies from Dongguk University and a master’s degree in Interpreting and Translation. Her research interests include AI-assisted translation, machine translation post-editing (MTPE), translation pedagogy, English education, and technology-enhanced language learning. She has extensive experience in English-Korean translation and English education and currently serves as President of the Korean MTPE Association. She is also actively engaged in developing educational programs that integrate generative AI, translation, and English language learning, with a particular focus on practical applications in higher education and professional training.

TITLE

TBA

ABSTRACT

TBA

Prof.Seongeun Cho
Dr. Huameng Yang

Dr. Huameng Yang

Affiliation: Tsinghua University, China

Bio

Dr. Huameng Yang is an Assistant Research Fellow at the School of Humanities, Tsinghua University, China. She earned her Ph.D. in linguistics from Tsinghua University and an M.Phil. in education from the University of Cambridge. Her research interests include language education, sociolinguistics, discourse analysis, and digital humanities. She is also a member of the Intelligent Laboratory for Chinese Traditional Culture at Tsinghua University, where she is currently involved in the construction of a thematic digital humanities database on Dream of the Red Chamber, with a particular focus on the analysis of emotional expression in classical Chinese literature.

TITLE

Interpreting Language and Narrative across Texts: Digital Methods in Media and Literary Studies

ABSTRACT

Recent advances in digital methods have opened new possibilities for analyzing large-scale textual data and exploring patterns of language, narrative, and meaning. This presentation examines how digital approaches can support textual analysis across different genres, drawing on two projects.

The first project investigates how global development narratives are constructed across English-language news media in four different countries. Combining topic modeling with intertextuality analysis, the study identifies cross-national variations in thematic organization, textual references, and narrative framing. The findings demonstrate how combining digital analysis and qualitative interpretation can reveal recurring patterns and distinctive narrative configurations in large-scale media corpora.

The second project focuses on the development of a specialized database for Dream of the Red Chamber. Designed to facilitate research beyond conventional textual browsing and keyword searches, the database integrates image, text, and scholarly resources. It employs large language models in collaboration with human annotation to structure information about characters, locations, events, relationships, and other textual elements, enabling systematic retrieval, analysis, and visualization.

Together, these projects illustrate complementary approaches to studying different types of texts, from contemporary media narratives to classical Chinese literature. They highlight the potential of digital methods to identify textual patterns, organize complex textual information, and support contextualized interpretation while accommodating the distinctive analytical needs of different genres.