Plenary Speakers

Generative AI in Language Education: Evolving Roles of Teachers and Pedagogical Innovations

Abstract

This talk explores the potential of generative AI in language education, focusing on the evolving roles and responsibilities of language teachers. It covers several key research areas. First, the emphasis is on developing prompt engineering strategies to create effective language teaching and learning materials. I will showcase prompts designed for content generation that cater to individual learners’ needs, interests, and proficiency levels. Second, I examine the pedagogical efficacy of AI-driven language instruction, including the evolving role of teachers and the best practices for utilizing AI as a co-teaching tool. This section highlights the importance of teachers’ prompt-writing skills and AI literacy, with practical use cases from my teaching experiences. Third, I introduce my ongoing project, “Fluid Language Pedagogy," which leverages AI to foster a more personalized, flexible, and collaborative language learning experience. Finally, I address critical issues related to generative AI in language instruction, posing questions such as: What is the role of language teachers in the AI age? How should the language curriculum adapt to incorporate generative AI? What ethical and pedagogical considerations accompany AI in language learning? I will offer anecdotal responses to these questions. I conclude that we need to cultivate “perspective shifts” that enable generative AI to optimize our future language education. The imperative now is not to resist but to adapt.

Bio

Dr Takako Aikawa is a Senior Lecturer in Japanese at Massachusetts Institute of Technology, Global Languages, Cambridge, USA. Before joining MIT’s Global Languages in 2013, she honed her expertise in machine translation and natural language processing at Microsoft Research. At MIT, she is responsible for directing the Japanese language program while utilizing technology for language learning.

Exploring the Realities of AI in the Language Classroom

Abstract

The emergence of generative AI tools has sparked widespread excitement about their potential in education, particularly in language teaching. However, beyond the initial hype lies the need for a critical examination of how AI is reshaping the language classroom. This presentation explores the tangible impacts of integrating AI into language learning, focusing on three key areas: language development, learner reliance, and ethical considerations. While AI tools offer opportunities for personalised feedback, adaptive learning, and enhanced engagement, their actual contributions to language acquisition remain uneven. Do they truly foster linguistic competence, or do they inadvertently reinforce surface-level fluency? Additionally, the increasing reliance on AI raises questions about learner autonomy and critical thinking. Are students developing the skills to evaluate and refine AI-generated content, or becoming overly dependent on these tools? The session also explores the ethical dimensions of AI in education, including data privacy, bias, and the implications of outsourcing linguistic creativity to machines. Drawing on research and practical examples, the presentation offers a balanced perspective on the promises and pitfalls of AI in the language classroom. By moving beyond the rhetoric of innovation, this presentation challenges educators to critically assess the role of AI, ensuring it supports meaningful learning outcomes while addressing the ethical and pedagogical complexities it introduces.

Bio

Glenn Stockwell (PhD, University of Queensland) is Professor of Applied Linguistics at the Graduate School of International Culture and Communication Studies, Waseda University. He is author of Mobile Assisted Language Learning: Concepts, Contexts and Challenges (Cambridge University Press, 2022) and editor of Smart CALL: Personalization, Contextualization, & Socialization (Castledown) and Computer Assisted Language Learning: Diversity in Research and Practice (Cambridge University Press, 2012). He is editor-in-chief of Computer Assisted Language Learning and the Australian Journal of Applied Linguistics. His current research interests include the impact of technology on teaching and learning, mobile-assisted language learning, artificial intelligence in language education, teacher and learner training with technology, and the development of learner autonomy.

Integration of AI technology in Chinese as a Second Language: Teacher Training, Student Learning, and Administrative Management

Abstract

At National Taiwan Normal University, three key institutes—the Department of Chinese as a Second Language, the Mandarin Training Center, and the Chinese Language and Technology Center—are at the forefront of integrating technology into the teaching and learning of Chinese as a second language. Given the vast amount of data involved and the large number of students and educators across these units, considerable efforts have been made to harness technology in order to enhance the effectiveness and efficiency of Chinese language education. 

This presentation will examine the integration of technology and artificial intelligence (AI) in three key areas: teacher training, student learning, and administrative management. In teacher training, workshops have been conducted to equip educators with the necessary knowledge and skills to effectively incorporate technology and AI into their teaching practices. These efforts aim to facilitate the preparation and delivery of lessons with greater efficiency and precision. In terms of student learning, AI technologies have been utilized to create virtual learning environments and to generate interactive images and videos, which have enhanced engagement and motivation among learners. Additionally, the development of digital learning platforms has afforded students greater flexibility, enabling them to tailor their learning experiences to their own pace and needs. From an administrative perspective, AI has also been employed to address the challenges posed by a diverse, multicultural, and multilingual student population. By streamlining processes for gathering student feedback and providing tailored services, AI has contributed to more efficient administrative management and improved support for the student body.

Bio

Dr Chao-Mei Tu received her Ph.D. from Purdue University and is currently a faculty member in the Department of Chinese as a Second Language at National Taiwan Normal University. She teaches both undergraduate and graduate courses, with a focus on Taiwanese film and literature, as well as Chinese for specific purposes. Known for her innovative teaching methods, Dr Tu has received accolades for her dedication to student learning and curriculum design. She has spearheaded initiatives to integrate technology and interdisciplinary approaches into her courses, enriching the learning experience for students from diverse backgrounds. 

Beyond her academic pursuits, Dr Tu serves as the Associate Director of the Mandarin Training Center. In this capacity, she plays a key role in fostering the professional growth of Chinese language educators, overseeing administrative operations, and contributing to the development of innovative courses and teaching materials. Her leadership and vision have been instrumental in advancing the center’s reputation as a hub for excellence in Chinese language education.

GenAI policies and practices: the need for staff and student education

Abstract

Many universities have faced challenges in establishing formal policies and clear guidelines for the use of generative AI (GenAI) tools, creating confusion among staff and students. Policies at leading universities worldwide have been shaped by a strong emphasis on originality in student work and concerns about academic misconduct (Luo, 2024), overlooking the substantial benefits that GenAI can bring to academic work. As a result of restrictive policies and insufficient professional guidance, academic staff often discourage the use of GenAI tools (Wise et al., 2024). This positions students, who are already widely using these tools, into a situation of uncertainty and illegitimacy. It has therefore been argued that policies cannot be based on traditional views of academic integrity but must be informed by knowledge of staff and students’ perceptions and actual use of GenAI (Ou et al., 2024).

To address the existing disconnect between policies and practices as well the lack of adequate guidance, we designed workshops on the ethical and effective use of GenAI tools for lecturers and students in a large university department. The design was guided by a survey and focus group interviews that elicited participants’ perceptions and practices. The workshops were concerned with the evaluation of existing policies, information on the affordances of a range of AI tools for the processes involved in academic writing, and ethical boundaries. The staff workshops aimed at reducing lecturers’ resistance to GenAI tools and equipping them to support students in their use. The student workshops sought to address digital inequalities and create a more level academic playing field.

In this talk, I present some workshop content and findings from the workshop evaluations conducted through questionnaires and interviews. The findings confirm that prevalent university policies, shaped by concerns over potential misconduct, are detrimental to the productive use of GenAI in academic settings as they discourage lecturers from engaging with the technology and are met with resistance from students. They also highlight the need for systematic guidance for both staff and students.

Luo, J. (2024). A critical review of GenAI policies in higher education assessment: a call to reconsider the “originality” of students’ work. Assessment & Evaluation in Higher Education, 49(5), 651–664. https://doi.org/10.1080/02602938.2024.2309963.

Ou, A. W., Stöhr, C. & Malmström, H. (2024). Academic communication with AI-powered language tools in higher education: From a post-humanist perspective. System, 121, 103225. https://doi.org/10.1016/j.system.2024.103225.

Wise, B., Emerson, L., Van Luyn, A., Dyson, B., Bjork, C. & Thomas, S. E. (2024). A scholarly dialogue: writing scholarship, authorship, academic integrity and the challenges of AI. Higher Education Research & Development, 43(3), 578–590. https://doi.org/10.1080/07294360.2023.2280195.

Bio

Ursula Wingate is Professor of Language Education in the School of Education, Communication and Society at King’s College London. Her research interests include theoretical and pedagogical models underpinning the development of students’ academic literacy. Her recent research explores policies, perceptions and practices related to the use of Generative AI.