Call for Papers for Special Issue on EMI in an Era of Generative AI

Title:
English-Medium Instruction in an Era of Generative AI: Rethinking Pedagogy, Assessment, and Teacher Development

Guest Editors:

• Jack Pun
  Associate Professor, Department of English
  The Chinese University of Hong Kong, Hong Kong SAR, China
  Email: jackpun@cuhk.edu.hk 
• Rui (Eric) Yuan
  Associate Professor, Faculty of Education
  University of Macau, Macau SAR, China
  Email: ericruiyuan@um.edu.mo
• Kailun Wang
  Lecturer, National Research Centre for Foreign Language Education
  Beijing Foreign Studies University, Beijing, China
  Email: wangkailun@bfsu.edu.cn

Jack Pun is Associate Professor in the Department of English at the Chinese University of Hong Kong, Hong Kong SAR, China. He completed his DPhil at the University of Oxford, which explored the teaching and learning process in EMI science classrooms, with a special focus on classroom interactions, use of codeswitching, and teachers’ and students’ views of EMI. His research interests lie in EMI and health communication. His research has been published in journals such as ELT Journal, Language Teaching, RELC Journal, Journal of English for Academic Purposes and International Journal of Bilingual Education and Bilingualism. He is associate editor of Journal of Research in Science & Technological Education, and published two books in EMI: Teaching and Learning in English Medium Instruction: An Introduction (with Jack C. Richards) and Research Methods in English Medium Instruction (with Samantha Curle) by Routledge.

Rui (Eric) Yuan is an Associate Professor at the Faculty of Education of the University of Macau. His research focuses on English medium instruction in higher education and language teacher education. He has published more than 100 research works, including journal articles and book chapters, in international journals such as TESOL Quarterly, Language Teaching Research, and Teaching and Teacher Education. He currently serves as Associate Editor of TESOL Journal and is an editorial board member of Linguistics and Education.

Kailun Wang is a Lecturer in the National Research Centre for Foreign Language Education, Beijing Foreign Studies University. His research interests include language teacher development and English-medium instruction in higher education. He has published articles on these topics in international journals including Language Teaching, Journal of English for Academic Purposes, TESOL Quarterly, and RELC Journal. He also serves as the anonymous reviewer for a number of international journals.

1. Overview

This Special Issue (SI) focuses on the transformative role of Generative Artificial Intelligence (GenAI) in reshaping pedagogy, assessment, feedback, and professional practices within English Medium Instruction (EMI) contexts. EMI, where academic subjects are taught in English to speakers of other languages, has become a widespread model of internationalised education, particularly in higher education institutions across Asia, Europe, and the Middle East (Macaro et al., 2018; Hu & Lei, 2014). While EMI programs traditionally prioritize content instruction in specific disciplines, a language focus is deemed important for students to foster their discipline-specific linguistic competence, which can reciprocally contribute to their content learning (Yuan et al., 2025). This dual demand on content and language development thus introduces unique linguistic, cognitive, and pedagogical challenges not typically present in standard language instruction settings (Pun & Jin, 2021; Pun & Thomas, 2020).

The emergence of GenAI tools—such as ChatGPT, Gemini, and Claude—offers both unprecedented affordances and complex disruptions to this content learning model. These technologies can generate high-level academic texts, simulate expert explanations, and offer feedback in real-time, raising fundamental questions about the nature of authorship, assessment integrity, instructional design, and teacher authority in EMI classrooms. The use of GenAI not only impacts language acquisition but also influences content comprehension, disciplinary discourse, formative feedback processes, and student engagement (Chiu, 2024; Law, 2024). In contexts where students may already face challenges in navigating content in a non-native language, the integration of AI introduces both new scaffolding opportunities and equity concerns (Pun et al., 2022).

EMI contexts are also distinctive in that they often involve two sets of educators—disciplinary content teachers and language specialists or English for Academic Purposes (EAP) professionals—who must collaborate to support students’ academic success (Wang et al., 2025; Yuan, 2023). The integration of GenAI in EMI therefore has implications not only for teaching and learning, but also for interdisciplinary collaboration, curriculum co-design, and EMI teacher development (Lo, 2024). For example, while subject teachers may view GenAI as a means to enhance disciplinary instruction, language educators might approach it from the perspective of academic literacy development, creating both synergies and tensions in pedagogical priorities.

This SI seeks to critically examine how GenAI is reshaping the goals, methods, and frameworks of EMI across these intersecting dimensions. We invite contributions that offer empirical research, theoretical frameworks, and practice-based innovations exploring how GenAI can support disciplinary literacy, promote inclusive pedagogy, reframe assessment practices, and shape EMI teacher development. Particular attention will also be paid to sociocultural, ethical, and equity-related considerations, including how GenAI may reinforce or challenge existing power structures in multilingual and multicultural EMI teaching and teacher education.

2. Rationale

The rationale for this SI lies in the urgent need to explore the pedagogical, institutional, and sociocultural implications of GenAI within EMI contexts—a rapidly expanding yet under-theorised educational context. While the integration of GenAI into ESL/EFL teaching as received increasing attention (e.g., Kohnke et al., 2023), its impact on EMI contexts remains relatively unexplored, despite the fundamentally different demands placed on students and educators in EMI settings.

EMI is distinct from traditional language classrooms in that students are expected to acquire disciplinary knowledge through a second or foreign language, often with limited language support (Richards & Pun, 2023). This dual focus on content and language learning makes EMI particularly susceptible to both the promises and pitfalls of GenAI. On one hand, GenAI tools can scaffold learning by simplifying texts, generating summaries, providing immediate feedback, or modelling academic discourse—potentially enhancing both content comprehension and language development (Mo & Crosthwaite, 2025). On the other hand, these same tools raise critical concerns regarding academic integrity, learner autonomy, epistemic authority, and the authenticity of language use in specific disciplinary settings (Yusuf et al., 2024).

The complexity of EMI environments is further compounded by the need for collaboration between content subject teachers and language educators (Deroey, 2023), each with differing pedagogical orientations, assessment practices, and understandings of how AI tools should be integrated into the classroom. This SI thus emphasises the need for interdisciplinary dialogue, encouraging contributions that investigate how GenAI is reshaping the dynamics of curriculum design, co-teaching, teacher development, and student support in EMI programmes.

In addition to pedagogical and institutional considerations, the SI will address broader sociocultural and ethical questions, such as how GenAI may reinforce inequalities in resource-constrained EMI settings, affect multilingual students’ identity formation, or shape institutional policies around acceptable AI use. These issues are especially relevant in EMI contexts where educational policies are often driven by internationalisation goals and where linguistic hierarchies may already marginalise students who do not conform to native-speaker norms (Dafouz & Smit, 2016).

The planned scope of the SI is therefore both broad and interdisciplinary, inviting submissions that:

• Theorise the implications of GenAI for EMI pedagogy, particularly in terms of disciplinary literacy, critical thinking, and communicative competence;
• Investigate how AI reshapesteacher instruction, classroom interactions, and assessment practices, including concerns around plagiarism, authorship, and AI-assisted writing;
• Examine how GenAI affectsEMI learners’ motivation, agency, and engagement;
• Explore the professional development needs and processes of EMI educators in response to AI integration;
• Analysethe formation and implementation of institutional policy frameworks and curriculum structures in guiding the ethical and responsible use of GenAI in multilingual learning environments.

By synthesising insights from applied linguistics, education technology, content and language integrated learning (CLIL), and EMI scholarship, this SI will provide a timely and comprehensive contribution to the field. It will serve as a foundational resource for researchers, practitioners, and policymakers seeking to understand and navigate the evolving role of AI in EMI contexts across diverse regions and disciplines.

3. Topics of Interest

We invite submissions on, but not limited to, the following areas:

• GenAI-Enhanced EMI Pedagogy
  Curriculum and instructional design integrating GenAI to support academic literacy and content learning.
• Assessment and Academic Integrity in the Age of AI
  Reconceptualising formative and summative assessment in light of GenAI capabilities and challenges to authorship norms.
• AI-Supported Teacher Development
  Use of GenAI in pre-service and in-service teacher education for instructional innovation, assessment literacy, and reflective practice.
• Language and Literacy Development in AI-Supported EMI
  The impact of GenAI on academic writing, multimodal composition, listening comprehension, and so forth.
• Equity, Ethics, and Policy in AI-Driven EMI
  Exploring the digital divide, algorithmic bias, and ethical policy frameworks for GenAI integration in EMI settings.
• Stakeholder Perspectives and Engagement
  Investigating the attitudes, engagement strategies, and identity formation of key stakeholders, such as teachers, learners, teacher educators, and school leaders, in GenAI-mediated EMI environments. This includes examining how each group navigates prompt engineering, pedagogical adaptation, and evolving roles in response to AI integration.

We welcome a wide range of manuscript types, including empirical research articles, systematic reviews, case studies, theoretical papers, and conceptual contributions. We particularly encourage submissions that provide empirical evidence, develop transferable frameworks applicable across diverse educational and sociolinguistic contexts, incorporate learner perspectives, address ethical dimensions of AI use, and advance theoretical understandings of AI-mediated language learning. We also welcome studies employing diverse methodological approaches, including qualitative classroom-based research, mixed-method and design-based studies, multimodal discourse analysis, ethnographic inquiry, AI-intervention and experimental research, as well as policy and institutional case studies.

Submission Guidelines

Authors are invited to submit an abstract of 300–500 words (excluding references) in APA 7th edition style as a Microsoft Word document (.doc or .docx). Abstracts should clearly outline the focus of the study, its theoretical and/or conceptual framing, the research gap being addressed, the methodology or research design employed, and the key findings or expected contribution to the theme of the special issue.

Submissions should include the title of the proposed paper, the names and affiliations of all authors, contact details for the corresponding author, and a brief biographical statement (approximately 100 words) for each author.

Following an initial review of abstracts, selected authors will be invited to submit full manuscripts for consideration in the special issue. All invited manuscripts will undergo double-blind peer review in accordance with the Australian Journal of Applied Linguistics' editorial procedures. Detailed author guidelines will be provided in the invitation to submit a full paper.

Submission and Enquiries

Please send abstracts and enquiries directly to the Guest Editors:

• Jack Pun:  jackpun@cuhk.edu.hk
• Eric Yuan:  ericruiyuan@um.edu.mo
• Kailun Wang:  wangkailun@bfsu.edu.cn

Timeline:

• Abstract Submission Deadline: August 31, 2026
• Notification of Acceptance: September 15, 2026
• Full Manuscript Deadline: April 15, 2027
• Expected Publication: Late 2027 [Continuous online publication]

References

Chiu, T. K. (2024). The impact of Generative AI (GenAI) on practices, policies and research direction in education: A case of ChatGPT and Midjourney. Interactive Learning Environments, 32(10), 6187-6203. https://doi.org/10.1080/10494820.2023.2253861 

Dafouz, E., & Smit, U. (2016). Towards a dynamic conceptual framework for English-medium education in multilingual university settings. Applied Linguistics, 37(3), 397-415. https://doi.org/10.1093/applin/amu034 

Deroey, K. L. (2023). English medium instruction lecturer training programmes: Content, delivery, ways forward. Journal of English for Academic Purposes, 62, Article 101223. https://doi.org/10.1016/j.jeap.2023.101223 

Hu, G., & Lei, J. (2014). English-medium instruction in Chinese higher education: A case study. Higher Education, 67, 551-567. https://doi.org/10.1007/s10734-013-9661-5 

Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537-550. https://doi.org/10.1177/00336882231162868 

Law, L. (2024). Application of generative artificial intelligence (GenAI) in language teaching and learning: A scoping literature review. Computers and Education Open, Article 100174. https://doi.org/10.1016/j.caeo.2024.100174 

Lo, Y. Y. (2024). From EMI to CLIL: Negotiating teacher identity. Journal of Multilingual and Multicultural Development, 1-17. https://doi.org/10.1080/01434632.2024.2380389 

Macaro, E., Curle, S., Pun, J., An, J., & Dearden, J. (2018). A systematic review of English medium instruction in higher education. Language teaching, 51(1), 36-76. https://doi.org/10.1017/S0261444817000350 

Mo, Z., & Crosthwaite, P. (2025). Exploring the affordances of generative AI large language models for stance and engagement in academic writing. Journal of English for Academic Purposes, 75, Article 101499. https://doi.org/10.1016/j.jeap.2025.101499 

Pun, J. K., & Thomas, N. (2020). English medium instruction: Teachers’ challenges and coping strategies. ELT Journal, 74(3), 247-257. https://doi.org/10.1093/elt/ccaa024 

Pun, J. K., Curle, S., & Yuksel, D. (Eds.). (2022). The use of technology in English medium education. Springer International Publishing. https://doi.org/10.1007/978-3-030-99622-2 

Pun, J., & Jin, X. (2021). Student challenges and learning strategies at Hong Kong EMI universities. Plos One, 16(5), Article e0251564. https://doi.org/10.1371/journal.pone.0251564 

Richards, J. C., & Pun, J. (2023). A typology of English-medium instruction. RELC Journal, 54(1), 216-240. https://doi.org/10.1177/0033688220968584 

Wang, K., Yuan, R., & De Costa, P. I. (2025). A critical review of English medium instruction (EMI) teacher development in higher education: From 2018 to 2022. Language Teaching, 58(2), 141-172. https://doi.org/10.1017/S0261444824000351 

Yuan, R. (2023). Promoting English-as-a-medium-of-instruction (EMI) teacher development in higher education: What can language specialists do and become?. RELC Journal, 54(1), 267-279. https://doi.org/10.1177/0033688220980173 

Yuan, R., Qiu, X., Wang, C., & Zhang, T. (2025). Students’ attitudes toward language learning and use in English-medium instruction (EMI) environments: a mixed methods study. Journal of Multilingual and Multicultural Development, 46(2), 244-261. https://doi.org/10.1080/01434632.2023.2176506 

Yusuf, A., Pervin, N., & Román-González, M. (2024). Generative AI and the future of higher education: a threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 21(1), Article 21. https://doi.org/10.1186/s41239-024-00453-6