For decades, second language pedagogies worldwide have operated under the assumption that language functions as a system—something that can be neatly broken into discrete units, systematically taught, and reinforced through corrective feedback. Once language has been abstracted from its natural contexts of use, educators set about organizing it into teachable units, packaged as methods. These methods, shaped by evolving educational discourse, have attempted—and continue to attempt—to incorporate anthropological and sociological insights, acknowledging both the social and individual dimensions of language. Consequently, modern pedagogies are now presented with terms like ‘personalised’, ‘communicative’, and ‘grounded in SLA theories’. Yet despite this shift in terminology, language remains disconnected from individuals and continues to be taught as an object, rather than as an integral part of a person’s living, personal experience—one that is learned and used to help navigate the complexities of their unique journey.

In this keynote, I propose a shift in perspective, viewing language learning as deeply intertwined with an individual’s experiences and responses to the world. AI offers powerful tools to help bridge gaps, facilitating more personalised and collaborative learning experiences, even in classrooms where teachers have little to no English proficiency. To this end, I will focus on three key elements that demonstrate the transformative potential of AI in language education: 1. AI and Feedback as Participation: AI can facilitate a participatory model of feedback, where students and teachers engage with language together, fostering deeper interaction and collaborative learning. 2. AI and Neuroscience: Neuroscience research emphasises the central role of emotions in the learning process. Emotions drive attention, motivation, and memory, all critical elements for effective learning. AI, particularly conversational AI, excels at interpreting and responding to emotions, offering new pathways to align students' personal experiences with language learning. 3. AI and Emotional Literacy: It is widely acknowledged that education requires well- informed processes to support learning as both a social and emotional transformative experience. In this keynote, I will introduce my Reading for Emotion (RfE) model, demonstrating how AI can enhance emotional literacy and foster deeper connections between learners and language.

Biography:

Dr. Ania Lian is a senior academic at the International Graduate Centre of Education, College of Education, where she has played a pivotal role in shaping the master’s in education course and developing a robust supervision culture for Higher Degree Research (HDR) students. With a focus on building HDR student capacity, Dr. Lian has also established strong international collaborations, particularly with Cambodia, Vietnam, and Indonesia. Recognised globally for her expertise in Computer-Assisted Language Learning (CALL), student-centred pedagogies for second and foreign languages, and English literacy and academic writing, Dr. Lian’s research is also expanding into projects focused on ensuring quality assurance in higher education curricula. In 2017, she was awarded the prestigious Australia Awards grant, linking Charles Darwin University with universities in Cambodia. The grant has been instrumental in developing internationally recognised academic courses that align with global standards. Dr. Lian’s recent research innovations include the Reading for Emotion (RfE) method, designed to enhance students’ language and literacy skills, and a groundbreaking integration of neuroscience with CALL to offer a student-centred approach to academic writing. Her work with PhD students continues to drive forward the conversation on the future of language learning and higher education.

The Age of AI’ began in 2022 with the arrival of ChatGPT. Many commentators now think that AI will be as revolutionary as the internet and the invention of smart phones that ushered in the era of Data and Devices, 15 years ago. The pace of change has been unprecedented. The global public has already become familiar with the jargon related to the latest models of generative AI, and well-established practices are now being disrupted in many sectors. The challenge for educationalists is to understand this jargon and to come to terms with the benefits and risks of the emerging tech in their own contexts.

In this talk I discuss the future of language learning, teaching and assessment in light of these developments, focusing in particular on the opportunities that EdTech supported by educational applications of AI (EdAI) offer for integrating learning and assessment (ILA) more effectively. In sharing these thoughts, I pose the following questions:

• In what ways will educators in Asia need to adapt their roles and practices to benefit from these opportunities?

Specifically, what are the implications for language teachers and what practical steps can they take to do things differently over the next few years? From the point of view of policy making, what needs to be done to ensure adequate access to digital technology in schools (internet, devices, etc), and to address the potential risks associated with AI? In addressing these questions, I provide examples of work at the University of Cambridge, including our latest approach to ILA.

Biography:

Nick Saville is Director of Thought Leadership, at Cambridge University Press & Assessment (English), UK and Secretary-General of the Association of Language Testers in Europe (ALTE). He holds a PhD in language assessment specialising in test impact and has over 40 years’ experience in language education. He has been an advisor for the Council of Europe, including for the CEFR and its Companion Volume, and is co-editor of the Studies in Language Testing series (CUP). His professional interests include English linguistics, plurilingualism, Learning Oriented Assessment (LOA), EdTech combined with educational uses of AI (EdAI), and ethical frameworks in language education.