Invited Colloquia

Presentation 2: Examining Interactional Characteristics of GenAI–Learner Task-Based Interaction in Chinese as a Second Language

Presenter: Jing YAN (The Education University of Hong Kong)

Abstract:

Oral interaction plays a facilitative role in second language (L2) acquisition by creating opportunities for learners to practise the target language, engage in negotiation of meaning and form, and enhance the comprehensibility of input and output. A task, which is characterised by a primary focus on meaning and the presence of a gap and a communicative outcome, have been widely adopted as an effective pedagogical tool to elicit oral interaction. Extensive research has examined task-based interaction between native speakers and L2 learners, as well as among L2 learners themselves. However, existing findings indicate that the frequency of negotiation of meaning and form is low, particularly in learner–learner interaction. With recent advances in generative artificial intelligence (GenAI), there is increasing research that has examined its role in enhancing L2 learners’ speaking skills. However, limited attention has been paid to how GenAI may enhance the quality of oral interaction with learners. Addressing this gap, this study will recruit Chinese as a second language (CSL) learners to engage in task-based interactions with GenAI. The study will train GenAI to implement negotiation of meaning and form strategies, and will examine the interactional characteristics of GenAI-CSL learners.

Presentation 4: Comparative Effectiveness of PPP and Task-Based Approaches on L2 Segmental Pronunciation Accuracy

Presenter: Amy KONG (The Hang Seng University of Hong Kong)

Abstract:

While the effectiveness of Present-Practice-Production (PPP) and Task-Based Language Teaching (TBLT) has aroused contentious debate, few studies have isolated their impact on both segmental and suprasegmental pronunciation accuracy within a short-term immersion context. This study investigates the comparative effectiveness of PPP and TBLT on the pronunciation accuracy of forty Grade-10 students participating in English language tasks in an English Summer Camp.

Participants were divided into two experimental groups: a PPP Group (N=20), who practiced reading aloud via four scripted drills, and a TBLT Group (N=20), who engaged in four communicative tasks, where segmental mispronunciations of vowels and consonants were corrected via immediate recasts, and suprasegmental issues were identified in the post-task reflection.

Adopting the pre-/post-test design, the students’ speech was recorded and analysed for segmental accuracy on selected content words, focusing on six specific criteria: vowel quality, vowel length, diphthong production, articulation for consonants, cluster reduction, and final consonant deletion. In addition, holistic ratings were also given to assess suprasegmental accuracy (intonation, stress, linking), as well as the overall speech intelligibility. Teachers of the respective groups were also interviewed regarding the students’ uptake of the correct phonological features during the practice and communicative tasks. Findings aim to inform determine whether explicit drilling or interaction-driven focus-on-form yields more significant improvement in pronunciation accuracy during intensive L2 immersion.

Presentation 1: Acceptance and Sustained Integration of AI-powered Learner Corpora in Primary Writing Instruction: Insights from TAM and ECM

Presenter: Wei Lun WONG (Universiti Kebangsaan Malaysia)

Abstract:

Artificial intelligence (AI) has considerable potential to enhance writing instruction in primary education. However, teachers’ sustained adoption of AI-powered learner corpora remains underexplored. This study investigated the factors influencing primary English teachers’ acceptance and continuance intention using the Technology Acceptance Model (TAM) and Expectation Confirmation Model (ECM). A structured questionnaire was administered to 355 Malaysian primary school English teachers to examine external support (facilitating conditions, learner engagement), individual characteristics (perceived self-efficacy, expectancy effects, growth mindset, interest), and technology perceptions (perceived ease of use, perceived usefulness, satisfaction, continuance intention). Data were analysed using exploratory and confirmatory factor analysis followed by Structural Equation Modelling. Findings indicate that perceived self-efficacy and interest significantly predicted perceived usefulness and continuance intention, indicating the central role of intrinsic motivation. In contrast, facilitating conditions did not significantly influence perceived ease of use. The results affirm the applicability of TAM–ECM in primary ELT and inform the pedagogically grounded design of AI-mediated writing tools.

Presentation 3: Reimagining the Übermensch in the Age of AI: A Philosophical Framework for Teachers’ Global Competence Development

Presenter: Nur Syafiqah YACCOB (Universiti Kebangsaan Malaysia)

Abstract:

In an era marked by rapid globalisation and the increasing integration of artificial intelligence (AI) in education, developing teachers’ global competence has become both urgent and complex. While current discourse often prioritises technological proficiency, less attention has been given to the philosophical foundations that can guide educators in navigating AI-enhanced learning environments critically and ethically. This session reinterprets Friedrich Nietzsche’s concept of the Übermensch as a metaphorical framework for reimagining teacher development in the digital age. Within contemporary educational contexts, the Übermensch symbolises a reflective, self-aware and forward-thinking educator who continuously engages in self-improvement and critical transformation. When aligned with models of global competence, this framework foregrounds key dispositions such as intercultural sensitivity, global-mindedness, pedagogical adaptability and critical self-awareness. These elements are essential for teachers responding to increasingly diverse classrooms and technologically mediated forms of instruction. In AI-driven educational settings, the Übermenschmetaphor encourages teachers to move beyond passive adoption of digital tools and instead exercise ethical judgement, professional agency and pedagogical discernment. Rather than positioning AI as a substitute for teacher expertise, this perspective emphasises educators as human-centred facilitators who promote empathy, critical thinking and global awareness among learners. By bridging classical philosophy with contemporary debates on AI and global competence, this conceptual paper offers a novel lens for understanding transformative teacher professionalism in twenty-first century education. It contributes to ongoing discussions on how educators can maintain agency, ethical responsibility and global responsiveness particularly within English as a Second Language (ESL) contexts.

Presentation 1: From Workers to Co-workers: Exploring Pre-service English Teachers’ Agency in AI-assisted Teaching from the Ecological Perspective

Presenter: Tsung-han Weng (Macau University of Science and Technology)

Abstract:

The growing integration of artificial intelligence (AI) in education has reshaped English language teaching and raised questions about teacher agency. Drawing on an ecological affordance perspective (Gibson, 1977, 1979), this qualitative study examines how four pre-service English teachers enact agency in AI-assisted teaching. Data were collected through semi-structured interviews, teaching artifacts, and classroom observations, and analyzed using Braun and Clarke’s (2006, 2021) thematic analysis. Findings identify five categories of AI affordances strategically mobilized in practice: efficiency enhancement (e.g., lesson planning and automated feedback), personalized generation (textual and multimodal resources), resource provision (linguistic and content support), classroom engagement (dialogue simulation and gamification), and problem-solving (instructional and management support). Participants demonstrated both proactive and ambivalent orientations toward AI. While valuing its pedagogical utility, they expressed concerns about technical limitations, over-reliance, and diminished human connection. Importantly, their conceptualization of AI evolved from a subordinate “worker” to a collaborative “co-worker.” AI adoption was mediated by ecological factors across technical, peer, institutional, and societal levels. The study highlights the transformative yet constrained nature of AI in ELT and underscores the need for ecologically informed teacher education that supports critical and agentic AI integration.

Presentation 3: Understanding the Crisis of Teacher Educators in Cultivating AI Literacy: A Role-Identity Conflict Perspective

Presenter: Xiaojing Wang  (Beijing Normal University)

Abstract:

This study addresses a critical yet under-researched crisis confronting teacher educators tasked with cultivating Artificial Intelligence (AI) literacy in both pre- and in-service teachers. While existing scholarship prioritizes the development of teachers’ AI competencies, the professional struggles of those who train teachers, the teacher educators themselves, remain largely unexamined. Framed by role‑identity conflict theory, this paper contends that teacher educators are entangled in a multilayered professional dilemma as they negotiate the rapid infusion of AI into education. This crisis unfolds across three interrelated dimensions: epistemological conflict, pedagogical-practical tension, and ethical-agency dilemma.

Employing a qualitative multiple‑case study design, the research draws on in‑depth interviews, reflective journals, and classroom observations with a purposive sample of teacher educators from two teacher-preparation programs. Thematic analysis is used to identify patterns of struggle, adaptation, and resistance in their professional practices.

Preliminary findings reveal that teacher educators experience what is termed “double mediation anxiety”, a dual-source anxiety stemming from (1) mediating between rapidly evolving AI technologies and their students (future teachers), and (2) mediating between their own emergent expertise and heightened institutional expectations. This crisis transcends technical skill gaps; it is fundamentally identity-laden, undermining professional self-efficacy and inhibiting pedagogical innovation.

By shifting scholarly attention from teacher AI literacy to the experiences of teacher educators who foster it, this study contributes a needed perspective to the literature. It underscores the necessity for targeted professional development, collaborative support networks, and policy frameworks that explicitly acknowledge and address the role-identity conflicts inherent in preparing teachers for an AI-mediated educational landscape. Finally, supporting teacher educators is imperative not only for the effective integration of AI literacy but also for sustaining the integrity and agency of the teaching profession in the digital age.