Faculty Publications and Conference Presentations

<p>Generative artificial intelligence (GenAI)-powered tools have become influential in the landscape of informal digital learning of English (IDLE). However, the psychological mechanisms underlying English-as-a-foreign-language (EFL) learners’ intention to continue using GenAI in IDLE remain underexplored. Drawing on the cognitive appraisal theory, the present study examined the relationship between appraisals of language learning (control, intrinsic value, extrinsic value), appraisals of technology (usefulness, ease of use, risk), emotional responses towards the use of GenAI, and continuance intention. To this end, 503 Chinese university students completed a survey, and data were analyzed using partial least squares structural equation modeling. The results showed that learners’ perceived control over the language was indirectly and positively related to their continuance intention. Their indirect relationship was mediated by higher perceived usefulness, higher perceived ease of use, or lower perceived risk, each of which was followed by increased positive emotions. Additionally, extrinsic value appraisal was found to be significantly and positively associated with perceived risk, which was then related to a decrease in positive emotion and continuance intention. Based on these findings, practical implications for EFL teachers on how to support learners in achieving a balanced and productive use of GenAI tools in IDLE are advanced.</p>

This study examines a redesigned peer observation of teaching (POT) framework in a Hong Kong university language centre. The framework offers multiple observation formats—self-observation, observing peers, and reciprocal observation—combined with peer dialogue and self-reflection to give teachers more autonomy. While existing literature lacks in-depth analysis of choice in teaching observations, findings indicate strong engagement with self-observation and peer observation, with many participants valuing reflective discussions and learning through observation. However, teachers suggested making participation optional and allowing observation reports to remain private. Overall, responses were positive regarding increased flexibility, with recommendations for incorporating ‘unseen observations' and a ‘just watching' approach. The paper concludes by advocating for professional learning communities and conversational networks to broaden the impact of teaching observations, fostering collegial development across the centre.

<p>The rapid emergence and integration of generative artificial intelligence (GenAI) in higher education present both challenges and opportunities. There is a critical need to consolidate empirical evidence, existing practices, assessments, and normative discussions to reflect on the recent advancement and inform adaptation in higher education policies. Asia presents a compelling context in which to explore the impact of GenAI on higher education due to its diverse educational landscape, rapid technological advancements, and unique socio-cultural dynamics. This Special Issue examines the inclusion of GenAI in higher education in Asia and reflects on the challenges and opportunities brought about by this sweeping revolution. Ten articles in this special issue examine GenAI and its application in higher education from diverse perspectives. Synthesizing these fresh findings based in Asia, this introductory article proposes a framework of actionable strategies for integrating GenAI into higher education that may inform education policymaking and administration in the years to come.</p>

The rapid advancement of Generative Artificial Intelligence (AI) has ushered in both unprecedented opportunities and challenges within the realm of higher education, particularly in the context of English language teaching. This article delineates seven foundational principles that aim to optimize the integration of Generative AI in teaching and learning environments. Developed from the practical experiences of frontline language teachers at Hong Kong universities, these principles advocate for a paradigm shift in the perception and utilization of AI—from viewing it merely as a tool for information retrieval to recognizing it as a robust collaborator capable of enhancing cognitive development and interaction. These principles emphasize the importance of leveraging AI's capabilities to foster a more interactive and dynamic educational experience. They address the need to prioritize AI as a reasoning engine, ensure the quality of data inputs, customize AI interactions, actively harness AI to simulate human textual interactions, and focus on ethical AI usage, among others. By implementing these principles, educators can transform AI into a powerful ally that not only streamlines educational processes but also significantly enriches learning outcomes. This approach prepares students to adeptly navigate and analyze the complex information landscapes of their academic and professional futures, thus making AI an essential partner in education. The article serves as a call to action for educators to engage deeply with AI technologies, proposing a thoughtful framework that fosters enhanced educational practices and prepares students for a digitally integrated world.

<p>This study explores the transformative impact of participatory research on the conceptualization of Kongish. A hybridized written form comprising a creative blend of Cantonese and English, Kongish is a grassroots media-linguistic phenomenon within the vibrant sociocultural ecology of Hong Kong. Our research aims to evaluate how a participatory methodology—as opposed to traditional researcher-oriented approaches where researchers remain at arm's length with their research subjects—reshapes the understanding of Kongish as well as Hong Kong English (HKE). Using a participatory linguistics framework, this study triangulates data from focus group interviews, individual interviews, and online surveys to explore the reciprocal influences between researchers and participants. The study is organized into two tiers: first, the transformation of participants, and second, the transformation of researchers. Each tier draws on personal narratives to offer distinct insights into real-life encounters with Hong Kong's written vernacular. By integrating the “lived experiences” of both researchers and participants, the study reveals how our interactions continuously influence each other's perceptions of language, language practices, and personal identities.</p>

This article examines the transformative role of Artificial Intelligence (AI), Generative AI (GenAI), and Large Language Models (LLMs) like ChatGPT and DeepSeek in language teaching and learning. It highlights their capabilities, including personalized content generation, automated assessment, real-time feedback, and administrative efficiency, while emphasizing the necessity of prompt engineering to optimize outputs. The PROMPT (Persona, Requirements, Organization, Medium, Purpose, Tone) framework is introduced as a structured approach to crafting effective prompts. Case studies, such as DeepSeek’s Python code generation, demonstrate practical applications. However, the article critically addresses limitations like bias, factual inaccuracies, and ethical concerns, advocating for rigorous fact-checking and balanced human-AI collaboration. By synthesizing research and practical examples, this article underscores AI’s potential to enhance language education while urging educators to adopt critical literacy and ethical frameworks to mitigate risks and ensure equitable, human-centered learning experiences.

<p>This brief article explores the integration of Generative Artificial Intelligence (GenAI) tools into blended learning environments for designing and teaching pronunciation tasks. Adapting Calamlam’s (2016) conceptual framework, a flowchart is proposed to guide the use of three GenAI tool types to enhance pronunciation instruction: Listen-Mimic Type, Analyze-Feedback Type, and Practice-Feedback Type. The framework facilitates effective use of both synchronous and asynchronous learning opportu-nities. While this study offers a theoretical overview, it highlights the potential for broader application across various language skills. Future research is necessary to evaluate the effectiveness of these tools and their role in the evolving educational landscape.</p>

<p>The present study investigates whether congruency facilitation and figurative interference—two counteractive effects—persist in L2 collocational processing when both congruency and figurativeness are present. A primed lexical decision task was administered to 44 L1-Chinese L2-English learners and 40 L1-English speakers to assess response times for figurative congruent collocations, along with their matched literal congruent and figurative incongruent collocations. Results showed that while collocational priming was absent, both congruency facilitation and figurative interference emerged, with their effects modulated by L2 proficiency. Specifically, in low-proficiency learners, congruency facilitation appeared to outweigh figurative interference, whereas in high-proficiency learners, figurative interference became more pronounced as L1-based facilitation was suppressed. These findings suggest that L2 learners initially rely on their activated L1 semantic network but gradually shift toward developing L2 collocational representations as proficiency increases, though these representations may remain weak and insufficient to facilitate collocate access.</p>

ここ数年の間に GenAI Technology の急速な進展は目覚ましい。ビジネ スのみならず、教育分野での GenAI Technology の活用が注目されている。 言語教育では、英語教育や中国語教育においては、実践事例、研究事例が 報告され始めている。世界的に見て大変に注目されている研究題目となっ ている。ところが、第３外国語教育、特に日本語教育での実践事例や研究 事例は限られている。正に、始まったばかりのようである。 本稿では、大学の初級日本語コースでの Chatbot の実践的活用について 報告する。日本語初級者においても、Chatbot は学習を少しでも効率よく できるツールとなるのか、初級者ではどのような学習内容に使うのか、 Chatbot 活用の良かった点や改善が必要と思われる点は具体的に何か、な どを学生の声をもとに明確にする目的で取り組んだ。 調査対象学生 40 名で、毎月ミニレポートという形で調査を学期中継続 した。Chatbot は対話的に学習者と関わることができる。これは学生には 親しみやすく興味が持てたようで Chatbot 活用には大変に積極的であった。 40 名全員からデータを回収することができた。学生が特に活用した学習 内容としては、１）語彙の学習、２）日本語の文法学習、３）日本文化に 関する学習が上位 3 つであった。 学生からは、Chatbot の対話型アプローチが楽しいという意見や、いつ でも使える、欲しい情報がすぐに手に入るなど良かった点が大変多く寄せ られた。一方、改善点としては説明が難しくわかりにくい、正確ではない ように感じる時があったなど、数は多くはなかったが寄せられた。 Chatbot の活用は初級日本語コースにおいて、教育的有用性が十分に認 められた。対話型学習、日本語の学習サポート、日本文化情報の提供など の点で、GenAI Technology の日本語教育への活用可能性を示唆している。 今後の日本語教育における GenAI Technology の一層の活用が期待される。