Faculty Publications and Conference Presentations

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.

The long-established notion that language is not the only available means of communication has led to a growing interest in diverse communication modalities, which can be extended to higher education assessment. Traditionally, language courses in higher education rely on written essays and presentations for assessment. However, the emergence of Generative Artificial Intelligence has introduced challenges to these conventional assessment methods, because students can complete these tasks with some simple prompts for ChatGPT. To enhance higher education assessments, incorporating multimodal components that merge various semiotic modes has been proposed. While scholars have addressed the integration of multimodality into course design, few studies have explored its practical implementation in assessment and course design. This article examines the effects of multimodal assessments on 86 students enrolled in an English communication course and the transferability of assessment skills to other courses and domains. As an initial exploration of a two-year large-scale project, by utilizing data from content analysis of qualitative student interviews along with quantitative survey results, the study assesses the implications for English communication assessment and skill transferability based on students’ self-perception. Initial 4ndings reveal that students who engaged in the course and multimodal assessment effectively translated a variety of so skills into other university courses and other domains. is study also showcased that collaborative multimodal projects are conducive to so skills development and transfer.

Generative artificial intelligence has emerged as a powerful technological innovation that is exerting a sweeping influence in the education sector. The rapid diffusion of ChatGPT in universities worldwide presents both opportunities and challenges to higher education policy and administration. This study seeks to examine students’ behavioural and perceptual experiences with the use of ChatGPT at The Hong Kong University of Science and Technology, which was among the first universities worldwide to introduce officially protected ChatGPT services. Launched in November 2023, a university-wide student survey sampled 680 students from both the Hong Kong campus and the Guangzhou campus in Mainland China. The results indicate significant usage of ChatGPT services among the students and a very high level of intention to continue that use. The students perceive ChatGPT to contribute positively to both their learning and career development. Significant variations were found according to gender, level of study, age group, discipline, and country/region of origin, in terms of the students’ experiences with ChatGPT. These results offer useful evidence for curriculum reform and the improvement of students’ AI literacy in higher education institutions.

The proliferation of COVID-19 fake news on social media poses a severe threat to the health information ecosystem. We show that affective computing can make significant contributions to combat this infodemic. Given that fake news is often presented with emotional appeals, we propose a new perspective on the role of emotion in the attitudes, perceptions, and behaviors of the dissemination of information. We study emotions in conjunction with fake news, and explore different aspects of their interaction. To process both emotion and ‘falsehood’ based on the same set of data, we auto-tag emotions on existing COVID-19 fake news datasets following an established emotion taxonomy. More specifically, based on the distribution of seven basic emotions (e.g. Happiness, Like, Fear, Sadness, Surprise, Disgust, Anger ), we find across domains and styles that COVID-19 fake news is dominated by emotions of Fear (e.g., of coronavirus), and Disgust (e.g., of social conflicts). In addition, the framing of fake news in terms of gain-versus-loss reveals a close correlation between emotions, perceptions, and collective human reactions. Our analysis confirms the significant role of emotion Fear in the spreading of the fake news, especially when contextualized in the loss frame. Our study points to a future direction of incorporating emotion footprints in models of automatic fake news detection, and establishes an affective computing approach to information quality in general and fake news detection in particular.

Collaborative digital multimodal composing (CDMC) has recently gained traction in the advent of technology-enhanced language learning, yet scant attention was paid to the influence of semiotic resources on the interaction patterns between learners. The present study attempted to fill the research gap by examining the interactions between English as a Second Language (ESL) learners in an online English for Academic Purpose (EAP) course. Using a multiple case-study design, three pairs of undergraduates completed a collaborative multimodal writing and video-making task for presentation using ZOOM. Influence of semiotic resources can be unveiled through manipulating the mode of presentation. Data of this study includes the verbal exchanges and non-verbal on-screen interactions between the participants while they are working on the tasks. Their interaction patterns were analyzed through conversation analysis and two aspects of collaborations, namely equality and mutuality, were also examined. Their interaction patterns were found to be strikingly different across both tasks, and languaging mainly serves the function of verbalization of content-related issues, such as searching for information and assembling various multimodal elements, with only rare instances of either grammar-based or lexis-based language-related episodes (LRE). Another striking finding is that pairs who are working collaboratively with balanced division and mutual contribution are less susceptible to our manipulation, as compared to those who are demonstrating a dominant-dominant or expert-novice working pattern. Implications of these findings on fostering peer collaborations during CDMC are discussed.

This longitudinal mixed-methods study tracked the informal language contact and phrasal verb knowledge of 21 Chinese foundation program students in the United Kingdom (UK) during the 2019–2020 academic year through three rounds of data collection. Because of the disruption of the COVID-19 pandemic, the study was able to capture the impact of a global crisis on the experiences of international students studying abroad and learning a second language (L2). Data from a Language Contact Questionnaire, a Study Abroad Social Network Survey, and semi-structured interviews indicated significant changes in their L2 use and social networks. Our findings showed that the students sharply reduced their L2 contact and increased their use of first language (L1) during the pandemic. Productive and receptive tests assessing phrasal verb knowledge revealed that the students did not make significant gains after the pandemic lockdown. The study suggests that significant changes in the living environment can directly impact students’ L2 usage and their formulaic language development.

This paper proposes the design of a course which integrates ChatGPT into Project-based Learning (PBL). It is a 12-hour Chinese language course which aims to cater to the diverse learning needs of the students in the formal Chinese language courses at the Hong Kong University of Science and Technology (HKUST). The design of the course empowers learners to set their own intended learning outcomes and to determine a topic for a group project which leads to a product based on their learning needs and interests. They can achieve the intended learning outcomes through exploring and accomplishing the project with the guidance of the teacher and the utilization of ChatGPT. The course objective, content and sequencing, format and presentation, and assessment are illustrated based on Nation and Macalister’s (2010) model. Through the analysis of the findings, we have identified the various roles of learners, teachers, and ChatGPT in the course. This paper provides insights into the potential of artificial intelligence (AI) tools in language education and a useful reference for future AIintegrated course design. 本研究旨在提出一个将 ChatGPT 融合到项目式学习中的短期汉语课程设计框架。该课程共 12小时，旨在满足香港科技大学学生多样化的学习需求，尤其是他们在正式课程中无法实现的个体需求。课程初始阶段，学生可以自主设定预期学习成果，并据此确定一个小组成员皆感兴趣的项目主题及最终产出成果。此后，学生会运用 Chat GPT 进行内容和语言方面的探索，并在教师的指导下完成项目。本文基于 Nation 和 Macalister（ 2010）的语言课程设计模型，详细论述了课程目标、教学内容与组织、学习活动，以及评估方式。在研究发现中，本文总结了学习者、教师和 ChatGPT 在课程中所扮演的角色，据此分析了人工智能（ AI）工具在语言教育中的潜力，并为未来整合 AI 的中文课程设计提供了参考建议。