International Summit on the Use of AI in Language Learning and Teaching 2026 (AIinLT 2026, 22 June).

​The Hong Kong Polytechnic University for the International Summit on the Use of AI in Language Learning and Teaching (AIinLT 2026) took place from 21–24 June 2026. Organised by the Department of English and Communication in partnership with the Education Bureau and SCOLAR, the summit explored the theme “AI in Language Education: From Ideas to Implementation to Impact.”
On 22 June, our team presented Transforming Academic English Pedagogy: GenAI-Integrated Tasks, AI Literacy, and the Psychology of Student Engagement.
The mixed-methods study examined how GenAI-integrated tasks, paired with AI literacy instruction, addressed students’ psychological needs for relatedness, competence, and autonomy (Self-Determination Theory) in first-year English for Academic Purposes and content-based courses across six Hong Kong universities. We investigated impacts on AI literacy (usage/competence, critical evaluation, ethical awareness) and cognitive, behavioural, and social engagement.
Highlights: Quantitative data from 249 paired pre- and post-questionnaires showed significant gains in all three AI literacy dimensions (small effect sizes). Students also reported high perceived social, cognitive, and behavioural engagement. Multiple regressions revealed that post-intervention critical evaluation strongly predicted all three forms of engagement; ethical awareness further predicted cognitive and behavioural engagement.
Qualitative interviews with 23 students highlighted six themes: enhanced AI literacy (prompt crafting, metacognitive strategies, critical debate with AI); mixed views on academic English competence (helpful for writing, less so for speaking); growth in autonomy and self-efficacy alongside risks of over-reliance; stronger cognitive-behavioural engagement through well-sequenced tasks, tempered by occasional disengagement; mixed social outcomes (better peer communication versus potential isolation); and a clear preference for human interaction and limited trust in AI feedback.
Takeaways: AI-integrated tasks work best when they deliberately foster social interaction and bonding, prioritise face-to-face connection and empathy, and embed robust AI literacy. Future work would benefit from control groups, more speaking-focused activities, performance measures, and longitudinal designs.

​Talk (11 May 2026, Language Centre, Hong Kong Baptist University)

​With the growing use of generative Al (GenAl), Al literacy and academic literacy have emerged as critical competencies for second language (L2) undergraduate students. However, limited research has explored how GenAl-enhanced tasks in English for Academic Purposes (EAP) courses meet students' psychological needs for relatedness, competence, and autonomy of the self-determination theory that underpin intrinsic motivation and learning. This study addresses this gap by examining how GenAl tasks integrated with explicit Al literacy instruction fulfill these needs among some EAP students across six Hong Kong universities. By analyzing survey data from participants, we found moderate gains in relatedness and competence, but large increases in student autonomy. Participants also demonstrated significantly higher post-intervention scores in Al literacy. Multiple linear regression analyses revealed significant connections between Al literacy dimensions and student engagement. Building on these quantitative findings, this presentation focuses on the qualitative interview findings from participants, which reveal six key themes. These findings offer actionable insights for educators and researchers seeking to design curricula that enhance student autonomy while shaping institutional Al policies that support meaningful learning.

​While the benefits of Generative AI tools for EAP courses have been investigated, the question of how to specifically integrate adapted GenAI tasks into EAP and content-based courses requiring academic English for first-year students at universities remains under-explored. Underpinned by the principled communicative language teaching approach and ChatGPT literacy framework, this study aims to identify university teachers’ difficulties and opportunities for incorporating GenAI into EAP courses and subject-based courses requiring the usage of academic English, and to adapt GenAI tasks for use in EAP and content-based courses at six UGC-funded universities in Hong Kong. To evaluate the effectiveness of tasks in increasing students’ engagement, learning, and GenAI literacy, data were collected using questionnaires and in-depth interviews. Brief insights into the findings will be shared. The findings may have implications for educators and researchers in terms of how to shape future curriculum design integrating adapted GenAI tasks, teacher training, and policy in HE. 

​While the impact of Generative AI tools on teaching and learning has been investigated, teachers’ perspective of their influence on EAP courses and content-based courses requiring the usage of academic English remains under-researched. This study examines the perceptions of teachers towards GenAI tools on EAP and content-based subjects. In-depth semi-structured interviews were conducted with ten teachers from six H.K. UGC-funded universities. Participants indicated that GenAI tools had affected the curriculum and assessments. However, most indicated a lack of confidence in using these tools effectively, the need to keep up with rapidly evolving tools while expressing the importance of clear institutional guidance on GenAI use. They also stressed the need for students to use GenAI ethically and critically. This study has implications for influencing future practices in HE such as equipping students with Generative AI literacy in the EAP context while raising teachers’ competence in using GenAI tools through more training.