Generative AI (GenAI) has become popular with many university students since late 2022 and is incorporated in their daily life. To use GenAI tools effectively and equitably, and also to meet industry expectations, students should be supported to develop GenAI literacy skills during their time at university. Even though some universities have provided AI literacy training courses to students, the courses are not yet widely available to students across the higher education sector. Besides, there has been a shortage of GenAI literacy courses in particular. And more importantly, it is unclear what knowledge and skills that need to be included relating to GenAI literacy, from the student perspective. Building a strong understanding of student perceptions in this area is critical, because it will help enhance appropriateness and content relevance and promote student engagement. Adopting the four-dimensional AI literacy framework as the theoretical foundation, this paper aims to address the gaps. It explores the perceptions of university students on GenAI literacy in the UK and Hong Kong contexts. Survey data from 234 students were collected and analyzed using the descriptive analysis and Kruskal–Wallis test. The results show that factors such as country of studies and prior learning about AI greatly impacted GenAI literacy. However, factors such as age and educational level do not have a significant effect. Built upon the findings and the theoretical foundation, this paper proposes a new GenAI literacy framework. This framework is a revised version of the four-dimensional AI literacy framework and proposes the essential GenAI knowledge and skills for university students. The new framework also acknowledges the impact of the macro, meso, and micro factors on student GenAI literacy development. Copyright © 2024 The Author(s).

This paper explores the pedagogical approaches teachers employ and the strategies that school leaders implement to promote the use of an Adaptive Learning Platform (ALP) in a high school in Hong Kong. It assesses how such an AI-powered platform can support teachers in enhancing student engagement and learning outcomes. The study employs a case study methodology to identify the factors influencing the incorporation of ALP. Qualitative data were collected through teacher interviews, and an in-depth analysis was carried out to identify effective pedagogies and strategies for implementing adaptive learning. The findings of this study underscore the significance of supportive school leadership that encourages teachers to embrace ALP as a tool for promoting student learning. Additionally, effective implementation of ALP necessitates a shift in pedagogical practices from traditional teacher-centred approaches to more student-centred strategies. The study concludes that the integration of ALP into teaching practices can boost student engagement and elevate the quality of learning outcomes. In summary, this research contributes to understanding the successful implementation of ALP in high schools, emphasising the critical roles of school leadership support and the adoption of student-centred pedagogies. The findings provide valuable insights for educators, administrators, and policymakers interested in utilising ALP to improve student learning outcomes. Copyright © 2023 by the owner/author(s).

Artificial Intelligence in Education (AIED) has been the focus of significant attention in recent years because of its growing social importance and pedagogical value. In Hong Kong, an increasing number of K-12 schools are planning or piloting their grassroots AIED incorporation practices. However, progress is reportedly slow due to a number of barriers. Unfortunately, because of their limited scope, disputed interpretations and contextual irrelevance, current research literature seems to be of limited referencing value in aiding schools to address the issues and overcome the barriers. This paper highlights the necessity of embracing AIED as wide-ranging given the current understanding of it as a collective notion. Three major directions of AIED are identified: Learning from AI, Learning about AI, and Learning with AI. A collective case study, which examined the perceived barriers to AIED incorporation in Hong Kong K-12 schools with different AIED directions, was conducted. Qualitative data were gathered via ten semi-structured interviews with key stakeholders from two schools. Ertmer’s (1999) typology was applied to segregate the barriers. The findings showed that both first-order and second-order barriers existed, although they varied between the cases. It was also found that the barriers did not hinder in isolation but appeared to be interconnected. The findings suggest that schools use differentiated strategies to tackle barriers according to their approach to incorporating AIED. Moreover, there is a need to trace the links between barriers and prioritise school efforts to remove or reduce them with high linkage. Several recommendations for practice are given. Copyright © 2021 The Author(s). Published by Elsevier Ltd.