Artificial Intelligence (AI) has rapidly emerged as a transformative force across diverse sectors worldwide, with policy initiatives signalling its rising prominence. In the realm of education, AI holds promise for revolutionising learning and teaching. However, the incorporation of AI in basic education faces significant challenges for many schools, particularly when explicit curriculum guidelines are absent, and teachers have limited exposure to AI technologies. This study delves into the importance of integrating Artificial Intelligence in Education (AIED), particularly in primary and secondary school settings, with a specific emphasis on the obstacles and facilitators. The research examines the impact of digital leadership and its role in mitigating teachers' barriers to AIED incorporations. These external variables are systematically outlined within the conceptual framework proposed by this study. Furthermore, it conceptualises and considers three AIED approaches—Learning from AI, Learning about AI, and Learning with AI—as internal variables central to basic-education AIED incorporations, as highlighted in previous research. The study gathers quantitative data through a self-constructed questionnaire survey from 204 school principals and management-level teachers across 60 primary and secondary schools in Hong Kong, capturing their perceptions of AIED incorporation. A structural equation model (SEM) is adopted to analyse the relationships between digital leadership, internal and external teacher barriers, and the three AIED approaches. The findings underscore that digital leadership is a significant factor in facilitating AI incorporations in schools, and that teachers' internal and external barriers considerably affect Learning about AI. The paper concludes by proposing strategies to enhance the capabilities of school leaders and teachers in promoting AIED in basic education. Copyright © 2023 The Author(s).

BACKGROUND: In recent years, there has been considerable interest in taking up the opportunities of Artificial Intelligence (AI) in education. As an emerging concept and a driving technological force, AI is changing the kind of knowledge and skills students need for success in the forthcoming Industrial Revolution 4.0, and at the same time, enabling novel, powerful methods of teaching and learning. Although many school leaders, policy-makers and frontline teachers understand the need for embracing AI, they may lack clarity on what to teach students in the era of AI; how to harness the power of AI tools; and where to start given their own school contexts. This paper describes an ongoing study that uses Communities of Practice (CoPs) as a knowledge management strategy to support Hong Kong K-12 schools in embracing AI. CoPs comprise members who genuinely care about a mutual interest and seek to solve a common problem. The premise underlying this study is that the cultivation of CoPs may present enabling conditions and pave the way for AI adoptions. In these CoPs, members would engage in reflective discussions on the opportunities and challenges of embracing AI in their schools and collaboratively support each other through meaningful knowledge exchange. METHODS: A collective case study is being conducted with members from 20 K-12 schools in Hong Kong to substantiate and illustrate the power of CoPs that aim to support schools in embracing AI. Qualitative data are gathered via semi-structured interviews and focus-group meetings with participants. Thematic analysis is adopted for analysing the data. RESULTS: The preliminary results of this ongoing study have shown that CoPs, if successfully launched, can play an uplifting role for Hong Kong K-12 schools embracing AI. More specifically, the collaborative learning process in CoPs has helped members make sense of AI core concepts and the pedagogical implications as they access authentic, relevant and updated information. The

BACKGROUND: Research on Artificial Intelligence in Education (AIEd) has become increasingly active during the past 20 years, with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in the application scope of AIEd research. A comprehensive overview of this field is necessary to understand what had been concerned in AIEd research, what is going on in the field of AIEd, and what might be the future of AIEd research. METHODS: This paper combined the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale AIEd literature in the past two decades. Specifically, by using STM, we uncovered important research topics concerned within the AIEd community. The evolution of these topics was explored by using a nonparametric Mann-Kendall (MK) trend test. In addition, the correlation between these topics was further explored and visualized based on a semi-parametric Gaussian procedure. RESULTS: The annual trend of publications concerning AIEd had grown consistently across the past 20 years, indicating that research on AIEd had received a growing interest from academia Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics and promising research orientations. Specifically, the top six topics having the highest proportions in the dataset were cognition and perception, programming education, prediction, MOOCs, semantic Web and recommender systems, assessment and feedback, and emotion detection. Results of the MK test highlighted five topics receiving an increase in research interest, including prediction, assessment and feedback, flipped classroom and biosignal data, STEM education, and English language learning. In addition, several inter-topic research directions were identified, including 1) flipped classroom and biosignal data, emotion detection, and