In this editorial, we discuss the affordances and challenges of emerging technologies in designing and implementing STEM education as a planned theme of this special issue. We view that emerging technologies, such as artificial intelligence (AI) and virtual reality, have a double-edged sword effect on STEM learning and teaching. Exploring the effect will help provide a balanced view that simultaneously recognizes the benefits and pitfalls of the technologies and avoids overstating either one. This themed issue highlights how immersive and AI-driven learning environments advance and transform STEM education in different contexts. It consists of this editorial, three research reviews, and two empirical research articles contributed by scholars from five different regions, including Australia, Hong Kong, mainland China, Singapore, and the USA. They discussed the educational, social, and technological effects of emerging technologies. Each article discusses to various extent about the current research status, what and how the technologies can afford, and what concerns the technologies may bring to STEM education. © The Author(s), 2023.

This paper proposes and describes a context- and subject-specific professional digital competence (PDC) framework for beginning English language teachers in Hong Kong. Taking a localised approach, the framework was developed through a four-stage data collection process, (1) Literature review of empirical studies conducted in Hong Kong of English language teachers’ technology use, (2) Review of local government curriculum documents; (3) An online survey of English language teachers’ technology use (n = 83); (4) follow-up individual interviews (n = 22). The study revealed that the local subject-specific PDC of teachers includes five aspects; Technological proficiency; Pedagogical compatibility; Preparing students for the digital world; Risk, well-being and ethical awareness and Professional work. Each aspect includes ability statements. The framework can be used to evaluate existing teacher education programmes, support the development of pre-service English-language teachers and facilitate self-, peer- and teacher assessment. The elements of PDC can be converted into programme- and course-level learning outcomes or incorporated into assessment rubrics. In addition, the methods used to develop the framework can be used by scholars in other contexts and subject areas to develop localised subject-specific PDC frameworks. Copyright © 2023 De La Salle University.

Artificial intelligence (AI) education for K-12 students is an emerging necessity, owing to the rapid advancement and deployment of AI technologies. It is essential to take teachers' perspectives into account when creating ecologically valid AI education programmes for K-12 settings. However, very few studies investigated teacher perception of AI education. Phenomenography is an empirical research method that was widely used to understand teacher's interpretive understanding of new phenomenon, in this study, the teaching of AI in secondary school. Therefore, the present study investigated teachers' conceptions of teaching AI using a phenomenographic approach. Twenty-eight in-service teachers from 17 secondary schools in Hong Kong were invited to participate in an interview after implementing an AI curriculum. Six categories of teacher conceptions were identified: (1) technology bridging, (2) knowledge delivery, (3) interest stimulation, (4) ethics establishment, (5) capability cultivation, and (6) intellectual development. The hierarchical relationships of the six concepts were organised as an outcome space. The space shows a range of surface to deep conceptions and offers an understanding of how teachers perceive AI education through their teaching experience. Two learning paths have been suggested for cultivating technical and non-technical teachers for teaching AI. These learning paths provide insights for teacher educators and policymakers to enhance teachers' competence in teaching AI and promote general AI education for K-12 students. Copyright © 2022 Springer.

Contributions: The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created the first pretertiary AI curriculum at the secondary school level for Hong Kong and evaluated its efficacy. This study added to the AI education community by introducing a new AI curriculum framework. The preposttest multifactors evaluation about students’ perceptions of AI learning confirmed that the curriculum is effective in promoting AI learning. The teachers also confirmed the co-creation process enhanced their capacity to implement AI education. Background: AI4Future is a cross-sector project that engages five major partners—CUHK’s Faculty of Engineering and Faculty of Education, secondary schools, Hong Kong government, and AI industry. A team of 14 professors collaborated with 17 principals and teachers from six secondary schools to co-create the curriculum. Research Questions: Would the curriculum significantly improve the student perceived competence, attitude, and motivation toward AI learning? How does the co-creation process benefit the implementation of the curriculum? Methodology: The participants were 335 students and eight teachers from the secondary schools. This study adopted a mix-method with quantitative data measures at pre- and post-questionnaires and qualitative data emphasizes teachers’ perspectives on the co-creation process. Paired [Formula Omitted]-tests and ANCOVAs, and thematic analysis were used to analyze the data. Findings: 1) students perceived greater competence and developed a more positive attitude to learn AI and 2) the co-creation process enhanced teachers’ knowledge in AI, as well as fostered teachers’ autonomy in bringing the subject matter into their classrooms. Copyright © 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE).

Purpose: A growing body of research has focused on the topic of inclusive education for ethnic minorities in Hong Kong. At the same time, few scholars have explored the role technology can play in enhancing inclusivity within the context of quality education. This study examines how the differentiated use of technology that takes into account disparities in prior knowledge can benefit students of different learning backgrounds.Design/Approach/Methods: This study adopted an experimental design to investigate how content-specific and content-neutral technologies can be strategically used to support the classroom discussions of Chinese and ethnic minority students' learning Chinese language and mathematics. One hundred and twenty-one secondary school students participated in the study. Each student was randomly assigned to a condition, in a 2 (ethnicity: Chinese vs. ethnic minority) × 2 (content: content-specific first vs. content-neutral first) design.Findings: The study found that (a) for Chinese language lessons, ethnic minority students preferred to learn with content-neutral and content-specific technologies in prediscussion and postdiscussion activities, respectively; whereas the Chinese students' preferences were the opposite and (b) for mathematics lessons, both groups of students performed better when content-specific and content-neutral technologies were used in prediscussion and postdiscussion activities, respectively.Originality/Value: The study presents five practical suggestions for the strategic use of technology in inclusive classrooms in Hong Kong. Copyright © 2020 The Author(s).