Background: Artificial Intelligence (AI) literacy is a crucial part of digital literacy that all individuals should possess in today's technologically advanced world. Despite the potential benefits that AI education offers, little research has been done on how to teach AI literacy to children. Objectives: This study aimed to fill that gap by investigating how children were engaged in AI literacy activities that are supported by intelligent agents. These activities were implemented in a Hong Kong kindergarten with a class of six 5-year-olds (Mage = 62.83 months, SD = 2.91) over a 6-week period. Methods: The study gathered data from multiple sources, including classroom observations, teacher interviews, and documents/artefacts. Results and Conclusions: The results showed that children could learn about AI through interaction with intelligent agents in embodied learning contexts. The findings of this study have implications for the broader field of digital technology education, particularly in the context of early childhood education. Copyright © 2023 John Wiley & Sons Ltd.

Background: Since the outbreak of COVID-19, universities in Hong Kong have implemented online and hybrid teaching modes, making computer-assisted language learning (CALL) a primary way for English learning. Research on English learning motivation and self-regulation has seldom considered learners' emotions (satisfaction and preparedness) and the synchronic online courses in a CALL context. Objectives: This study analyses the relationship between Hong Kong university students' motivation and self-regulation by considering their emotions in computer-assisted synchronic online English courses. Methods: 311 Hong Kong university students were invited to complete two questionnaires that investigated students' motivation and self-regulation in computer-assisted online English courses. SPSS 26 was employed for exploratory factor analysis (EFA) to extract factors and exclude unsuitable items, and confirmatory factor analysis (CFA) was performed to verify the results of EFA. One-way ANOVA was conducted to detect the impact of personal emotion on motivation and self-regulation. AMOS 26 was adopted to construct a structural equation model (SEM) to explore the relationship between motivation and self-regulation in the CALL context. Results and Conclusions: The results show that satisfaction and preparedness positively correlate with online English learning motivation and self-regulation. Instrumentality-attitude significantly predicts all factors of self-regulation via SEM and goal setting significantly predicts all factors of motivation. With feasible English learning goals, Hong Kong university students are more proactive in learning English online where they actively interact with teachers and peers. Implications: Based on the L2 self-system (Dörnyei, The psychology of the language learner: Individual differences in second language acquisition, 2005) and self-determination theory (Barnard et al., Internet and Higher Education, 2009, 12, 1–6), this study clarifies the relationship

Collective academic supervision (CAS) is a collective model for students' academic supervision to reduce their isolation and as a measure to establish a congenial culture and to develop networks with their peers. Most studies focus on the benefits of online CAS, leaving the pedagogical process and students' learning experiences understudied. This research examines the participation and learning experience of a cohort of Master of Education (MEd) students in online supervision that took place on a Moodle platform. This article reports a case study of Moodle-based CAS in Hong Kong that aims to train postgraduate students into teacher-researchers. A class of MEd students and their supervisors were observed, and their online dialogues were analysed. The bio-ecological student engagement model was used to explain the online supervision process. The results indicated that the students' learning was situated and embodied in the online social processes facilitated by peers' and supervisors' replies. The online interaction behaviours mainly included proposing questions or problems, providing information or solutions, and making comments. The findings have provided an exemplary case regarding the application of the online learning environment in supporting CAS and active research-based learning. The productive online CAS seems to benefit both teacher candidates and their supervisors by promoting the co-construction of the knowledge and skills of educational research, although more evidence is needed. Copyright © 2021 The Authors. Journal of Computer Assisted Learning published by John Wiley & Sons Ltd.

This study aimed to examine the role and process of reflective assessment supported by the Knowledge Connections Analyzer in helping low academic achievers to develop epistemic agency in knowledge building. The participants were 33 ninth-grade low achievers from a visual arts course in Hong Kong. A comparison class of 33 students, taught by the same teacher and studying the same topics in a regular knowledge-building environment, also participated. Qualitative tracing of students' online discourse showed that reflective assessment can help low achievers develop high-level epistemic agency. Qualitative analysis of the students' prompt sheets revealed that reflective assessment encouraged low achievers to set knowledge-building goals, collectively and continuously analyse and reflect on their inquiry and ideas, and generate actions to address identified gaps, thus helping them engage in high-level epistemic agency. The study results have important implications for designing technology-rich environments that support learners and offer insights into how teachers can help learners develop epistemic agency.
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