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Thomas K. F. Chiu

Publications and source records attributed to Thomas K. F. Chiu.

3 recordsLinked to original sources

Educational Short Videos: Bibliometric Trends, Thematic Structure, and Operationalisation

Educational short-video research spans disciplines, platforms and learning contexts, but the same label is applied to resources differing in function, activity, context and evaluation, complicating comparison and evidence synthesis. This study mapped the development, thematic organisation and operationalisation of educational short-video research. We analysed 2,169 records indexed in Web of Science and Scopus up to 11 June 2026 using bibliometric analysis, non-negative matrix factorisation topic modelling and structured content analysis. Publication output increased sharply from the mid-2010s but remained dispersed across outlets. Among 16 first-level topics, Skill Development in Educational Contexts was the largest, forming the structural core of the field, while topic overlap was predominantly pairwise. Video-Based Health Interventions for Attitude Change and Cognitive Load and Engagement in Instructional Video Design combined positive recent growth with comparatively high citation visibility, whereas Social Media Engagement Strategies for Education emerged as a rapidly growing direction. Knowledge/Achievement and Engagement/Motivation were the most widely represented outcome domains, and experimental and synthesis designs were more common among identifiable records in health-related topics. Indexed descriptions most often foregrounded intended users, educational uses, learning content, and interactivity. A representative duration was available for 511 records, but no consistently applied numerical threshold was evident. These findings support a working definition of educational short videos as discrete multimedia messages combining words and visuals and designed or used to promote learning. Shortness should be interpreted relative to educational purpose, content unit and surrounding activity rather than duration alone.

cs.CY↗

How to Assess AI Literacy: Misalignment Between Self-Reported and Objective-Based Measures

The widespread adoption of Artificial Intelligence (AI) in K-12 education highlights the need for psychometrically-tested measures of teachers' AI literacy. Existing work has primarily relied on either self-report (SR) or objective-based (OB) assessments, with few studies aligning the two within a shared framework to compare perceived versus demonstrated competencies or examine how prior AI literacy experience shapes this relationship. This gap limits the scalability of learning analytics and the development of learner profile-driven instructional design. In this study, we developed and evaluated SR and OB measures of teacher AI literacy within the established framework of Concept, Use, Evaluate, and Ethics. Confirmatory factor analyses support construct validity with good reliability and acceptable fit. Results reveal a low correlation between SR and OB factors. Latent profile analysis identified six distinct profiles, including overestimation (SR > OB), underestimation (SR < OB), alignment (SR close to OB), and a unique low-SR/low-OB profile among teachers without AI literacy experience. Theoretically, this work extends existing AI literacy frameworks by validating SR and OB measures on shared dimensions. Practically, the instruments function as diagnostic tools for professional development, supporting AI-informed decisions (e.g., growth monitoring, needs profiling) and enabling scalable learning analytics interventions tailored to teacher subgroups.

cs.CY↗

Creation and Evaluation of a Pre-tertiary Artificial Intelligence (AI) Curriculum

Contributions: The Chinese University of Hong Kong (CUHK)-Jockey Club AI for the Future Project (AI4Future) co-created an AI curriculum for pre-tertiary education and evaluated its efficacy. While AI is conventionally taught in tertiary level education, our co-creation process successfully developed the curriculum that has been used in secondary school teaching in Hong Kong and received positive feedback. Background: AI4Future is a cross-sector project that engages five major partners - CUHK Faculty of Engineering and Faculty of Education, Hong Kong secondary schools, the government and the AI industry. A team of 14 professors with expertise in engineering and education collaborated with 17 principals and teachers from 6 secondary schools to co-create the curriculum. This team formation bridges the gap between researchers in engineering and education, together with practitioners in education context. Research Questions: What are the main features of the curriculum content developed through the co-creation process? Would the curriculum significantly improve the students perceived competence in, as well as attitude and motivation towards AI? What are the teachers perceptions of the co-creation process that aims to accommodate and foster teacher autonomy? Methodology: This study adopted a mix of quantitative and qualitative methods and involved 335 student participants. Findings: 1) two main features of learning resources, 2) the students perceived greater competence, and developed more positive attitude to learn AI, and 3) the co-creation process generated a variety of resources which enhanced the teachers knowledge in AI, as well as fostered teachers autonomy in bringing the subject matter into their classrooms.

cs.AI↗