This study, which investigated primary school English teachers’ cognition in English Language Teaching (ELT), was a two-year longitudinal qualitative case study with five cases. Data were collected across two years from June 2014 to June 2016 at two sites (i.e., the informants’ practicum schools and in-service schools) using different sources, including semi-structured interviews, lesson observations, post-lesson conferences and reflective journals (i.e., one retrospective text and one questionnaire) for checking the consistency and inconsistency of the informants’ claimed beliefs about ELT and their pedagogy during actual teaching practice; other artifacts collected during the lesson observation (e.g., lesson plans, learning products, etc.) were also taken into consideration.Data were gathered at three stages: (1) before the teaching practicum (2) during the teaching practicum, and (3) during the 1st year of teaching. These procedures were followed first to identify the initial beliefs, assumptions and pedagogical content knowledge the five informants held before they served in schools, and then to track the stability and changes over time from “schooling to teaching practicum” and “teaching practicum to 1st year teaching.” The analysis revealed that the five informants’ cognition, shaped by both schooling, and professional coursework would change over time when influenced by contextual factors.The perceptions the informants held about ELT were quite consistent with their fond, and negative experiences encountered during their early “schooling.” They regarded the following elements as effective: motivation, inviting learning environments, good teacher-student rapport, availability of learning resources and encouragement from knowledgeable adults, e.g., NETs, favorable teachers, etc. The following traditional teaching approaches were considered ineffective and demotivating to language learning: drilling, rote-learning, and being teacher-centred. Evidence showed that

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).

The advent of generative artificial intelligence (AI) has transformed information acquisition and utilization in higher education academic writing. This study fills the empirical research gap by examining how university students use generative AI tools for digital academic writing. The study investigates the experiences of 140 undergraduates enrolled in an ‘Introduction to Linguistics’ course at a university in Hong Kong employing generative AI applications, such as ChatGPT3.5 and the AI Image Generation feature in Padlet, for a collaborative multimedia Wikibook project spanning 13 weeks. The mixed-method approach included analyzing AI-generated content integration within 29 multimedia-enhanced Wikibook chapters, development logs with ChatGPT interactions, the assimilation of AI-generated content into drafts, and collaborative composition dynamics. Additionally, a survey with 98 student responses assessed perceptions regarding AI efficacy in academic writing support. Key findings reveal the multifaceted role of ChatGPT in shaping discourse, offering linguistic tools, and structuring information. The students recognized the advantages of leveraging AI tools, rapidly acclimating to their strengths and limitations through practical engagement. The outcomes of this research offer insights into students’ effective utilization of AI-generated content and suggest potential directions for incorporating generative AI tools into academic writing pedagogy. Copyright © 2024 by the authors.

Assistive technology (AT) professionals are in pressing need with nowadays growing aged/disabled population, so as well-designed higher education programs in this field. This study designed and implemented a case-based active learning approach within an undergraduate course related to AT in Hong Kong, and assessed its impact on enhancing student engagement over two academic years. A total of twelve multimedia patient case dossiers on six major physical disabilities were created. Two cohorts of students enrolled in course “Rehabilitation Engineering and Assistive Technology” were instructed to utilize the case dossiers to facilitate their learning, understanding, and application of ATs for aged/disabled individuals. The Revised Two-Factor Study Process Questionnaire was employed to evaluate the student feedback on their learning experience, engagement, and learning approaches (i.e., Deep Approach, DA; and Surface Approach, SA) before and after the course. Upon completing the course, students’ DA scores significantly increased from 29.4 ± 6.9 to 31.4 ± 8.9 (p = 0.013). Additionally, significantly moderate positive correlations were found between the DA-SA value and students’ individual written report grades (p = 0.004) and overall grades (p = 0.048). In contrast, a significantly moderate negative correlation was identified between students’ individual report grades and SA scores (p = 0.019). These findings support the feasibility and effectiveness of implementing case-based active learning in higher education within the field of AT, supporting future large-scale implementation and optimization of such case-based active learning and teaching strategy in the AT field. Copyright © 2025 Informa UK limited, trading as Taylor & Francis group.

Empowerment enables students to be psychologically and affectively ready to leverage the benefits of artificial intelligence (AI). However, a theory-driven scale to extend empowerment into the use of AI for problem-solving is lacking. This study developed and validated an 11-item scale of empowerment in using AI for problem-solving (EUAIPS) based on a proposed conceptual framework that synthesises empowerment and AI-related literature. The EUAIPS scale encompasses impact, self-efficacy, and meaningfulness in using AI for problem-solving. We collected data from a diverse sample of Hong Kong senior secondary and university students before (N = 477) and after the course (N = 409). Results demonstrated that the EUAIPS scale with a three-factor structure had good reliability and validity. Students also felt significantly more empowered to use AI for problem-solving after a 14-h course using AI for problem-solving. These findings empirically support the affective dimension of AI literacy and show that psychological control/competence is particularly important for students to harness AI to solve problems at the affective level. This study presents a valid instrument for researchers and practitioners to measure empowerment in using AI for problem-solving and informs AI literacy curriculum designers about including learning activities to help students realise its impact, self-efficacy, and meaningfulness. Copyright © 2025 The Authors. Published by Elsevier Ltd.

This study examines the interplay among language proficiency, use and attitudes of Shenzhen-Hong Kong cross-border students (CBS), a unique group of Hong Kong students who reside in Shenzhen and attend school in Hong Kong. A questionnaire survey of over 500 CBS reveals that Putonghua is their dominant language, followed by Cantonese, while their hometown dialect and English play peripheral roles. CBS show strong preferences for Putonghua, both instrumentally and integratively. Statistical analyses indicate significant correlations between CBS’ proficiency in the four languages and their attitudes towards them. The study identifies pairwise associations among CBS’ use of Cantonese at school, their proficiency in the language, and their attitudes towards it, forming a reinforcing cycle. However, no such patterns were found for Putonghua or English. The study concludes that improving CBS’ Cantonese proficiency and emotional connection to the language is essential for their integration into Hong Kong society, addressing identity challenges, and fostering a cohesive sense of belonging. Copyright © 2024 Informa UK Limited, trading as Taylor & Francis Group.

In order to create a linguistically inclusive environment for multilingual learners’ meaning making of nature of science (NOS), it is important to allow students to freely mobilise different linguistic and modal resources for meaning making. In this single case study of a Hong Kong seventh-grade linguistically diverse classroom, we characterise two types of translanguaging spaces for meaning making of NOS: NOS-negotiation space and NOS-inscription space. Furthermore, we articulate how linguistically diverse learners navigate within and across these two translanguaging spaces. Our findings indicate that the teacher and students discussed a network of NOS ideas using gestures, different named languages as well as visual resources within the NOS-negotiation space. Students co-learnt metalanguages of NOS and created a multimodal ensemble of NOS within the NOS-inscription space. We also identified factors that facilitate or impede students’ connection of NOS ideas across two translanguaging spaces. Research reported in the paper calls for science educators to explicitly design translanguaging spaces to foster an inclusive environment for linguistically diverse learners’ meaning making of NOS. Copyright © 2024 Informa UK Limited, trading as Taylor & Francis Group.

Artificial intelligence (AI) literacy is at the top of the agenda for education today in developing learners' AI knowledge, skills, attitudes and values in the 21st century. However, there are few validated research instruments for educators to examine how secondary students develop and perceive their learning outcomes. After reviewing the literature on AI literacy questionnaires, we categorized the identified competencies in four dimensions: (1) affective learning (intrinsic motivation and self-efficacy/confidence), (2) behavioural learning (behavioural commitment and collaboration), (3) cognitive learning (know and understand; apply, evaluate and create) and (4) ethical learning. Then, a 32-item self-reported questionnaire on AI literacy (AILQ) was developed and validated to measure students' literacy development in the four dimensions. The design and validation of AILQ were examined through theoretical review, expert judgement, interview, pilot study and first- and second-order confirmatory factor analysis. This article reports the findings of a pilot study using a preliminary version of the AILQ among 363 secondary school students in Hong Kong to analyse the psychometric properties of the instrument. Results indicated a four-factor structure of the AILQ and revealed good reliability and validity. The AILQ is recommended as a reliable measurement scale for assessing how secondary students foster their AI literacy and inform better instructional design based on the proposed affective, behavioural, cognitive and ethical (ABCE) learning framework. Copyright © 2023 The Authors.

In recent years, AI technologies have been developed to promote students' self-regulated learning (SRL) and proactive learning in digital learning environments. This paper discusses a comparative study between generative AI-based (SRLbot) and rule-based AI chatbots (Nemobot) in a 3-week science learning experience with 74 Secondary 4 students in Hong Kong. The experimental group used SRLbot to maintain a regular study habit and facilitate their SRL, while the control group utilized rule-based AI chatbots. Results showed that SRLbot effectively enhanced students' science knowledge, behavioural engagement and motivation. Quantile regression analysis indicated that the number of interactions significantly predicted variations in SRL. Students appreciated the personalized recommendations and flexibility of SRLbot, which adjusted responses based on their specific learning and SRL scenarios. The ChatGPT-enhanced instructional design reduced learning anxiety and promoted learning performance, motivation and sustained learning habits. Students' feedback on learning challenges, psychological support and self-regulation behaviours provided insights into their progress and experience with this technology. SRLbot's adaptability and personalized approach distinguished it from rule-based chatbots. The findings offer valuable evidence for AI developers and educators to consider generative AI settings and chatbot design, facilitating greater success in online science learning. Copyright © 2024 The Authors.