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.

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.

Artificial intelligence (AI) literacy is widely recognized as a new set of competencies that people use AI effectively and ethically in everyday life. In K-12 education, educators have started to employ different pedagogical strategies to foster students' AI literacy. Using digital story writing (DSW) in the classroom is an effective inquiry-based pedagogical approach to address literacy development by improving language and technological abilities across disciplines. This study recruited 82 primary students in Hong Kong to attend a three-month DSW journey to learn AI and completed a knowledge test at the end of the program. Among the students, 16 best-achieving students were further invited to participate in artefact-based interviews, and we further analysed their work to understand how they formulate AI understandings. It is found that the participating students were able to propose an authentic scenario, apply their new knowledge of AI and think up meaningful AI-driven solutions in their digital stories. Furthermore, we examined students' DSW process in terms of inquiry-based pedagogical cycle (orientation, conceptualization, investigation, conclusion, discussion) to demonstrate how they learn AI. Overall, DSW as an inquiry could effectively foster students' AI literacy in using and applying AI knowledge to solve real-life problems, far beyond merely knowing and understanding related concepts. We suggest that using DSW as a pedagogy has the potential to support students to scaffold students’ AI understanding, particularly for young children. Copyright © 2022 The Authors. Published by Elsevier Ltd.

In recent years, schools started to incorporate artificial intelligence (AI) into computer science/STEAM curricula. However, few validated measurements have been designed to examine how secondary students develop AI literacy and perceive their learning outcomes. AI literacy has been measured from students' knowledge and skill acquisition, and behavior and attitudes. This research aims to develop and validate an instrument to assess AI literacy for secondary students. A questionnaire with 25 items measured in a 5-point Likert scale was created. In a pilot study, the questionnaire was administered to a sample of 363 secondary school students from two different schools in Hong Kong. Confirmatory factor analysis was conducted and grouped into six factors: (1) intrinsic motivation, (2) self-efficacy, (3) behavioral intention, (4) behavioral engagement, (5) know and understand, and (6) use and apply AI. The questionnaire showed a good fit model to support internal consistency reliability in most of the factors, with Cronbach's Alpha levels ranging from.58 to.88. A less-parsimonious model was proposed that help educators measure a wider AI literacy skill set with an acceptable model fit, with Cronbach's Alpha levels ranging from.91 to.94 based on affective, behavorial, cognitive and ethical (ABCE) learning framework. Further studies are needed to confirm the factor structure. Copyright © 2023 IEEE.

COVID-19 virus outbreak started in Hong Kong in January 2020. All schools in Hong Kong were suspended from January 2020 to December 2022. Many secondary schools in Hong Kong would like to catch up with the learning schedule during the pandemic and e-learning became the possible solution. There were different forms of e-learning method. Some common examples were online lecture, e-learning platform and e-learning education technology tools. The use of e-learning tools became popular and common during the pandemic. Because of the sudden shift to e-learning lecture, teachers might not have enough time to realize students’ e-learning needs. As a result, teachers may not be able to provide sufficient needs-satisfying support to students when they are using e-learning tools in the lesson. This study would like to apply an experimental approach to investigate the influence of needs-satisfying teaching approaches on the motivation of using e-learning tool based on Self-determination Theory (Deci & Ryan, 1986). Hong Kong secondary school students would be intrinsically motivated to e-learning tool in the Science subject through satisfying the needs for competence, autonomy and relatedness. In this study, the context was e-learning in Science class for secondary school students in Hong Kong. The learning tasks were junior secondary school level Physics and Chemistry. Questionnaire was utilized for quantitative data collection and a subgroup of students were invited to have individual interviews for qualitative data collection. A modified model based on SDT was proposed in this applied field research to study the influence of the intervention of needs-satisfying approaches to the motivation of using science e-learning tool in Science subjects on Hong Kong secondary school students. All rights reserved.

The rapid development of generative artificial intelligence (GenAI) tools has given rise to a growing discussion of the potential challenges and benefits that the use of these technologies may present in the field of education. This study examines the acceptance of the use of GenAI tools for teaching and learning among primary and secondary school teachers in Hong Kong. It uses an extension of the technology acceptance model (TAM) with a modified framework that incorporates two key factors: self-efficacy and subjective norm. Data were collected from a sample of 367 primary and secondary school teachers in Hong Kong using questionnaires containing items for six constructs: self-efficacy, perceived usefulness, perceived ease of use, attitude towards using, subjective norm, and behavioural intention. The results show that fostering teachers' self-efficacy, perceived usefulness, and attitude is essential for successfully increasing their behavioural intention to use GenAI tools. Subjective norm was also found to influence teachers' behavioural intention. To enhance teachers' effective use of GenAI for teaching, teacher development programmes should focus on equipping teachers with comprehensive conceptual knowledge and skills and an understanding of the application of these tools to teaching and learning. Policy support to create a conducive environment for the use of GenAI in teaching and learning would also be beneficial. The study has theoretical implications in its extension of the TAM model as well as implications for enhancing teachers’ AI literacy and developing pedagogies for the meaningful use of GenAI tools for teaching and learning in K–12 settings. Copyright © 2024 The Authors. Published by Elsevier Ltd.