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).

Aviation is a multidisciplinary subject that has influenced human development over the last century. Learning about aviation exposes students to principles of flight, earth science, aeronautical engineering, language, aviation communication and airmanship. In higher education, many non-aviation undergraduates participate in aviation related activities to have a first glimpse of the aviation industry and equip themselves with basic concepts. This study aims to examine learning perception among 82 university students who have participated in a series of online aviation career exploration activities during the pandemic in Hong Kong and China. They participated in virtual visits and career talks led by aviation professionals, hands-on flight simulation activities and online discussion in an online lab setting. A mixed research method was employed with the use of a motivational survey, teachers’ observation and semi-structured interviews to understand students’ learning perceptions. This study found that flying laboratory activities could effectively motivate students to learn aviation and improve their aviation knowledge. This could promote students’ aviation industry optimism, which may help the industry to recover in the post-pandemic world. This article offers recommendations for online engineering educators to use emerging technologies to teach aviation for future career preparation. Copyright © 2023 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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.

Aim/Purpose: This article aims at the critical present: to serve a constructive purpose in the current COVID-19 crisis by presenting practice driven pedagogical strategies for online learning and teaching. It acknowledges the multitude of challenges faced by educators through the delivery of online instructional strategies for schools.Background: The development of information technology enables online learning and blended learning to be increasingly popular in extending students’ learning opportunities. Technology-enabled learning approaches make students’ learning more flexible and personalized. In Hong Kong, one of the first few cities where the COVID-19 pandemic outbreak was first reported, school classes have been suspended since the end of Lunar New Year on February 3, 2020.Methodology: This research used a qualitative method of multiple case analysis to explore how three educators from primary, secondary, and tertiary institutes employed various strategies to offer learning and teaching as usual. Naturalistic inquiry was used to observe, describe, and interpret the “lived experiences” of the three educators and the perceptions of stakeholders.Contribution: Since early February 2020, school classes have been suspended amid the COVID-19 pandemic in Hong Kong, one of the first cities where the coronavirus outbreak was first reported. This timely article overviews effective practices with the use of online learning technologies to support academia from around the world to achieve teaching and learning in an online environment.Findings: Results indicate that meaningful cognitive activities rely on teachers’ leading role to build a blended approach that combines the advantages of asynchronous and synchronous methods in order to facilitate social interaction among students. Furthermore, our research has revealed that educators are likely to perceive three non-teaching challenges on a rapid blended transition of the learning – digital divide, data privacy, and professional

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.