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 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.

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.

The rapid development of generative artificial intelligence (GenAI) tools (e.g. ChatGPT) has elicited mixed reactions among English language instructors and learners. This study explores how first-year students in an English for Academic Purposes (EAP) course at a Hong Kong university perceive GenAI and traditional grammar-checking tools (e.g. Grammarly, MS Word). We employed a qualitative methodology grounded in the interpretivist paradigm, conducting semi-structured interviews with 14 students. The findings revealed the students perceived GenAI tools to be more comprehensive and authoritative, as they provide detailed explanations and contextual insights that enhance language proficiency. However, they also noted concerns about overreliance, data privacy and equitable access to premium features. The study examines the ethical and pedagogical implications of integrating GenAI tools into higher education, highlighting their potential and the necessity of institutional guidance. It contributes to the ongoing discourse on the role of GenAI in academic writing instruction. Copyright © 2024 The Author(s).

Artificial Intelligence (AI) has rapidly emerged as a transformative force across diverse sectors worldwide, with policy initiatives signalling its rising prominence. In the realm of education, AI holds promise for revolutionising learning and teaching. However, the incorporation of AI in basic education faces significant challenges for many schools, particularly when explicit curriculum guidelines are absent, and teachers have limited exposure to AI technologies. This study delves into the importance of integrating Artificial Intelligence in Education (AIED), particularly in primary and secondary school settings, with a specific emphasis on the obstacles and facilitators. The research examines the impact of digital leadership and its role in mitigating teachers' barriers to AIED incorporations. These external variables are systematically outlined within the conceptual framework proposed by this study. Furthermore, it conceptualises and considers three AIED approaches—Learning from AI, Learning about AI, and Learning with AI—as internal variables central to basic-education AIED incorporations, as highlighted in previous research. The study gathers quantitative data through a self-constructed questionnaire survey from 204 school principals and management-level teachers across 60 primary and secondary schools in Hong Kong, capturing their perceptions of AIED incorporation. A structural equation model (SEM) is adopted to analyse the relationships between digital leadership, internal and external teacher barriers, and the three AIED approaches. The findings underscore that digital leadership is a significant factor in facilitating AI incorporations in schools, and that teachers' internal and external barriers considerably affect Learning about AI. The paper concludes by proposing strategies to enhance the capabilities of school leaders and teachers in promoting AIED in basic education. Copyright © 2023 The Author(s).

The integration of generative artificial intelligence (AI) in English language teaching presents opportunities and challenges for instructors. This study explores the attitudes of higher education English language instructors towards generative AI tools, their intentions to use them and the institutional support and professional development necessary to teach and learn with them. As the field continues to evolve rapidly, it is essential to comprehend the readiness of front-line language instructors. This qualitative interpretive study seeks to identify the digital competencies and pedagogical knowledge required to implement generative AI in education and provide guidance for the design of professional development programmes that address the challenges and concerns associated with adopting AI. Drawing on semi-structured interviews with twelve instructors at a higher education institution in Hong Kong, the findings reveal the significance of familiarity and confidence with using AI-driven teaching tools, the challenges and concerns language instructors face and the need for tailored support and professional development. The study offers ten practical implications to cultivate language instructors’ digital competencies, pedagogical knowledge and positive attitudes towards integrating AI to enhance their students’ learning experiences. Copyright © 2023 The Authors. Published by Elsevier Ltd.

Artificial Intelligence in Education (AIED) has been the focus of significant attention in recent years because of its growing social importance and pedagogical value. In Hong Kong, an increasing number of K-12 schools are planning or piloting their grassroots AIED incorporation practices. However, progress is reportedly slow due to a number of barriers. Unfortunately, because of their limited scope, disputed interpretations and contextual irrelevance, current research literature seems to be of limited referencing value in aiding schools to address the issues and overcome the barriers. This paper highlights the necessity of embracing AIED as wide-ranging given the current understanding of it as a collective notion. Three major directions of AIED are identified: Learning from AI, Learning about AI, and Learning with AI. A collective case study, which examined the perceived barriers to AIED incorporation in Hong Kong K-12 schools with different AIED directions, was conducted. Qualitative data were gathered via ten semi-structured interviews with key stakeholders from two schools. Ertmer’s (1999) typology was applied to segregate the barriers. The findings showed that both first-order and second-order barriers existed, although they varied between the cases. It was also found that the barriers did not hinder in isolation but appeared to be interconnected. The findings suggest that schools use differentiated strategies to tackle barriers according to their approach to incorporating AIED. Moreover, there is a need to trace the links between barriers and prioritise school efforts to remove or reduce them with high linkage. Several recommendations for practice are given. Copyright © 2021 The Author(s). Published by Elsevier Ltd.