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With the rapid development of generative artificial intelligence, its application in the workplace has shown significant innovative potential and practical value. However, the existing literature lacks a systematic and widely applicable definition and measurement framework for Generative AI Literacy. Based on the existing literature and following a rigorous scale development process, this study constructs a Generative AI Literacy measurement framework that covers five core dimensions, basic technical competence, prompt optimization, content evaluation, innovative application, and ethical and compliance awareness, and validates its reliability and validity. Furthermore, based on the Ability–Motivation–Opportunity (AMO) theory, this study explores the mechanism through which Generative AI Literacy influences employee job performance and examines the mediating role of Creative Self-Efficacy. The results show that Generative AI Literacy has a significant positive impact on job performance (β = 0.680, p < 0.001), with Creative Self-Efficacy playing a partial mediating role (indirect effect = 0.537). The developed five-dimensional framework demonstrates strong psychometric properties and provides empirical evidence for AI literacy’s role in enhancing workplace performance through Creative Self-Efficacy mechanisms. This study provides an effective measurement tool for research on the application of Generative AI Literacy in workplace settings and offers practical insights for organizations to optimize performance and promote the responsible use of AI.

The growing presence of artificial intelligence (AI) in education raises critical questions about teachers’ AI literacy and ethical reflection. This study aims to examine the effects of pre-service science and mathematics teachers’ AI literacy and their ethical use of information technologies on their ethical reflections on AI. The study, designed as a correlational survey, included 219 pre-service science and mathematics teachers from a university in Eastern Türkiye. Data were collected using three instruments: the Artificial Intelligence Literacy Scale, the Information Technologies Ethical Use Scale, and the Artificial Intelligence Ethical Reflection Scale. The findings indicate that, although pre-service teachers demonstrate high levels of knowledge in the technical and critical dimensions of AI, they remain at a moderate level in translating this knowledge into practical application and in engaging in ethical reasoning. Structural equation modelling results indicate that AI literacy significantly and directly influences ethical reflection on AI, whereas the ethical use of information technologies constitutes a weaker predictor. The mediation analysis further reveals that general ethical use of information technologies does not play a significant mediating role in the relationship between AI literacy and ethical reflection on AI. Keywords: artificial intelligence (AI) ethics, artificial intelligence (AI) literacy, information technology (IT), mathematics education, science education

With the rapid advancement of Artificial Intelligence (AI) in early childhood education (ECE), young children face the challenge of learning to use AI ethically and appropriately. Developing AI education programs requires an age- and culturally-appropriate AI literacy framework. This study addresses this fundamental gap by creating a Chinese framework for early childhood AI literacy through an expert interview study with a grounded theory approach. Seven Chinese experts, including ECE and AI professors, kindergarten principals, and Directors of ECE Information Departments, were purposely sampled and interviewed, representing scholars, policymakers, and practitioners. The synthesis of the transcribed evidence generated five dimensions of young children’s AI literacy, namely Safety, Identity, Attitude, Cognition, and Capability, collectively forming a holistic framework titled the ‘SIACC’ framework. The Chinese definition of early childhood AI literacy was also reported. This study introduces the Chinese framework of AI literacy and provides a scientific basis for policymakers to establish AI literacy standards for young children. Additionally, it offers a conceptual structure for developing systematic indicators and scales within AI literacy in ECE.

AI literacy has become crucial for preparing students navigating an AI-mediated world. This requires integrating technical principles with ethical reasoning, yet such comprehensive instructional approaches risk imposing excessive cognitive demands. Pre-training offers a theoretically grounded solution to manage cognitive demands by establishing foundational knowledge before instruction. However, the effects of pre-training in AI literacy education have received limited attention. This quasi-experimental study is on whether video-based pre-training of AI fundamentals enhances the effectiveness of game-based AI ethics instruction compared to game-based learning alone. One hundred fifty-six 7th- and 8th-grade students participated in the study. Results suggested that pre-training AI fundamentals was associated with higher AI ethics capability compared to game-based learning alone (η2ₚ = .028), though classroom-level clustering created statistical uncertainty. No significant difference was observed for expectancy-value beliefs.

The pandemic has catalyzed a significant shift to online/blended teaching and learning where teachers apply emerging technologies to enhance their students’ learning outcomes. Artificial intelligence (AI) technology has gained its popularity in online learning environments during the pandemic to assist students’ learning. However, many of these AI tools are new to teachers. They may not have rich technical knowledge to use AI educational applications to facilitate their teaching, not to mention developing students’ AI digital capabilities. As such, there is a growing need for teachers to equip themselves with adequate digital competencies so as to use and teach AI in their teaching environments. There are few existing frameworks informing teachers of necessary AI competencies. This study first explores the opportunities and challenges of employing AI systems and how they can enhance teaching, learning and assessment. Then, aligning with generic digital competency frameworks, the DigCompEdu framework and P21’s framework for twenty-first century learning were adapted and revised to accommodate AI technologies. Recommendations are proposed to support educators and researchers to promote AI education in their classrooms and academia.

The present discussion examines the transformative impact of Artificial Intelligence (AI) in educational settings, focusing on the necessity for AI literacy, prompt engineering proficiency, and enhanced critical thinking skills. The introduction of AI into education marks a significant departure from conventional teaching methods, offering personalized learning and support for diverse educational requirements, including students with special needs. However, this integration presents challenges, including the need for comprehensive educator training and curriculum adaptation to align with societal structures. AI literacy is identified as crucial, encompassing an understanding of AI technologies and their broader societal impacts. Prompt engineering is highlighted as a key skill for eliciting specific responses from AI systems, thereby enriching educational experiences and promoting critical thinking. There is detailed analysis of strategies for embedding these skills within educational curricula and pedagogical practices. This is discussed through a case-study based on a Swiss university and a narrative literature review, followed by practical suggestions of how to implement AI in the classroom.

In recent years, with the popularity of AI technologies in our everyday life, researchers have begun to discuss an emerging term “AI literacy”. However, there is a lack of review to understand how AI teaching and learning (AITL) research looks like over the past two decades to provide the research basis for AI literacy education. To summarize the empirical findings from the literature, this systematic literature review conducts a thematic and content analysis of 49 publications from 2000 to 2020 to pave the way for recent AI literacy education. The related pedagogical models, teaching tools and challenges identified help set the stage for today’s AI literacy. The results show that AITL focused more on computer science education at the university level before 2021. Teaching AI had not become popular in K-12 classrooms at that time due to a lack of age-appropriate teaching tools for scaffolding support. However, the pedagogies learnt from the review are valuable for educators to reflect how they should develop students’ AI literacy today. Educators have adopted collaborative project-based learning approaches, featuring activities like software development, problem-solving, tinkering with robots, and using game elements. However, most of the activities require programming prerequisites and are not ready to scaffold students’ AI understandings. With suitable teaching tools and pedagogical support in recent years, teaching AI shifts from technology-oriented to interdisciplinary design. Moreover, global initiatives have started to include AI literacy in the latest educational standards and strategic initiatives. These findings provide a research foundation to inform educators and researchers the growth of AI literacy education that can help them to design pedagogical strategies and curricula that use suitable technologies to better prepare students to become responsible educated citizens for today’s growing AI economy.

Generative Artificial Intelligence (GAI) has emerged as a transformative force in higher education, offering both challenges and opportunities. This paper explores the multifaceted impact of GAI on academic work, with a focus on student life and, in particular, the implications for international students. While GAI, exemplified by models like ChatGPT, has the potential to revolutionize education, concerns about academic integrity have arisen, leading to debates on the use of AI detection tools. This essay highlights the difficulties in reliably detecting AI-generated content, raising concerns about potential false accusations against students. It also discusses biases within AI models, emphasizing the need for fairness and equity in AI-based assessments with a particular emphasis on the disproportionate impact of GAI on international students, who already face biases and discrimination. It also highlights the potential for AI to mitigate some of these challenges by providing language support and accessibility features. Finally, this essay acknowledges the disruptive potential of GAI in higher education and calls for a balanced approach that addresses both the challenges and opportunities it presents by emphasizing the importance of AI literacy and ethical considerations in adopting AI technologies to ensure equitable access and positive outcomes for all students. We offer a coda to Ng et al.’s AI competency framework, mapped to the Revised Bloom’s Taxonomy, through a lens of cultural competence with AI as a means of supporting educators to use these tools equitably in their teaching.

Generative artificial intelligence provides both opportunities and challenges for higher education. Existing literature has not properly investigated how this technology would impact assessment in higher education. This scoping review took a forward-thinking approach to investigate how generative artificial intelligence transforms assessment in higher education. We used the PRISMA extension for scoping reviews to select articles for review and report the results. In the screening, we retrieved 969 articles and selected 32 empirical studies for analysis. Most of the articles were published in 2023. We used three levels—students, teachers, and institutions—to analyze the articles. Our results suggested that assessment should be transformed to cultivate students’ self-regulated learning skills, responsible learning, and integrity. To successfully transform assessment in higher education, the review suggested that (i) teacher professional development activities for assessment, AI, and digital literacy should be provided, (ii) teachers’ beliefs about human and AI assessment should be strengthened, and (iii) teachers should be innovative and holistic in their teaching to reflect the assessment transformation. Educational institutions are recommended to review and rethink their assessment policies, as well as provide more inter-disciplinary programs and teaching.

Emerging research is highlighting the importance of fostering artificial intelligence (AI) literacy among educated citizens of diverse academic backgrounds. However, what to include in such literacy programmes and how to teach literacy is still under-explored. To fill this gap, this study designed and evaluated an AI literacy programme based on a multi-dimensional conceptual framework, which developed participants' conceptual understanding, literacy, empowerment and ethical awareness. It emphasised conceptual building, highlighted project work in application development and initiated teaching ethics through application development. Thirty-six university students with diverse academic backgrounds joined and completed this programme, which included 7 hours on machine learning, 9 hours on deep learning and 14 hours on application development. Together with the project work, the results of the tests, surveys and reflective writings completed before and after these courses indicate that the programme successfully enhanced participants' conceptual understanding, literacy, empowerment and ethical awareness. The programme will be extended to include more participants, such as senior secondary school students and the general public. This study initiates a pathway to lower the barrier to entry for AI literacy and addresses a public need. It can guide and inspire future empirical and design research on fostering AI literacy among educated citizens of diverse backgrounds.