Artificial intelligence regulation matures: Landscapes of the USA, European Union, and China

Purpose The purpose of this paper is to introduce the artificial intelligence (AI) Citizenship Framework, a model that equips teachers and school library professionals with the tools to develop AI literacy and citizenship in students. As AI becomes increasingly prevalent, it is essential to prepare students for an AI-driven future. The framework aims to foster foundational knowledge of AI, critical thinking and ethical decision-making, empowering students to engage responsibly with AI technologies. By providing a structured approach to AI literacy, the framework helps educators integrate AI concepts into their lessons, ensuring students develop the skills needed to navigate and contribute to an AI-driven society. Design/methodology/approach This paper presents a theoretical framework, developed from the author’s experience as an information and digital literacy coach and teacher librarian across Asia, the Middle East and Europe. The AI Citizenship Framework was created without following specific empirical methodologies, drawing instead on practical insights and educational needs observed in diverse contexts. It outlines a scope and sequence for integrating AI literacy into school curricula. The framework’s components build on existing pedagogical practices while emphasising critical, ethical and responsible AI engagement. By providing a structure for AI education, it serves as a practical resource for school librarians and educators. Findings While no empirical data was collected for this theoretical paper, the AI Citizenship Framework offers a structured approach for school librarians and educators to introduce and develop AI literacy. It has the potential to influence AI education by fostering critical and ethical awareness among students, empowering them to participate responsibly in an AI-driven world. The framework’s practical application can be expanded beyond school librarians to include classroom teachers, offering a comprehensive model adaptable to various educational settings. Its real-world implementation could enhance students’ readiness to engage with AI technologies, providing long-term benefits for both educational institutions and the broader society. Research limitations/implications One limitation of the AI Citizenship Framework is that it has not yet been empirically validated. Future research could focus on testing its practical effectiveness in real-world settings, offering insights that may inform refinements and adaptations to better support school librarians and educators in fostering AI literacy and AI citizenship. Practical implications The practical implication of the AI Citizenship Framework is its application in educational settings to equip students with AI literacy and responsible citizenship skills. School library professionals and teachers can use the framework to integrate AI concepts into curricula, fostering critical thinking, ethical understanding and informed decision-making about AI technologies. The framework provides ready-to-use curriculum plans, enabling educators to prepare students for an AI-driven world. Its adaptability also allows classroom teachers to lead AI literacy initiatives, making it a versatile tool for embedding AI education across subjects and promoting responsible use and engagement with AI technologies in real-world contexts. Originality/value The originality and value of the AI Citizenship Framework lie in its approach to integrate AI literacy into educational contexts, specifically tailored for teacher librarians and school librarians. To the best of the authors’ knowledge, it is the first framework that comprehensively addresses the need for AI literacy from an ethical, critical and societal perspective, while also promoting active participation and leadership in AI governance. The framework equips educators with practical tools and curriculum plans, fostering responsible AI use and engagement. Its adaptable structure ensures it can be implemented by classroom teachers as well, adding significant value to AI education across disciplines and age groups.

This study investigates the evolving landscape of Artificial Intelligence (AI) literacy, acknowledging AI's transformative impact across various sectors in the twenty-first century. Starting from AI's inception to its current pervasive role in education, everyday life, and beyond, this paper explores the relevance and complexity of AI literacy in the modern world. To evaluate the current state of the literature on AI literacy, a systematic literature review was conducted with the objective of identifying thematic and recent research trends. Through a rigorous selection process involving 323 records from databases such as Web of Science, SCOPUS, ERIC, and IEEE Xplore, 87 high-quality studies have been analysed to identify central themes and definitions related to AI literacy. Our findings reveal that AI literacy extends beyond technical proficiency to encompass ethical considerations, societal impacts, and practical applications. Key themes identified include the ethical and social implications of AI, AI literacy in K-12 education, AI literacy curriculum development, and the integration of AI in education and workplaces. The study also highlights the importance of AI literacy models and frameworks for structuring education across diverse learning environments, as well as the significance of AI and digital interaction literacy. Additionally, our analysis of publication trends indicates a strong growth in AI literacy research, particularly in China and the United States, reflecting the global urgency of addressing AI literacy in policy and education. Conclusively, the research underscores the importance of an adaptable, comprehensive educational paradigm that incorporates AI literacy, reflecting its diverse interpretations and the dynamic nature of AI. The study advocates for interdisciplinary collaboration in developing AI literacy programs, emphasizing the need to equip future generations with the knowledge, skills, and ethical discernment to navigate an increasingly AI-driven world.

A critical review of teaching and learning artificial intelligence (AI) literacy: Developing an intelligence-based AI literacy framework for primary school education

Africa as a continent recognizes the need for Artificial Intelligence (AI) governance frameworks. However, despite initiatives like the African Union's Continental AI Strategy, African nations grappled with challenges in formulating comprehensive AI policies, while established global frameworks provided advanced benchmarks for comparison. To address this disparity, this study conducted a comparative analysis of AI governance in Africa relative to global standards and practices. Employing a comprehensive document analysis methodology, the research examined key policy documents, strategic frameworks, and regulatory guidelines across African, European, American, and Asian contexts. The findings revealed that while the African Union demonstrated commitment to coordinated AI governance, African approaches generally lagged behind global benchmarks in comprehensiveness, formalization, and ethical integration. The study identified a notable fragmentation in AI governance across African nations, contrasting with more unified approaches in other regions. African frameworks emphasized leveraging AI for socio-economic development, diverging from the risk mitigation focus seen in EU regulations. The integration of indigenous African ethical perspectives in AI governance frameworks was limited, presenting both challenges and opportunities. Significant disparities in digital infrastructure and AI capacity between Africa and other regions were found to affect governance implementation. The study concluded that despite these challenges, there was potential for Africa to develop innovative, context-specific AI governance models that could contribute valuable insights to the global discourse on responsible AI development. Recommendations included accelerating the implementation of the Continental AI Strategy, prioritizing investment in digital infrastructure, developing Africa-centric AI ethics frameworks, establishing mechanisms for regular benchmarking against global standards, fostering increased collaboration, and implementing AI literacy programs across the continent.

BACKGROUND Artificial intelligence (AI) literacy is increasingly essential for medical students. However, without systematic characterization of the subsidiary components and relevant drivers, designing targeted medical education interventions may be challenging. OBJECTIVE Systematically describe (1) the levels of and (2) the drivers of multidimensional AI literacy among Chinese medical students. METHODS A cross-sectional, descriptive analysis was conducted using data from a nationwide survey of Chinese medical students (n = 80,335) across 109 medical schools in 2024. AI literacy was assessed with a multidimensional instrument comprising three domains: knowledge, evaluating students’ self-reported proficiency in core areas of medical AI applications; attitude, reflecting their views on using AI for teaching and learning; and behavior, capturing the frequency and patterns of AI use. Factors associated with AI literacy included individual factors (i.e., demographic characteristics, family background, and enrollment motivation) and environmental factors (i.e., educational phase, type of education program, and tier of education program). RESULTS Respondents showed moderate to high levels of AI knowledge (mean, 76.0 [SD, 26.9]), followed by moderate AI attitude scores (mean, 71.6 [SD, 24.4]). In contrast, AI behavior scores were much lower (mean, 32.5 [SD, 28.5]), indicating little usage of AI tools. Of the individual factors, male students reported higher levels of AI attitude and behavior; both intrinsic and extrinsic motivation were positively associated with all three dimensions; advantaged family background was positively related to AI attitude and behavior, but not knowledge. Among the environmental factors, attending prestigious Double First-Class universities was positively associated with higher AI usage. Enrollment in long-track medical education programs was associated with higher AI attitude and behavior, while being in the clinical phase was negatively associated with both AI knowledge and behavior. Environmental factors moderated the associations between individual characteristics and AI literacy, potentially attenuating disparities. CONCLUSIONS Medical students reported moderate to high AI knowledge, moderate AI favorability, and low AI use. Individual characteristics and environmental factors were significantly associated with AI literacy, and environmental factors moderated the associations. The moderate AI literacy overall highlights the need for AI-related medical education, ideally with practical use and nuanced by drivers of inequitable distribution. CLINICALTRIAL This study is a cross-sectional observational analysis and does not involve a clinical trial; therefore, trial registration is not applicable.

This study aims to develop a comprehensive competency framework for artificial intelligence (AI) literacy, delineating essential competencies and sub‐competencies. This framework and its potential variations, tailored to different learner groups (by educational level and discipline), can serve as a crucial reference for designing and implementing AI curricula. However, the research on AI literacy by target learners is still in its infancy, and the findings of several existing studies provide inconsistent guidelines for educational practices. Following the 2020 PRISMA guidelines, we searched the Web of Science, Scopus, and ScienceDirect databases to identify relevant studies published between January 2012 and October 2024. The quality of the included studies was evaluated using QualSyst. A total of 29 studies were identified, and their research findings were synthesized. Results show that at the K‐12 level, the required competencies include basic AI knowledge, device usage, and AI ethics. For higher education, the focus shifts to understanding data and algorithms, problem‐solving, and career‐related competencies. For general workforce, emphasis is placed on the interpretation and utilization of data and AI tools for specific careers, along with error detection and AI‐based decision‐making. This study connects the progression of specific learning objectives, which should be intensively addressed at each stage, to propose an AI literacy education pathway. We discuss the findings, potentials, and limitations of the derived competency framework for AI literacy, including its theoretical and practical implications and future research suggestions. Practitioner notesWhat is already known about this topic AI literacy is becoming increasingly important as AI technologies are integrated into various aspects of life and work. Research on AI literacy competencies across diverse learner groups and disciplines remains fragmented and inconsistent to guide educational practices. Studies providing a coherent pathway for AI literacy development throughout educational and working life are lacking. What this paper adds A comprehensive AI literacy competency framework consisting of 8 competencies and 18 sub‐competencies. Variations in AI literacy competencies with tailored configuration and prioritization across different learner groups by school levels and disciplines. A proposed pathway for developing AI literacy from K‐12 to higher education and workforce levels. Implications for practice and policy The framework can guide the design and implementation of AI curricula tailored to different learner characteristics and needs. Education should shift focus from teaching how to use AI to fostering competencies for critical, strategic, responsible and ethical integration of AI. Policies are needed to support a systematic pathway for lifelong AI literacy development from K‐12 education to workforce training.

In the same way that smart technology is boosting growth in many different sectors, artificial intelligence (AI) is becoming a major factor boosting change and innovation in the educational system. Improving one's AI literacy and learning how to effectively incorporate AI into the classroom are now essential objectives for educators seeking long-term success in their careers. In an effort to increase the efficacy of classroom instruction and the widespread use of AI, this study investigates the relationships between several aspects of teachers' AI literacy. Our research is based on an examination of 280 survey responses that assessed instructors' AI literacy in four areas: AI understanding and knowledge, AI application, AI evaluation, and AI ethics. All three of these other variables were positively and significantly impacted by AI Application (AAI). The findings suggest that the government should support initiatives that increase educators' knowledge of artificial intelligence. Making AI literacy a crucial enabler for teachers' sustainable future development requires a broad curriculum, material, techniques, and practical support for special training that aims to promote teachers' AI literacy.

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. Practitioner notesWhat is already known about this topic AI literacy has drawn increasing attention in recent years and has been identified as an important digital literacy. Schools and universities around the world started to incorporate AI into their curriculum to foster young learners' AI literacy. Some studies have worked to design suitable measurement tools, especially questionnaires, to examine students' learning outcomes in AI learning programmes. What this paper adds Develops an AI literacy questionnaire (AILQ) to evaluate students' literacy development in terms of affective, behavioural, cognitive and ethical (ABCE) dimensions. Proposes a parsimonious model based on the ABCE framework and addresses a skill set of AI literacy. Implications for practice and/or policy Researchers are able to use the AILQ as a guide to measure students' AI literacy. Practitioners are able to use the AILQ to assess students' AI literacy development.

This poster showcases a case study of an Australian higher education institution’s artificial intelligence (AI) literacy staff development program. It offers practical suggestions to ASCILITE attendees on how to empower academic and professional staff to navigate the unknown terrain of generative AI collaboratively and responsibly. Since the release of ChatGPT in November 2022, higher education institutions have been grappling with its impact on assessment, teaching and learning, and the world of work (CRADLE Blog, 2023) -culminating in the Tertiary Education Quality and Standards Agency (TEQSA) Request for Information (RFI) about how institutions will engage with AI and secure course integrity (TEQSA, 2024). Effective institutional responses to TEQSA’s RFI are predicated on staff at all levels rapidly developing their AI literacy in order to conceptualise and implement the curriculum and assessment changes required. AI literacy is generally accepted to include understanding of AI tools and how they work, discussion of ethical and societal implications and critical evaluation of their outputs, and competency in integration of AI ethically and effectively into daily practice (Chan & Colloton, 2024; Hibbert, Melanie et al., 2024; Hillier, 2023). This poses a significant challenge for institutions because of rapidly evolving AI tools and the diverse capabilities and starting points of large staff cohorts, including among third space support staff responsible for implementation. ECU's evolving strategy for building organisational capacity in AI literacy is outlined in this poster. The approach, which aligns with ECU’s Framework and Guidelines for Ethical and Productive Use of AI (Edith Cowan University, 2023), is designed to empower and enable staff. It intentionally incorporates connectivist and constructivist learning theories, informed by Fink's Taxonomy of Significant Learning (Fink, 2013) and Miller's Pyramid (Miller, 1990). This meant (a) providing essential foundational knowledge about AI, (b) developing practical skills through hands-on experience and exploration, and (c) fostering collective capability through sharing and collaboration. These efforts complemented initiatives to support student AI literacy through similar impactful interventions (Sullivan et al., 2024). In 2024, ECU implemented the following activities to support academic and professional staff: “AI 101” Canvas site: Covers how AI works, ethical and societal considerations, and AI in learning and teaching. “Explore AI” workshops: Focused on practical exploration of AI tools that generate both text and images, as well as ethics, research and assessment. “AI Digest” Viva Engage Community: Provides regular updates about AI. Generative AI tools: A series of tools for trials e.g., custom chatbots and image generators. Workshops co-designed with Schools: Explores generative AI in discipline-specific ways (including arts, humanities, business, law and performing arts) Despite currently being voluntary, these initiatives have received strong engagement and positive feedback to date. For example, all respondents to the Explore AI Session feedback forms said they would recommend the sessions to colleagues. 331 academic and professional staff have engaged with the AI 101 Canvas site so far, spending a median of 3 hours and 5 minutes in the course. 74% of the 50 respondents to the AI 101 evaluation form stated that their confidence levels improved after completing the course. ECU continues to iteratively improve its AI literacy offerings and expand staff engagement, collectively making sense of generative AI and its effects as an institution.

Artificial intelligence (AI) literacy is an indispensable skill in the Fourth Industrial Revolution. Many countries and institutions have cultivated specialty and basic AI literacy at various levels to ensure a competitive edge in AI and related fields. Many universities have followed this trend but often suffer from an unsuitable curriculum for AI literacy. This questionnaire-based study determines whether a distinguishable difference in perspective regarding AI literacy education exists in students of various majors. Additionally, this study investigates which majors are more positive, interested, and demanding regarding university AI liberal arts classes to design an effective AI literacy curriculum. The participants are 452 nonscientific or nonengineering undergraduate students who took the 15-week AI liberal arts class at a university in Seoul, Korea, in 2021. The survey was conducted at the end of the semester. The analysis demonstrated that different perspectives exist for various majors and that students in business or economics majors were more interested and positive concerning AI education than those in other majors. Students with arts or physical education majors were least interested in AI literacy. These results highlight the need to design an AI literacy curriculum considering major-specific characteristics to enhance education and student satisfaction with AI education. Keywords: Artificial Intelligence (AI) literacy, AI education, different perspectives, Artificial Intelligence Education Platforms, Education Policy

Artificial intelligence (AI) governance is required to reap the benefits and manage the risks brought by AI systems. This means that ethical principles, such as fairness, need to be translated into practicable AI governance processes. A concise AI governance definition would allow researchers and practitioners to identify the constituent parts of the complex problem of translating AI ethics into practice. However, there have been few efforts to define AI governance thus far. To bridge this gap, this paper defines AI governance at the organizational level. Moreover, we delineate how AI governance enters into a governance landscape with numerous governance areas, such as corporate governance, information technology (IT) governance, and data governance. Therefore, we position AI governance as part of an organization’s governance structure in relation to these existing governance areas. Our definition and positioning of organizational AI governance paves the way for crafting AI governance frameworks and offers a stepping stone on the pathway toward governed AI.

Artificial intelligence (AI) literacy has recently emerged on the educational agenda raising expectations on teachers’ and teacher educators’ professional knowledge. This scoping review examines how the scientific literature conceptualises AI literacy in relation to teachers’ different forms of professional knowledge relevant for Teacher Education (TE). The search strategy included papers and proceedings from 2000 to 2023 related to AI literacy and TE as well as the intersection of AI and teaching. Thirty-four papers were included in the analysis. The Aristotelian concepts episteme (theoretical-scientific knowledge), techne (practical-productive knowledge), and phronesis (professional judgement) were used as a lens to capture implicit and explicit dimensions of teachers’ professional knowledge. Results indicate that AI literacy is a globally emerging research topic in education but almost absent in the context of TE. The literature covers many different topics and draws on different methodological approaches. Computer science and exploratory teaching approaches influence the type of epistemic, practical, and ethical knowledge. Currently, teachers’ professional knowledge is not broadly addressed or captured in the research. Questions of ethics are predominantly addressed as a matter of understanding technical configurations of data-driven AI technologies. Teachers’ practical knowledge tends to translate into the adoption of digital resources for teaching about AI or the integration of AI EdTech into teaching. By identifying several research gaps, particularly concerning teachers' practical and ethical knowledge, this paper adds to a more comprehensive understanding of AI literacy in teaching and can contribute to a more well-informed AI literacy education in TE as well as laying the ground for future research related to teachers’ professional knowledge.

How much do citizens support artificial intelligence (AI) in government and politics at different levels of decision‐making authority and to what extent is this AI support associated with citizens’ conceptions of democracy? Using original survey data from Germany, the analysis shows that people are overall sceptical toward using AI in the political realm. The findings suggest that how much citizens endorse democracy as liberal democracy as opposed to several of its disfigurations matters for AI support, but only in high‐level politics. While a stronger commitment to liberal democracy is linked to lower support for AI, the findings contradict the idea that a technocratic notion of democracy lies behind greater acceptance of political AI uses. Acceptance is higher only among those holding reductionist conceptions of democracy which embody the idea that whatever works to accommodate people's views and preferences is fine. Populists, in turn, appear to be against AI in political decision making.

Abstract: Artificial Intelligence (AI) is increasingly influencing various aspects of daily life, including education and digital services. However, disparities in AI literacy among different generational groups pose challenges to equitable technological adaptation. This study explores how preschool children and elderly individuals in Bulgaria perceive and engage with AI technologies, identifying key challenges and policy gaps affecting their AI literacy. Based on qualitative expert interviews, the research examines public awareness, accessibility, and the effectiveness of AI-related policies in fostering digital inclusion. The findings reveal that preschool children frequently interact with AI-driven educational tools but lack structured learning frameworks to develop critical AI literacy. Conversely, the elderly face significant barriers to AI adoption due to low digital literacy, technological anxiety, and usability constraints. Current Bulgarian policies emphasize on economic and industrial applications of AI while neglecting age-specific educational strategies. This research underscores the need for comprehensive AI literacy programs tailored to both children and the elderly to mitigate risks such as digital misinformation and social exclusion. The findings will be beneficial for policymakers, educators, and technology developers aiming to create inclusive AI education initiatives and to bridge generational digital divides. Future research should explore quantitative assessments of AI literacy levels and investigate best practices in AI education across Eastern Europe in order to compare more inclusive policy frameworks in the region. Keywords: Artificial Intelligence (AI), Digital Literacy, Media Education, Preschool Children, Elderly Individuals, Educational Technology, AI Governance.

The issue of Artificial Intelligence (AI) literacy is gaining popularity in the field of education. Most research on AI literacy has focused on primary, secondary, and higher education, and there has been limited examination of AI literacy programs in early childhood education. This study aimed to evaluate the impact of an eight-week AI literacy program on young children’s AI literacy, AI-related creativity, and their perceptions of the AI4KG program. A total of 26 young children (average age of 4 years) enrolled in the program at a kindergarten in Hong Kong were the subjects of the study. The results indicated that (1) young children were capable of learning basic AI concepts and knowledge; (2) in terms of AI-related creativity, younger children were able to design a chatting robot via imagination, while older children created an AI robot to assist people in their drawings; (3) in regards to the children's perceptions of the AI4KG program, older children who enjoyed the activity were able to train AI, while younger children preferred to draw the future AI city and participate in the AI story activity. This study highlights the positive benefits of AI literacy education in preparing young children for an AI-driven future.