Teachers’ artificial intelligence (AI) literacy: an exploratory study

Nursing students' artificial intelligence (AI) literacy, AI self-efficacy and AI self-competency: A cross-sectional design and structural equation model analysis.

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

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.

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 notes What 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.

This study aims to examine the relationships among university students’ artificial intelligence (AI) literacy, AI ethical awareness, and technology use, and to determine the mediating role of AI ethical awareness in this relationship. The sample of the study consisted of 438 university students in Kazakhstan (233 female, 205 male). Data were collected using the AI literacy scale, AI ethical awareness scale, and technology use scale. Pearson correlation analysis, <i>independent samples t-test, one-way analysis of variance,</i> and mediation analysis with<i> PROCESS macro (version 4.2) </i>were employed for data analysis. The findings revealed that male students scored significantly higher than female students in AI ethical awareness and technology use according to the gender variable. Significant differences were found among age groups in terms of AI ethical awareness and technology use, with students aged 27 and above obtaining the highest scores. Regarding the field of study variable, social sciences students had the highest means in AI ethical awareness and technology use, whereas health sciences students demonstrated the lowest scores. The results indicated positive and significant relationships among AI literacy, AI ethical awareness, and technology use. Mediation analysis results revealed that AI ethical awareness played a partial mediating role in the effect of technology use on AI literacy. Technology use had both direct and indirect effects on AI literacy through AI ethical awareness. In conclusion, this study demonstrated that technology use influences AI literacy both directly and indirectly through the development of ethical awareness. The findings suggest that AI literacy education in higher education institutions should be designed with holistic approaches that incorporate ethical dimensions alongside technical content.<br />  

Artificial intelligence (AI) and algorithmic systems influence health workers' access, interpretation, and action on clinical and public health information, positioning them as intermediaries between algorithmically mediated outputs and patients, communities, and decision makers. This study examines how AI and algorithmic literacy are conceptualized and measured among health workers through a digital health literacy (DHL) lens. Using Arksey and O'Malley's scoping review framework, we searched Ovid MEDLINE, Ovid Embase, Scopus, IEEE Xplore, ACM Digital Library, Europe PMC, and arXiv for English language sources published between January 2020 and May 2025. Two reviewers screened records and extracted data using a theory informed charting framework grounded in Nutbeam's model (functional: basic understanding and use; critical: evaluation and ethics; communicative: interacting with AI systems and explaining AI-mediated information). We synthesized findings using descriptive statistics and a narrative synthesis. Twelve studies published between 2021 and 2025 met inclusion criteria. Evidence was concentrated in health professions education (10/12), primarily among medical (6/12) and nursing students (2/12), with no studies exploring public health practice. Explicit, theory-grounded definitions of AI literacy were uncommon, and links to DHL were only implied. AI literacy was frequently operationalized through self-reported instruments, commonly the Artificial Intelligence Literacy Scale (AILS; 3 studies), Meta Artificial Intelligence Literacy Scale (MAILS; 2 studies) and the Scale for the Assessment of Non-Experts' AI Literacy (SNAIL), alongside self-developed tools. Only one study explicitly defined and measured algorithmic literacy as a distinct construct; in other studies, algorithmic considerations appeared indirectly through recognizing AI presence in systems or evaluating AI generated content. Across studies, competencies aligned mainly with functional and critical dimensions of DHL, particularly awareness, use, evaluation, and ethics, while communicative literacies were infrequently assessed. AI and algorithmic literacy among health workers is underdeveloped, weakly integrated with digital health literacy, and inconsistently measured. Research prioritizes AI literacy using non-health-specific self-report tools and largely overlooks communicative competencies essential to clinical and public health practice. These findings point to the need for clearer conceptual alignment, health-specific measurement, and systems-based approaches to workforce readiness as AI-enabled tools expand across healthcare and public health.

BackgroundAI technology has had a significant revolutionary impact on the fields of healthcare and education. For the nursing staff population, the lack of artificial intelligence (AI) literacy may not only weaken the construction of their professional self‐concept but also constrain the development of evidence‐based practice. However, empirical research on the intrinsic correlation mechanism between these three factors is still relatively scarce at present.AimThe purpose of this study was to explore the mediating role of AI literacy in the relationship between nurses’ professional self‐concept and evidence‐based practice.MethodsA cross‐sectional study was conducted from October 15 to November 1, 2025, using convenience sampling to select 497 nurses from four tertiary public hospitals in Chongqing. The data collection tools include participant demographic characteristics, AI Literacy Scale (AILS), Nurse Self‐Concept Questionnaire (NSCQ), and Evidence‐Based Practice Questionnaire (EBPQ). The statistical software R (version 4.5.2) was adopted for data analysis, which included data feature description, correlation verification, and structural equation modeling.ResultsThe overall demographic characteristics of the respondents were characterized by high educational levels, a mix of middle‐aged and young people, and extensive work experience. The average scores for professional self‐concept, AI literacy, and evidence‐based practice were 232.29 ± 42.57, 63.34 ± 10.14, and 143.05 ± 22.45, respectively. It was found that a positive relationship exists between nurse AI literacy and professional self‐concept (r = 0.89, p < 0.001), as well as between nurse professional self‐concept and evidence‐based practice (r = 0.94, p < 0.001). A significant positive correlation has also been found between AI literacy and evidence‐based practice (r = 0.92, p < 0.001). AI literacy played a partial mediating role between nurse professional self‐concept and evidence‐based practice, with a mediation effect value of 0.587 (95% CI: 0.569–0.606), which explained 38.5% of the total effect.ConclusionThe study confirmed that there was a positive relationship between nurse professional self‐concept and evidence‐based practice, and AI literacy played a partial mediating effect in this relationship chain. It can be seen that AI literacy plays an indispensable and critical role in promoting the shape of nurse professional self‐concept and enhancing their evidence‐based practice ability.Implications for Nursing ManagementImproving the AI literacy of nurses and conducting precise training are fundamental tasks in promoting the effective empowerment of clinical nursing scenarios with AI technology. To this end, it is necessary to integrate knowledge and skills related to AI into the nursing education system and simultaneously promote the lifelong professional development of nurses to effectively enhance their ability to use AI technology to optimize medical services. At the same time, healthcare institutions and nursing managers should focus on building supportive practice environments, advocating for standardized clinical applications of AI technology, and always adhering to the nursing core values guided by patient needs.Trial Registration: Chinese Clinical Trial Registry: ChiCTR2600118905

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.

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 notes What 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.

Many researchers have started using artificial intelligence (AI) tools in the different parts of their scientific research processes. A high level of AI literacy is required to utilize AI tools effectively. AI literacy also includes many cognitive and technical skills. These pre-requisite skills can impact researchers’ AI usage competencies. In this context, the current study aimed to investigate the determinants of researchers’ AI literacy from the demographic variables and twenty-first century skills. For this purpose, a model was created with 24 hypotheses and tested using data collected by 708 researchers from various universities in Türkiye. Non-experts AI literacy, digital literacy, data literacy, and computational thinking scales, and demographic information form were used as data collection tools. Structural Equation Modelling (SEM) was used to reveal the relationship among the variables. The results revealed that digital literacy and data literacy skills are the strongest predictors of AI literacy. In addition, digital literacy and data literacy have mediating roles between the some 21st skills and AI literacy. There are also some demographic variables such as English language level and frequency of AI use, which are the significant predictors of AI literacy.

This scoping review explores the field of artificial intelligence (AI) literacy, focusing on the tools available for evaluating individuals’ self-perception of their AI literacy. In an era where AI technologies increasingly infiltrate various aspect of daily life, from healthcare diagnostics to personalized digital platforms, the need for a comprehensive understanding of AI literacy has never been more critical. This literacy extends beyond mere technical competence to include ethical considerations, critical thinking, and socio-emotional skills, reflecting the complex interplay between AI technologies and societal norms. The review synthesizes findings from diverse studies, highlighting the development and validation processes of several key instruments designed to measure AI literacy across different dimensions. These tools – ranging from the Artificial Intelligence Literacy Questionnaire (AILQ) to the General Attitudes towards Artificial Intelligence Scale (GAAIS) – embody the nature of AI literacy, encompassing affective, behavioral, cognitive, and ethical components. Each instrument offers unique insights into how individuals perceive their abilities to understand, engage with, and ethically apply AI technologies. By examining these assessment tools, the review sheds light on the current landscape of AI literacy measurement, underscoring the importance of self-perception in educational strategies, personal growth, and ethical decision-making. The findings suggest a critical need for educational interventions and policy formulations that address the gaps between perceived and actual AI literacy, promoting a more inclusive, critically aware, and competent engagement with AI technologies.

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.

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.

Artificial intelligence (AI) transforms healthcare data collection, analysis, and application, making AI proficiency a growing necessity across health professions.ObjectiveThis study aimed to examine the influence of demographic factors on AI literacy among Health Information (HI) professionals, identify key knowledge gaps and inform workforce-aligned training recommendations. This mixed-methods study analysed convenience-sampled survey data on AI literacy among HI professionals. Quantitative responses were examined with descriptive statistics, t-tests, Analysis of Variance (ANOVA), Spearman rank-order correlation, linear regression, geospatial analysis and a random forest to examine AI knowledge across demographic groups. The one qualitative open-ended response was analysed with latent Dirichlet allocation (LDA) topic modelling to identify themes.ResultsA total of 128 valid responses were analysed, including 22 participants who completed the technical knowledge section and 48 who responded to the open-ended question on AI education. Higher educational attainment and geographic location significantly predicted greater general AI literacy. However, no significant associations were found between AI literacy (general or technical) and age group, possession of non-health informatics credentials or prior AI experience. The cross-validated Random Forest models were assessed with and without oversampling. Accuracy was identical across both models (0.95), indicating that the overall prediction correctness of low versus high AI literacy was not affected by oversampling. The oversampled model had a superior ability to detect the minority class, making it more suitable for imbalanced classification tasks where recall is critical. This study identified several important knowledge gaps on the influence of demographic factors on AI literacy, which informs workforce-aligned training recommendations. These findings underscore the need for competency-based education to strengthen AI readiness within the health information workplace.Implications for health information management practice:The thematic analysis demonstrated the urgent need for AI knowledge, training and literacy for HI professionals and students. Themes from the LDA topic modelling informed the development of AI educational frameworks, structured into domains, subdomains and specific components of educational competencies. With multidisciplinary collaboration and further research, standardised AI core competencies for HI professionals could be created, validated by experts and adopted across educational programs to improve AI literacy in the HI field.

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.