Conceptualizing AI literacy: An exploratory review

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

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

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.

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.

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.

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 />  

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) 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) 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.

This study explores variables associated with teachers’ Artificial Intelligence (AI) literacy, a key competency for effective and responsible AI integration in education. A total of 270 teachers completed an online survey including measures of AI literacy, AI acceptance, computational thinking, AI anxiety, and digital divide. Results revealed that all AI acceptance variables were positively associated with AI literacy, with hedonic motivation and willingness to use AI emerging as the strongest predictors. Computational thinking, AI anxiety, and digital divide also showed significant associations with AI literacy. The findings highlight the central role of teachers’ attitudes and motivational variables over technical and demographic variables. The study contributes to the understanding of how teachers engage with AI technologies and provides practical implications for designing professional development programs that enhance AI literacy and reduce barriers to AI adoption in educational contexts.

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.

Teachers’ artificial intelligence (AI) literacy is a critical competency for building effective interactive learning environments, yet a substantial gap persists between organizational support and teachers’ individual capacities. Drawing on the Technology–Organization–Environment (TOE) framework, this study employs structural equation modeling to analyze survey data from 10,683 primary and secondary school teachers in Shanghai, China, examining the mechanism through which organizational support enhances teachers’ AI literacy via innovation capability and technology acceptance. The findings reveal that organizational support exerts both a significant direct effect on AI literacy (18.2% of total effect) and substantial indirect effects through innovation capability (22.4%) and technology acceptance (22.8%). Most importantly, innovation capability and technology acceptance jointly form a chain mediation pathway accounting for 36.6% of the total effect, revealing a complete mechanism: organizational support → innovation capability → technology acceptance → AI literacy. This study is the first to clarify this chain transmission mechanism, offering a new theoretical perspective for understanding how organizational factors systematically promote teachers’ AI literacy. Based on these findings, a four-level collaborative framework—institutional support, resource coordination, mechanism innovation, and agent empowerment—is proposed to provide actionable strategies for enhancing teachers’ AI literacy and optimizing interactive learning environments.

To determine the relationship between nursing managers' diverse leadership styles and nurses' artificial intelligence (AI) literacy. Nurses' AI literacy serves as a core competency for optimizing clinical workflows and safeguarding patient safety. Although leadership has been recognized as a critical factor in the adoption of technology, empirical evidence linking specific leadership styles to AI literacy among nurses remains unclear. A cross-sectional study was conducted between October and November 2025, involving 1644 nurses recruited from 15 general tertiary hospitals across Sichuan, Jilin, Tibet, Hunan, and Hubei Provinces in China. Data were collected using five standardized instruments: a demographic information form, the AI Literacy Scale, the Multifactor Leadership Questionnaire (MLQ), the Authentic Leadership Questionnaire (ALQ), and the Servant Leadership Questionnaire (SLQ). The univariate analysis, Pearson correlation analysis, and multiple linear regression analysis were employed to examine the relationships among the study variables. Univariate analysis revealed significant differences in nurses' AI literacy associated with work experience, received AI-related training, and prior experience in using AI (all p < 0.05). Four leadership styles (transformational, transactional, authentic, and servant leadership) were observed to be positively correlated with AI literacy (r = 0.184-0.378, p < 0.001). Multiple linear regression identified five significant predictors of AI literacy: received AI-related training (β = 0.147, p < 0.001), prior experience in using AI (β = 0.131, p < 0.001), transformational leadership (β = 0.163, p < 0.001), transactional leadership (β = 0.073, p = 0.037), and authentic leadership (β = 0.113, p = 0.015), with the model explaining 20.2% of variance. Our study demonstrated that transformational, transactional, and authentic leadership in nursing managers were positively associated with nurses' AI literacy, whereas servant leadership showed no significant predictive effect. These findings highlight the multidimensional nature of leadership in fostering AI literacy among nurses and inform the strategic design of targeted leadership development initiatives within healthcare organizations.