Model of Formation of Digital Competences of Future Teachers in the Conditions of Integration of E-Learning and Artificial Intelligence Technologies

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.

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

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 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 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.

The research titled “digital and AI literacy in teacher training” seeks to bolster the professional training of future educators in all phases of teacher education within Germany, with a particular emphasis on integrating digital and artificial intelligence (AI) literacy into contemporary educational practices. Recognizing the escalating importance of digital competencies—an urgency that the COVID-19 pandemic underscored globally—this initiative establishes a cohesive framework connecting universities, seminar leaders, and schools. Its core objective is to enable student teachers to adopt and implement digital methodologies in the classroom while providing continuous, contextually relevant training for in-service educators. Through this interconnected structure, the research aims to bridge educational theory and practice. Methods: The research applies a Design-Based Research (DBR) methodology, facilitating a dynamic process in which educational tools and approaches are developed, tested, and refined in real-world settings. To assess efficacy, the research utilizes online questionnaires aligned with established digital competence frameworks, such as the European DigCompEdu model, enabling educators at all stages of teacher training to self-assess their digital and AI literacy skills. The geographical context of Bavaria in southern Germany is specifically referenced, where the research pilot takes place to set a scalable example for broader implementation. Findings: Preliminary evaluations reveal that the module-based structure effectively enhances participants’ digital competencies. Teacher candidates report a higher degree of readiness to implement digital teaching tools, collaborate effectively online, and navigate AI-related resources in classroom contexts. This reflects an overall improvement in digital confidence and capability, particularly in areas like content creation and pedagogical communication. Conclusions: The research’s structured approach, fostering institutional collaboration and phased integration of digital competencies, highlights an effective model for embedding AI and digital literacy in teacher education. Continuous assessments and feedback loops ensure its relevance across training stages, enabling educators to remain adaptive and responsive to new educational technologies. Ultimately, this model may serve as a blueprint for other regions and countries aiming to update and enhance their teacher training frameworks in response to digital transformation demands.

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 />  

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

The paper presents research and preliminary findings aimed at improving curricula so that digital competencies are aligned with the required Artificial Intelligence (AI) literacy. The research was conducted at the Faculty of Technical Sciences in Čačak, University of Kragujevac (Serbia). The participants in the research were future computer science teachers and IT engineering students. The research tool for self-evaluation of AI literacy was a questionnaire based on the Serbian version of the AILS (Artificial Intelligence Literacy Scale), while digital competencies, based on the DigComp framework, were determined by objective testing. The research took into account the socioeconomic status of the students, demographic characteristics, and English language proficiency. Preliminary results indicated the persistence of significant relationships between certain digital competencies (such as programming, digital signal processing, and creative thinking) and all four constructs of AI literacy. The research findings highlight the impact of AI literacy on data analysis performance and problem solving.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science 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 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.

This study investigates the key components of teachers’ self-assessed artificial intelligence (AI) literacy competencies and how they align with existing digital literacy frameworks. The rapid development of AI technologies has highlighted the need for educators to develop AI-related skills and competencies in order to meaningfully integrate these technologies into their professional practice. A pilot study was conducted using a self-assessment questionnaire developed from frameworks such as DigiCompEdu and the Selfie for Teachers tool. The study aimed to explore the relationships between AI literacy competence and already defined digital skills and competencies through principal component analysis (PCA). The results revealed distinct components of AI literacy and digital competencies, highlighting competence overlaps in some areas, for example, digital resource management, while also confirming that AI literacy competencies form a separate and essential category. The findings show that although AI literacy aligns with other digital skills and competencies, focused attention is required to professionally develop AI-specific competencies. These insights are key elements of future research to refine and expand AI literacy tools for educators, providing targeted professional development programs to ensure that teachers are ready for the opportunities and challenges of AI in education.

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

PurposeThis paper aims to explore the intricate relationship between artificial intelligence (AI) and health information literacy (HIL), examining the rise of AI in health care, the intersection of AI and HIL and the imperative for promoting AI literacy and integrating it with HIL. By fostering collaboration, education and innovation, stakeholders can navigate the evolving health-care ecosystem with confidence and agency, ultimately improving health-care delivery and outcomes for all.Design/methodology/approachThis paper adopts a conceptual approach to explore the intricate relationship between AI and HIL, aiming to provide guidance for health-care professionals navigating the evolving landscape of AI-driven health-care delivery. The methodology used in this paper involves a synthesis of existing literature, theoretical analysis and conceptual modeling to develop insights and recommendations regarding the integration of AI literacy with HIL.FindingsImpact of AI on health-care delivery: The integration of AI technologies in health-care is reshaping the industry, offering unparalleled opportunities for improving patient care, optimizing clinical workflows and advancing medical research. Significance of HIL: HIL, encompassing the ability to access, understand and critically evaluate health information, is crucial in the context of AI-driven health-care delivery. It empowers health-care professionals, patients and the broader community to make informed decisions about their health and well-being. Intersection of AI and HIL: The convergence of AI and HIL represents a critical juncture, where technological innovation intersects with human cognition. AI technologies have the potential to revolutionize how health information is generated, disseminated and interpreted, necessitating a deeper understanding of their implications for HIL. Challenges and opportunities: While AI holds tremendous promise for enhancing health-care outcomes, it also introduces new challenges and complexities for individuals navigating the vast landscape of health information. Issues such as algorithmic bias, transparency and accountability pose ethical dilemmas that impact individuals’ ability to critically evaluate and interpret AI-generated health information. Recommendations for health-care professionals: Health-care professionals are encouraged to adopt strategies such as staying informed about developments in AI, continuous education and training in AI literacy, fostering interdisciplinary collaboration and advocating for policies that promote ethical AI practices.Practical implicationsTo enhance AI literacy and integrate it with HIL, health-care professionals are encouraged to adopt several key strategies. First, staying abreast of developments in AI technologies and their applications in health care is essential. This entails actively engaging with conferences, workshops and publications focused on AI in health care and participating in professional networks dedicated to AI and health-care innovation. Second, continuous education and training are paramount for developing critical thinking skills and ethical awareness in evaluating AI-driven health information (Alowais et al., 2023). Health-care organizations should provide opportunities for ongoing professional development in AI literacy, including workshops, online courses and simulation exercises focused on AI applications in clinical practice and research.Originality/valueThis paper lies in its exploration of the intersection between AI and HIL, offering insights into the evolving health-care landscape. It innovatively synthesizes existing literature, proposes strategies for integrating AI literacy with HIL and provides guidance for health-care professionals to navigate the complexities of AI-driven health-care delivery. By addressing the transformative potential of AI while emphasizing the importance of promoting critical thinking skills and ethical awareness, this paper contributes to advancing understanding in the field and promoting informed decision-making in an increasingly digital health-care environment.