Digital and artificial intelligence literacy in inflammatory rheumatic and degenerative joint diseases.

Higher artificial intelligence literacy among patients with fibromyalgia syndrome: Results from a cross-sectional survey study.

The effects of e-Health and artificial intelligence literacy levels on disease self-management in patients with diabetes.

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.

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.

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.

Background:The chronic nature of rheumatic diseases leads to a negative impact on the quality of life. The upper limb is the target of multiple inflammatory and non-inflammatory rheumatic diseases ranging...

Background:Selenoprotein P (SELENOP) and glutathione peroxidase 3 (GPx) are important selenoproteins that transport selenium in body (SELENOP) and exhibit antioxidative effects [1]. Selenium deficiency can be associated with the development...

To standardize the histopathological assessment of synovial membrane specimens in order to contribute to the diagnostics of rheumatic and non-rheumatic joint diseases. Three features of chronic synovitis (enlargement of lining cell layer, cellular density of synovial stroma, leukocytic infiltrate) were semiquantitatively evaluated (from 0, absent to 3, strong) and each feature was graded separately. The sum provided the synovitis score, which was interpreted as follows: 0-1, no synovitis; 2-4, low-grade synovitis; 5-9, high-grade synovitis. Five hundred and fifty-nine synovectomy specimens were graded by two independent observers. Clinical diagnoses were osteoarthrosis (n=212), post-traumatic arthritis (n=21), rheumatoid arthritis (n=246), psoriatic arthritis (n=22), reactive arthritis (n=9), as well as controls (n=49) from autopsies of patients without joint damage. Median synovitis scores when correlated with clinical diagnoses were: controls 1.0, osteoarthritis 2.0, post-traumatic arthritis 2.0, psoriatic arthritis 3.5, reactive arthritis 5.0 and rheumatoid arthritis 5.0. The scores differed significantly between most disease groups, especially between degenerative and rheumatic diseases. A high-grade synovitis was strongly associated with rheumatic joint diseases (P<0.001, sensitivity 61.7%, specificity 96.1%). The correlation between the two observers was high (r=0.941). The proposed synovitis score is based on well-defined, reproducible histopathological criteria and may contribute to diagnosis in rheumatic and non-rheumatic joint diseases.

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

Magnetic resonance (MR) imaging of 36 temporomandibular joints (TMJs) in 27 patients and six healthy volunteers was performed before and after injection of gadopentetate dimeglumine. Twelve asymptomatic joints were used as controls, 12 TMJs had symptomatic internal derangement, and 12 TMJs had rheumatic inflammatory disease. A small or moderate joint effusion was seen in one asymptomatic joint, four joints with internal derangement, and one joint with rheumatic involvement; in all of these, contrast enhancement of the effusion was observed. A large effusion in one rheumatic joint was enhanced only after delayed imaging. In healthy controls and patients with internal derangement, no or only minimal enhancement of intraarticular tissues was seen. Eleven of the 12 rheumatic TMJs showed moderate or intense soft-tissue enhancement along the disk and articular surfaces (ie, in areas normally devoid of synovial membrane). The one rheumatic joint without enhancement had bony ankylosis and no remaining soft tissue within the joint space. Gadolinium-enhanced MR imaging of the TMJ may effectively depict the proliferating synovium of rheumatic inflammatory joint disease.

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.

Background:Accurate and rapid diagnosis of rheumatic joint diseases is essential for further treatment decision. Early treatment initiation of different rheumatic diseases slows the progression and positively influence their courses [1-3]....

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.

Purpose This study critically aims to examine the growing emphasis on artificial intelligence (AI) literacy in education and libraries and argues that information literacy constitutes the missing epistemic core of AI literacy. It contends that without a strong grounding in information literacy, digital, media and AI literacy, initiatives risk prioritizing tool fluency over critical judgment, ethical reasoning and epistemic understanding. The study highlights the evolving role of librarians in addressing this gap. Design/methodology/approach This study adopts a conceptual and critical approach, drawing on scholarship in information literacy, critical information literacy, constructivist learning theory, epistemic cognition and critical pedagogy. Through theoretical synthesis and critical analysis of contemporary AI literacy discourses, it proposes a hierarchical meta literacy model that positions information literacy as the foundational condition for robust digital, media and AI literacy. Findings The study argues that when information literacy is marginalized, AI literacy risks becoming procedural and instrumental rather than epistemic and ethical. Challenges associated with generative AI, including bias, hallucination and misinformation, are framed as failures of information integrity. Grounding AI literacy in information literacy enables librarians to design instruction that emphasizes source evaluation, system interrogation and responsible AI use. Originality/value This study contributes a theoretically grounded reframing of AI literacy by positioning information literacy, as its epistemic foundation rather than a parallel or auxiliary competence. By introducing a hierarchical meta literacy model and foregrounding the evolving role of librarians in AI education, this study offers conceptual clarity for educators, librarians and policymakers seeking to design AI literacy initiatives that prioritize epistemic rigor, critical judgment and ethical responsibility over short-term technological proficiency.

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.