How to measure AI literacy (Fast): Development and norming of the AI competence objective scale short version (AICOS-S)

Artificial intelligence and nursing science: Opportunities, challenges, implications, and guidelines.

Artificial intelligence (AI) is increasingly integrated into healthcare, particularly in adult intensive care units (ICUs), yet nurses' knowledge, attitudes and concerns regarding AI remain insufficiently examined. This study aimed to assess ICU nurses' AI-related knowledge, attitudes and job-related concerns, and to explore associations among these variables. To assess ICU nurses' knowledge of artificial intelligence, attitudes towards its use in healthcare and job-related concerns, and to examine the relationships among these variables. A descriptive cross-sectional study was conducted among adult ICU nurses recruited through convenience sampling from five hospitals in Irbid, Jordan, between February and April 2025. Of the 105 eligible nurses invited, 91 completed the questionnaire and were included in the analysis. AI knowledge ranged from limited to moderate (mean 4.18 ± 1.50). Attitudes towards AI were moderately positive (57.95 ± 11.45), while job security concerns were moderate (18.1 ± 4.5; 95% CI: 17.2-19.0). Greater factual knowledge of AI concepts was significantly associated with more positive attitudes (r = 0.366, p < 0.001) and lower job security concerns (r = -0.302, p = 0.004). Attitudes were inversely associated with job security concerns (r = -0.275, p = 0.008). ICU nurses showed positive attitudes towards AI despite limited technical knowledge. Associations with job-related concerns highlight the need for targeted education and further multisite research on AI readiness in critical care. Structured AI literacy programmes, ethical governance frameworks and nurse involvement in AI implementation planning are essential to promote safe and balanced AI integration in adult critical care settings.

Research on Artificial Intelligence (AI)-mediated L2 writing has focused mainly on students’ use of tools and their effects on writing outcomes, while comparatively little is known about how writing teachers construct professional expertise in AI-rich classrooms. This article addresses that gap through a year-long dialogical–narrative case study of an experienced Chinese university writing teacher (“Wei”), based on three interviews, two classroom observations, student texts and teaching artefacts. The analysis shows that Wei’s expert identity is not a fixed set of traits, but an evolving, dialogically negotiated configuration of integrated knowledge about writing and writing instruction, student-centred pedagogy, sustained reflection, leadership and growing AI-related competence, co-constructed in interaction with students, colleagues, digital tools and institutional reform discourses. Wei’s trajectory both confirms and extends existing models of L2 writing teacher expertise by showing that AI literacy and the capacity to negotiate technological and institutional tensions have become central to expert identity in technology-mediated classrooms. Conceptually, the study refines existing accounts of expert writing teachers in the age of AI. Practically, it highlights the need for professional development and institutional support that help teachers critically integrate AI into writing pedagogy under digital reform.

Case-based learning is a proven pedagogical strategy in medical laboratory science (MLS) education, fostering critical thinking, data interpretation, and clinical reasoning skills. This article describes an innovative "artificial intelligence (AI)-first, student-corrected" assignment model that leverages generative artificial intelligence (AI) as a drafting tool while positioning students as expert validators of clinical information. Students use AI to generate initial case studies based on current course material, then critically appraise and correct inaccuracies in laboratory analytes, reference ranges, diagnostic pathways, and clinical reasoning. A structured rubric guides revision across four domains: patient background, laboratory data integration, evidence-based case questions, and diagnostic conclusions. Importantly, students also create paired videos to demonstrate comprehensive knowledge as well as patient-professional communication skills. Following implementation, MLS students who had completed both the traditional and AI-enhanced versions of the assignment were surveyed. Of the 17 students who responded, 15 (88%) preferred this AI-enhanced approach over traditional case writing, citing improved engagement, reduced writer's block, enhanced error detection skills, and increased confidence with emerging AI tool usage. By transforming students from passive case consumers into active builders of clinical understanding, this model strengthens essential MLS competencies, including critical appraisal, data verification, and AI literacy, all while preparing learners for a future where AI-assisted tools are embedded throughout laboratory medicine. This pedagogical innovation demonstrates how educators can harness AI's efficiency while preserving and amplifying the cognitive and professional benefits of case-based learning.

The integration of Artificial Intelligence (AI) in education is increasingly urgent in the era of digital transformation, yet AI literacy levels among Indonesian educators remain insufficiently mapped. This study aims to analyse AI literacy levels among primary and secondary school teachers in the context of digital teaching material development in Yogyakarta. Using a descriptive-qualitative design with an observational approach, the study involved 161 educators across educational levels observed during four AI, AR, and VR workshop sessions in Yogyakarta. Data were collected through structured observation and field notes, and analysed using reflexive thematic analysis. Results revealed significant differences in generative AI exposure: all teachers with less than five years of teaching experience (49 teachers, 100%) had been exposed to generative AI, while 72.32% of senior teachers (81 out of 112) had comparable exposure. Thematic analysis identified three main themes: (1) varying patterns of AI exposure and technology readiness, (2) barriers to AI adoption encompassing infrastructure, mindset, and competency factors, and (3) preferences for practice-based training. These findings are interpreted through the Technology Acceptance Model (TAM) and Diffusion of Innovation frameworks, contributing to theoretical understanding of intergenerational gaps in educational technology adoption in Indonesia.

ABSTRACT The increasing integration artificial intelligence into daily life, coupled with the development and advancement of eHealth literacy, can significantly impact healthy lifestyle behaviors by shaping how individuals access, evaluate, interpret, and use health-related information. The purpose of this study is to investigate how academics at a university in western Türkiye relate to artificial intelligence literacy, eHealth literacy, and healthy lifestyle behaviors. Data were collected with a sociodemographic form, the Artificial Intelligence Literacy Scale, the eHealth Literacy Scale, and the Health Promoting Lifestyle Profile-II. The study 190 participants. The mean score on the artificial intelligence literacy scale was 55.97±10.26, on the eHealth literacy scale 27.86±4.68, and on the healthy lifestyle behaviors scale-II 128.75±19.15. A moderate, positive, and significant correlation was found between artificial intelligence literacy and eHealth literacy (ρ = .594, p < .01). Weak, positive, and significant correlation were found between artificial intelligence literacy and healthy lifestyle behaviors (ρ = .285, p < .001), and between healthy lifestyle behaviors and eHealth literacy (ρ = .327, p < .001). The association between artificial intelligence literacy, eHealth literacy, and healthy lifestyle behaviors is better understood thanks to this study, which also offers information on potential influencing factors.

Background Artificial intelligence (AI) is fundamentally transforming higher education by enhancing pedagogical efficiency and administrative productivity. However, empirical evidence regarding faculty readiness and adoption patterns remains scarce within the Middle Eastern context. Objective This study investigated the levels of AI familiarity, usage patterns, and attitudes among faculty at the University of Buraimi, Oman, and examined the correlations between these variables to inform institutional policy. Method Utilizing a quantitative, cross-sectional design, a validated online questionnaire was distributed to 92 faculty members ( n = 92) across four academic colleges. The instrument assessed AI familiarity, usage, and attitudes on a 7-point Likert scale (Cronbach's alpha = .857). Data were analyzed using descriptive statistics and Pearson correlation coefficients. Results Participants exhibited moderate levels of AI familiarity (M = 24.84, SD = 4.92) and usage (M = 22.47, SD = 5.80), while maintaining notably positive attitudes toward AI integration (M = 50.12, SD = 8.34). Significant positive correlations were identified between AI familiarity and usage ( r = .573, p &amp;lt; .001), familiarity and attitude ( r = .427, p &amp;lt; .001), and usage and attitude ( r = .473, p &amp;lt; .001). These findings indicate a significant “willingness-usage gap”, where positive attitudes precede technical proficiency. Conclusion The results suggest that faculty familiarity and practical experience are primary determinants of technology acceptance. To align with the digital transformation goals of Oman Vision 2040, higher education institutions should prioritize role-specific AI literacy programs and robust institutional support frameworks to bridge the gap between psychological readiness and practical application. These findings highlight a contextualized “willingness–usage gap”, where positive psychological readiness precedes technical proficiency, offering institution-level strategic insight.

In the context of digital transformation and the rapid advancement of artificial intelligence (AI), academic integrity has become a central issue in higher education. This article examines the evolving role of university libraries, shifting from traditional resource providers to active enablers of academic integrity within digital scholarly environments. Drawing on a review of recent literature, the study identifies three core functional dimensions of contemporary academic libraries: (i) fostering information literacy and AI literacy among learners; (ii) enhancing awareness of copyright and the ethical use of knowledge; and (iii) acting as an intermediary between policy, pedagogy, and technology in shaping responsible AI practices. Building on these dimensions, the article proposes an ecosystem-based approach in which university libraries serve as integrative hubs that connect and operationalize the components of academic integrity into practice. The findings suggest that repositioning the role of academic libraries not only contributes to mitigating academic misconduct but also supports the development of a sustainable culture of integrity in contemporary higher education.

Artificial intelligence (AI), particularly in the form of large language models, can be highly useful in academic work. While many students are using such chatbots extensively, others remain skeptical or uncertain about the new technology. To prevent a growing divide between these groups, the Learning Support Center at OsloMet has developed a course in AI literacy. The course is grounded in a learning philosophy in which knowledge and understanding are constructed and developed through dialogue and collaboration. AI literacy encompasses attitudes as well as intellectual understanding and technical skills. Therefore, emotional engagement is also incorporated into the course. In the case of large language models, students have often taken up these tools without prior training. In developing the course, an anthropological approach was used to understand how GenAI has influenced the learning culture among students. Their experiences and attitudes toward GenAI are integrated into the course to foster personal engagement. The article presents the pedagogical approaches rather than describing the entire course. This is illustrated with five teaching examples that show how dialogue, including with the language model, and active learning activities build experience and awareness for using GenAI in a critical, learning-oriented, and more effective way.

Debates about generative artificial intelligence (AI) authorship have intensified within higher education and among academic publishers, with divisive opinions about responsible collaboration versus prohibition of AI use. Within the field of library and information science, these debates are especially complex due to the field’s highly interdisciplinary nature, which brings together contributors from the humanities, social and natural sciences, and dual commitments to professional practice and technical innovation. This essay examines the tensions surrounding AI-assisted writing and argues for the need to move beyond the simplistic accept-prohibit dichotomy. Drawing on the previously introduced concept of “bothorship,” the paper proposes a model of responsible AI authorship that preserves human intellectual ownership while acknowledging the practical and equity-related benefits of AI-supported writing. Attention is given to the role of libraries and librarians – including theological libraries – in advancing AI literacy, supporting ethical writing practices, and mitigating structural inequities in scholarly communication.

Against the background of globalization and digital integration, artificial intelligence (AI) provides a new approach to improving second language willingness to communicate (WTC). Following the PRISMA guidelines, this study screened 31 empirical studies and analyzed the influence of AI on EFL learners’ WTC from research trends, mediating mechanisms and moderating factors. Research has grown rapidly since 2020, focusing on East Asian university students with interdisciplinary theories and mixed methods. AI enhances WTC through affective and cognitive pathways: it reduces anxiety and builds confidence by creating a low-threat environment, and promotes growth mindset, AI literacy and instrumental cognition. The two pathways function synergistically. Effects are moderated by individual and contextual variables. Learners with lower proficiency and higher technology agency benefit more. Embodied tools, simple interaction scenarios and teacher-student-AI collaboration are more effective. This study suggests differentiated AI strategies, emphasizing teachers’ role in human-AI cooperation, cultivating learners’ technological agency and positive psychology, and addressing ethics and equity. Limitations lie in relatively concentrated participants and contexts. Future research should expand samples and settings, adopt longitudinal designs to verify causality, and explore dynamic mechanisms with AI development.

Abstract Artificial intelligence (AI) is increasingly shaping how young learners interact with digital technologies, yet many upper elementary students engage with AI systems passively and develop intuitive and sometimes inaccurate conceptions of how these systems work. This study examines the Foundational AI construct within a refined learning progression (LP), exploring how scaffolded instruction and dynamic assessment support conceptual shifts in students' understanding of how AI collects, learns from and uses data to make decisions. Drawing on Vygotsky's zone of proximal development and synergistic scaffolding theory, we refined the foundational AI construct of a five‐level LP and designed a two‐phase activity grounded in this LP to elicit and support student reasoning through structured tasks, informational scaffolds and facilitator prompts. Through mixed methods analysis of clinical interviews with 13 fourth and fifth graders (9–11 years), we identified recurring misconceptions and tracked shifts in student reasoning and movement along the Foundational AI construct of the LP. Furthermore, we examined one student's trajectory in depth to illustrate how dynamic assessment can function as a responsive instructional tool. Findings provide initial empirical insight into how scaffolded LP‐aligned instruction, paired with dynamic assessment, can support young learners' movement from surface‐level ideas to more structured understandings of how AI systems function. These insights contribute to the design of developmentally appropriate and contextually responsive AI learning experiences for primary education. Practitioner notes What was already known about this topic? Many young learners interact with AI technologies (eg, voice assistants, recommendation systems) but often hold surface‐level or inaccurate conceptions of how AI works. AI literacy frameworks exist, but none currently provide scaffolded pathways that align with young students' developmental readiness or explicitly address their initial misconceptions What this paper adds? Provides an initial empirical examination of a refined five‐level Foundational AI construct within a broader Learning Progression (LP) for upper elementary students. Demonstrates how LP‐aligned scaffolded instruction, using tasks, just‐in‐time informational supports and decision trees, can guide students from intuitive ideas to more data‐centered reasoning. Uses dynamic assessment to track and support conceptual growth, providing insight into students' readiness to reason about AI systems. Implications for practice and/or policy Scaffolded LPs that integrate structured tasks, informational prompts and dialogic facilitation can help support developmentally grounded AI instruction that is responsive to learner needs. Dynamic assessment frameworks can help researchers and educators capture students' shifts in reasoning, differentiating between ideas students can articulate independently and those requiring additional support. Designing layered, responsive scaffolds that actively elicit student reasoning and provide opportunities for reflection can support educators in guiding students' conceptual growth in AI literacy.

The integration of artificial intelligence (AI) into professional domains represents a fundamental transformation in how professionals conceptualize their identities, develop competencies, and engage in collaborative work practices. This comprehensive review synthesizes empirical and theoretical literature examining AI's impact on professional identity and skills evolution across healthcare, law, education, and engineering. Drawing from 60 peer-reviewed studies published between 2017 and 2025, this article analyzes how AI technologies reshape professional self-concepts, redefine core competencies, and establish new paradigms of human-AI collaboration. Key findings reveal that AI integration triggers identity tensions characterized by concerns about autonomy, expertise devaluation, and role displacement, while simultaneously creating opportunities for augmented practice and hybrid professional roles. The evolution from specialized technical skills toward human-centric competencies—including critical thinking, ethical judgment, creativity, and emotional intelligence—emerges as a central theme across all professions examined. Healthcare professionals experience AI as both diagnostic augmentation and identity threat; legal practitioners navigate tensions between efficiency gains and professional jurisdiction; educators redefine pedagogical roles from knowledge transmission to mentorship and ethical guidance; and engineers embrace AI-augmented design while maintaining human oversight. This article contributes to professional identity theory by proposing a framework of "AI-extended professional self " that conceptualizes identity transformation as dynamic negotiation between technological augmentation and human expertise preservation. Implications for professional education, organizational culture, and workforce development strategies are discussed, emphasizing the necessity of continuous learning ecosystems, ethical AI literacy, and institutional support for identity adaptation in the age of intelligent automation.

ABSTRACT This research examines the prompt of generative artificial intelligence (AI) feedback on cognitive anxiety at a Jordanian university in the field of IT security, with specific consequences for cybersecurity. Study design: A cross-sectional sprawling correlational design with a sample of 1879 public and private university students. Data Collection: A structured survey is designed based on the calculation of confidence toward AI for feedback and cognitive anxiety. Results displayed high reliance on generative AI tools (M = 3.79) and cognitive anxiety (M = 3.09). Furthermore, the correlation between sureness of AI feedback and cognitive anxiety was also high (r = 0.542, p < 0.01). IT safety remastered results in a higher designation of students being stuck in a cognitive excess, hindering them from appraising correctness, reliability, and safety of AI augmented outputs. Due to lack of proper knowledge and experience obligatory at this level, they get less surety about their answers notwithstanding getting great feedback analytics from AI. The research underlines the dual impact of generative AI, increasing academic productivity but also prompting mental and protectionist tension. The implications call for embedding AI literacy, cybersecurity recognition, and cognitive adjustment strategies into higher education.

As media planning becomes increasingly data-intensive and algorithmically driven, advertising educators face the challenges of preparing students to operate effectively within hybrid human–Artificial Intelligence (AI) workflows. This paper introduces a human–AI collaborative model for teaching undergraduate media planning, centered on a custom GPT tool, the Media Planning AI Assistant, embedded within a semester-long course. Rather than functioning as an autonomous decision-maker, the Assistant serves as a generative partner that models industry-standard workflows while requiring students to construct the knowledge base, interpret research inputs, and critically evaluate outputs. Using a mock national campaign, the paper demonstrates how students engage in iterative human–AI collaboration across audience analysis, competitive assessment, media strategy, budgeting, and scheduling. The study offers a practical pedagogical framework for integrating generative AI into media planning education, designed to reduce cognitive load, scaffold strategic reasoning, and support ethical AI literacy without displacing human judgment.

Artificial Intelligence Literacy in Intensive Care: From Algorithmic Fluency to Clinical Accountability

In the Philippine educational context, In-Service Teacher Training (INSET) remains central to teacher professional development and to sustaining educational reforms. As technological innovations—particularly artificial intelligence (AI)—reshape teaching and learning, teachers face increasing pressure to integrate AI and media tools in ways that are meaningful, ethical, and pedagogically sound. However, many existing INSET programmes continue to prioritise tool-based demonstrations rather than structured and reflective pedagogical practice, while challenges such as the digital divide, limited infrastructure, and unequal access to technology constrain responsible implementation. This descriptive-exploratory study examines how integrating Design Thinking (DT) and Media and Artificial Intelligence Literacy (MAIL) within INSET can strengthen teachers’ capacity to respond to these challenges while aligning professional development with international benchmarks such as the Programme for International Student Assessment (PISA) 2029 framework. The paper develops a conceptual framework illustrating how DT-informed INSET can operationalise MAIL competencies through practices such as ethical reflection, bias evaluation, iterative prototyping, peer feedback, and stakeholder validation. The analysis also considers feasibility factors including institutional readiness, technological resources, and sustainability requirements. By strengthening teachers’ capacity to integrate ethical AI use, media evaluation, and human-centred problem solving into classroom practice, the proposed DT–MAIL–informed INSET approach functions as a skills multiplier that supports learners’ development of critical thinking, ethical reasoning, digital literacy, adaptability, and other future-ready competencies.

ABSTRACT The metaverse and artificial intelligence (AI) are increasingly intersecting in educational contexts, yet limited empirical research has examined how generative AI avatars function within socially interactive virtual environments. This study investigates the deployment of generative AI avatars within a socio-educational metaverse environment. Using a developmental evaluation approach, data were collected through interviews with seven institutional stakeholders, teacher-generated reflections, internal documentation, embedded user feedback captured through in-platform reporting tools, and longitudinal field memos across an iterative deployment cycle. Findings indicate that the transition from scripted NPCs to generative AI avatars recalibrated users’ attribution of agency, intensified dialogic unpredictability, and elevated social realism beyond visual fidelity. Voice-mediated interaction emerged as a threshold mechanism for co-presence, while algorithmic improvisation exposed tensions between pedagogical intent and stochastic response generation. The deployment further revealed affective frictions, expectation misalignments, and the mediating role of AI literacy in shaping trust, participation, and interpretive coherence. Overall, the study advances a sociotechnical understanding of AI avatars as co-constructors of meaning and interaction, offering implications for the design, implementation, and governance of future AI-enhanced metaverse learning environments.

Background: Artificial intelligence (AI) is increasingly shaping healthcare education and clinical practice; however, the preparedness of healthcare students in Saudi Arabia to adopt AI remains unclear. Aim: To assess AI literacy and examine its relationship with intention to use AI among healthcare students at Majmaah University. Methods: A cross-sectional study was conducted with 802 undergraduate students from Nursing, Applied Medical Sciences, and Medicine. Data were collected using MAIRS-MS and a TAM-based behavioural intention scale. Descriptive statistics, correlation, and hierarchical regression analyses were performed. Results: A notable “education–interest gap” emerged. Although students reported high interest in AI (M = 5.13), 80% had little to no formal AI education and relied mainly on self-learning (71.7%) and social media (65.1%), with only 37.5% learning through coursework. Intention to use AI was moderately strong (M = 4.85/7), while overall readiness was moderate (M = 3.02/5). Domain scores showed higher “Ability” (M = 3.15) than “Cognition” (M = 2.90) or “Ethics” (M = 2.93). AI literacy correlated positively with intention (r = 0.381, p &amp;lt; 0.001). Regression analysis confirmed AI literacy as a significant predictor, explaining an additional 11.2% of variance in intention (β = 0.383, p &amp;lt; 0.001). Conclusion: Students show strong interest but limited formal preparation for AI adoption. Enhancing AI education, particularly theory and ethical/legal content, is essential to support safe and effective clinical integration aligned with Vision 2030.