Search Results (94)

Background: Artificial intelligence (AI) is increasingly embedded in critical care nursing through monitoring, decision support, and documentation systems, yet nurses’ readiness to use it remains uncertain, particularly in the Middle East region. Critical care nurses are central to AI implementation at the bedside, and their AI-related literacy and fears can influence safe and ethical integration into clinical practice. Aim: To assess AI-related literacy and fears among critical care nurses in Oman and the associated factors. Methods: A nationwide cross-sectional survey was conducted among critical care nurses (N = 477) working in tertiary hospitals in Oman. The Multidimensional Artificial Intelligence Literacy Scale and the Fear towards AI Scale were used to measure AI literacy and fears, respectively. Results: The participants had low overall AI literacy (146.62 ± 84.03), and low AI self-efficacy and AI self-competency. The lowest level of literacy was related to creating AI (2.43 ± 2.63). On the other hand, participants reported moderate overall fear towards AI and moderate levels of fear related to job issues and humanity and ethics. Age, marital status, levels of education, receipt of prior computer or information technology, AI-related training, and work experience, were significant predictors of AI literacy. The predictors of AI self-efficacy and AI competence are presented. Conclusions: Nurses working in critical care settings in Oman reported low levels of AI literacy, but moderate fears related to AI, and this provides an opportunity to build AI competencies and capacity. There is need for deliberate and structured continuing education programs to build capacity for AI utilization, competence, and self-efficacy among critical care nurses. Structured, hands-on AI training that integrates ethical reflection for older nurses with more experience but limited professional education is needed and essential to support safe and equitable AI utilization by critical care nurses in Oman. Full article

This mixed-methods study examined preschoolers’ digital home environment (DHE), parent–child digital and traditional literacy activities, children’s interest in literacy across modalities and parent and child perspectives on literacy practices. Participants included 121 Israeli parents of preschool-aged children and their children. Quantitative data were collected via parent questionnaires assessing joint digital literacy activities, general digital activities, parental involvement in selecting digital content, traditional literacy activities, and children’s interest in digital and traditional literacy. Qualitative data comprised parents’ open-ended responses about children’s digital media exposure and children’s perspectives on digital writing. Findings revealed positive associations between digital and traditional literacy practices in the home. Parent–child joint digital literacy activities emerged as the strongest predictor of children’s interest in digital literacy, beyond the effects of children’s age and traditional literacy practices. Conversely, parental involvement in selecting digital content was negatively associated with children’s interest in digital literacy activities. Qualitative findings indicated that parents perceived digital media use as offering educational opportunities while also raising developmental concerns, and placed strong emphasis on parental mediation and supervision. Children associated digital writing with learning, letters, and school-related literacy activities, while also linking computers with play and entertainment. Children’s preferences for handwriting versus keyboard writing were nearly equally divided, with explanations reflecting varied perceptions of convenience, enjoyment, and the meaning of writing in digital contexts. Together, these findings suggest that young children’s digital literacy experiences are shaped less by technology per se and more by the socially mediated interactions surrounding digital media use. Full article

Informal science, technology, engineering, and mathematics (STEM) learning settings can provide early and accessible opportunities for students who have been historically underrepresented in computing to engage with cybersecurity concepts and emerging technologies. This one-group pretest-posttest mixed-methods study examined a week-long, GenAI-integrated informal cybersecurity summer camp for 33 underrepresented and underserved racial and ethnic minority (UUREM) middle school girls. The study investigated pre-post patterns in perceived cybersecurity knowledge, domain-specific self-efficacy, interest, utility value, career aspirations, and selected AI-literacy practices. Quantitative findings showed the strongest support for increases in perceived cybersecurity knowledge and cyber threat identification self-efficacy, both of which remained significant after Holm adjustment for multiple comparisons. Networking and web management self-efficacy showed preliminary unadjusted increases, whereas overall cybersecurity interest, enjoyment and intent to pursue, utility value, career aspirations, and the remaining self-efficacy domains did not show statistically significant differences. Qualitative observations documented activity-specific engagement, collaborative discussion of cybersecurity ideas, and mentor-mediated GenAI practices such as prompt development, output comparison, and script revision. These findings suggest that informal GenAI-integrated cybersecurity programs may support selected aspects of perceived learning and cybersecurity self-efficacy, while highlighting the need for more rigorous designs, validated AI-literacy measures, and longer-term follow-up. Full article

Introduction: Traditional instruments for measuring lifelong learning of health professionals fail to capture digital-age learning, creating a critical measurement disconnect. To address this gap, we developed a 16-item Self-Pursuits, Aspirations, and Knowledge Endeavors in the Digital Era (SPArKED) instrument. Methods: To gather validity evidence for SPArKED, a cross-sectional survey was deployed to health professional students (n = 558). The survey questionnaire included: SPArKED, Jefferson scale of lifelong learning for students in health professions, basic psychological needs satisfaction scale, and human–computer trust scale assessing students’ trust in generative technology to support lifelong learning. Exploratory factor analysis (EFA) and correlation analysis were performed. Results: The EFA of the SPArKED revealed a three-component structure: networked learning, i-learning (individual mastery), and AI-powered learning, together explaining 55% of the total variance. The SPArKED demonstrated good internal consistency (α = 0.86) and convergent validity with the Jefferson scale of lifelong learning (r = 0.75). The correlations between SPArKED and psychological needs satisfaction scores were moderately high: autonomy (r = 0.50), competence (r = 0.48), and relatedness (r = 0.51). SPArKED had a higher correlation with students’ trust in generative technology to support lifelong learning than the Jefferson scale (r = 0.52 and r = 0.32, respectively). Conclusions: Compared to the Jefferson scale, the SPArKED instrument appears to better capture digital-age learning behaviors among students in health professions. By assessing these evolving behaviors in learners, education programs can better guide future health practitioners in developing desired lifelong learning competencies and digital literacies. Future research should gather validity evidence for SPArKED across diverse learner samples and educational stages, informing a critical re-assessment of established instruments in the rapidly evolving learning landscape. Full article

The emergence of artificial intelligence (AI) tools, particularly generative models, in the last five years has fundamentally transformed the framework and methodologies of learning in higher education. Students are integrating AI for producing new ideas, assisted and personalized search, academic writing, advanced data analysis, and personalized learning. For this reason, an update of the theoretical and conceptual framework regarding the adoption of technologies in the educational environment is required. Based on traditional Technology Acceptance Model/Unified Theory of Acceptance and Use of Technology (TAM/UTAUT) models, we propose a new Partial Least Squares Structural Equation Modeling (PLS-SEM) model developed for the context of AI in higher education. The novelty of the model lies in the integration of the mediating relationship through trust (trust in AI outputs, TAIO) between perceived academic integrity risk (PAIR) and behavioral intention to use (BI), while anchoring perceived learning utility (PUL) and perceived effort expectancy (PEE) in AI literacy-specific self-efficacy (AILSE). The model is tested using a sample of 339 higher education students from economics and computer science specializations and validated using the R environment and the SEMinR package as specific software tools. Our proposed research hypotheses consider six reflective latent constructs and a mediating relationship, which we analyze using validated PLS-SEM techniques. All items included in the model constructs are formulated for use in university educational contexts and are adapted to specific AI tools for learning in the university environment. Full article

Aim: This study aimed to evaluate self-reported digital health literacy levels and work engagement among nurses in the United Arab Emirates (UAE), while also examining associations with demographic factors and the interplay between digital health literacy and work engagement. Background: The integration of digital technologies into healthcare has transformed patient care, clinical practice, and administration. Nurses, as frontline practitioners, play a crucial role in utilizing digital tools to enhance patient interactions and navigate complex healthcare systems. Methods: Between May and August of 2024, 364 nurses in the United Arab Emirates participated in a cross-sectional design study. A standardized 21-item self-reported Digital Health Literacy questionnaire and a 9-item Utrecht Work Engagement Scale were administered. Descriptive statistics were used, with t-tests, ANOVA, correlations, and multiple linear regression applied. Results: The average score for self-reported digital health literacy (3.05 ± 0.57) and work engagement (4.83 ± 1.13) was high. Gender, age, work experience, and education level showed varying patterns of association with self-reported DHL and work engagement across total and subscale scores. Education level was significantly associated with self-reported DHL but not with work engagement. The overall work engagement score and its subscales were positively correlated with self-reported DHL. Conclusions: Our findings provide a robust basis for subsequent research on DHL and work engagement. These findings support the relevance of self-reported DHL as a factor associated with nurses’ work engagement in digitally intensive healthcare settings. The study reveals that nurses reported high levels of digital health literacy and work engagement. Full article

The digital transformation of mental health practice increasingly requires psychologists to integrate technological literacy with emotional and cognitive skills. This study presents a pilot project combining Visual Education and Artificial Intelligence (AI) through a Computer Vision model for emotional recognition. A convolutional neural network (CNN), based on MobileNetV2, was trained to identify facial emotions and tested for educational use within a serious game for psychologists’ professional development. Using transfer learning, the model achieved an accuracy of about 75% under controlled conditions but only 15.54% on a biased dataset. These results reveal both the potential and limitations of AI in emotional learning. The findings are discussed in relation to visual literacy, digital mental health, and AI ethics, illustrating how computational bias can become a pedagogical tool for psychology professionals. Full article

Filipino Sign Language (FSL) serves as a vital means of communication for the Deaf and hard-of-hearing in the Philippines. However, its societal use remains limited due to the scarcity of qualified interpreters and the general lack of FSL literacy among the population. Therefore, this study aims to address the gap between FSL development and automated FSL translation by employing machine learning and computer vision techniques. A model was trained using the FSL-105 dataset, which comprises video clips of gestures related to greetings and colors, and utilized MediaPipe for real-time detection of hand, face, and body landmarks. Through iterative training with transfer learning, the model’s performance improved from an initial accuracy of 80% to a final accuracy of 98.75%. The results demonstrate that the MediaPipe-based model can reliably interpret FSL gestures, positioning it as a potentially accessible assistive tool for the Deaf and hard of hearing community. This technology holds promise for applications in education, healthcare, and public service, offering new opportunities to promote the social inclusion of Filipino Deaf communities through more inclusive communication. Full article

Computational thinking (CT) has become a cross-curriculum priority in many educational jurisdictions, yet a growing body of research reports uneven integration in initial teacher education (ITE), limited preservice teacher confidence, and persistent misconceptions that equate CT with coding. Concurrently, generative artificial intelligence (GenAI) has rapidly entered university programmes, offering new possibilities for modelling problem-solving, generating multiple representations, and supporting iterative design. However, while constructs such as self-efficacy, cognitive load, and affect are well established in educational psychology, their specific application to the intersection of CT and GenAI in teacher education remains under-theorised: existing research has not systematically examined how these psychological dimensions interact when preservice teachers learn CT through GenAI-mediated tasks. This thematic literature review synthesises 54 sources across three intersecting domains: CT frameworks and their pedagogical implications, CT integration in preservice teacher preparation, and GenAI in teacher education and learning design. Drawing on Bandura’s social cognitive theory, cognitive load theory, and research on technology-related affect, the review foregrounds the affective, cognitive, and cultural dimensions of preservice teachers’ engagement with CT and GenAI. The review proposes the GenAI-Enabled Computational Thinking for Preservice Teachers (GECT-P) model, which integrates CT dimensions with GenAI-supported learning cycles, psychological mediators, and teacher education outcomes. The model positions prompting as an epistemic and pedagogical practice that can make CT visible, supports cycles of decomposition, abstraction, pattern recognition, and algorithmic design, and embeds critical AI literacy, ethics, affective scaffolding, and classroom enactment. Design principles and practical pathways are offered for teacher educators seeking to prepare graduates who can develop CT with and beyond GenAI across diverse curriculum areas. Full article

Background: Artificial intelligence (AI) is increasingly integrated into healthcare systems, with important implications for nursing practice and clinical workflows. However, evidence regarding nurses’ digital skills, perceptions, and readiness to adopt AI-enabled technologies remains limited, particularly in national healthcare contexts such as Greece. Objectives: This study examined nurses’ digital skills, perceptions of AI, and readiness for AI adoption in clinical practice, and explored demographic and professional factors associated with these outcomes. Methods: A cross-sectional survey was conducted among 166 nurses working in two public hospitals in Greece. Results: Nurses reported moderate digital skills, with 59.1% indicating competence in email/video communication and 27.2% reporting adequate use of digital security tools, while exposure to AI remained limited (18.0% reported using AI products/services in daily life). Perceived professional impact of AI was moderate, whereas readiness for AI adoption was comparatively lower, with only 7.8% considering health professionals adequately prepared and 7.2% reporting adequate AI training. Statistical analyses indicated that educational level and computer literacy certification were positively associated with digital skills, whereas longer professional experience was negatively associated with readiness for AI adoption. Conclusions: These findings highlight a gap between general digital competence and preparedness for AI-driven healthcare applications and underline the need for targeted education and implementation strategies to support effective and ethical integration of AI in nursing practice. From a nursing workforce perspective, the results underscore the importance of integrating AI literacy into continuing professional education and aligning digital health implementation strategies with clinical nursing practice. Full article

The rapid spread of misinformation on social media has emerged as a major societal issue. Over 40% of British social media news-sharers admitted they had shared inaccurate or fake news. The extensive distribution of false information causes public trust deterioration while modifying public opinions and potentially destabilizing social and political systems. There are profound challenges due to this hard-to-detect, hard-to-stop reality and the financials and societal implications are remarkable. As an attempt to limit the challenges created from misinformation this paper introduces some preliminary work on detection of fake news and verification of their reliability based on online content. Large language models (LLMs) are being used along with natural language processing (NLP) techniques to evaluate news articles through their linguistic and contextual characteristics. Several models are compared on how they can typically identify typical indicators of misinformation through the analysis of extensive verified datasets to develop an ability to classify content as authentic or fabricated. This work has been through thorough testing to determine its operational effectiveness and dependability after completion. We present a relatively easy-to-use tool which enables a wide range of people also for those without a background in computer science to easily verify news accuracy before sharing or trusting it. This work could help to stop false information from spreading while promoting fact-based discussions and improving digital literacy skills. The research demonstrates how technology fights the fake news crisis to create an informed digital environment which supports public conversation protection and information integrity in the modern digital age. Full article

This study examines the differences in sports learning among Generation Z based on digital literacy, using the Unified Theory of Acceptance and Use of Technology (UTAUT) and Media Richness Theory (MRT). As non-face-to-face sports learning—including online lectures, remote coaching, and virtual reality—rapidly expands, digital literacy has become a key factor influencing learning outcomes and equity. Data were collected from Generation Z adults engaged in sports learning through platforms including YouTube, social networking services, online lecture platforms, and mobile applications. Participants were classified into low (n = 87)-, medium (n = 80)-, and high (n = 70)-digital-literacy groups. A 32-item questionnaire adapted from prior studies assessed digital literacy (4 items), four UTAUT constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions; 16 items), and three media richness dimensions (multiple channels, immediacy of feedback, and personalness; 12 items). Confirmatory factor analysis demonstrated acceptable model fit (χ² = 779.013, df = 436, p < 0.001, NFI = 0.914, IFI = 0.960, TLI = 0.954, CFI = 0.960, SRMR = 0.037, RMSEA = 0.058), reliability (all ω and α > 0.70), and convergent/discriminant validity (all AVE > 0.50; C.R. > 0.70). Group comparisons indicated that higher digital literacy was linked to higher scores in technology acceptance and media richness perceptions (F = 40.364–64.150, p < 0.001, ηp² = 0.257–0.354) These findings indicate that intra-generational differences in digital literacy shape technology use and media experience in sports learning, highlighting the need to enhance media richness and systematically develop learners’ digital literacy to improve digital sports education’s effectiveness and equity. But causal inferences are limited by the cross-sectional design. Full article

Background: In recent years, virtual consultations have emerged as a crucial approach for continuity of chronic care provision, indicating a promising avenue for the future of smart healthcare systems. However, reversions to in-person care highlight persistent limitations, despite notable advantages of remote modalities. In parallel, recent developments in artificial intelligence (AI) indicate the potential to enhance remote chronic care, but user perceptions of such assistance and the corresponding human factors remain underexplored. Objective: This mixed methods study aims to better understand the virtual consultation experiences and attitudes toward AI-assisted tools in remote care among patients with noncommunicable chronic conditions and their healthcare professionals (HCPs). It conducts an in-depth examination of the associated human–computer interaction and usability elements of virtual consultations and of potential AI assistance. Methods: Public and Patient Involvement was integrated to run pilots and refine documentations. Semi-structured interviews with patients (n = 10), focus groups with HCPs (n = 15), and an online survey (n = 83) were conducted. Qualitative data was analysed through a reflexive thematic approach. The survey comprised the Telehealth Usability Questionnaire (TUQ) and bespoke items on user AI views, and the data was used to triangulate the qualitative findings. Nonparametric Kruskal–Wallis tests and ε² effect sizes compared TUQ and AI views scores between current and former virtual consultation user groups. Results: Seven themes emerged from the qualitative data, which were supported by the quantitative findings. The statistical analyses resulted in a mean TUQ total score of 90.6 (SD = 15.0), which indicates high usability and user satisfaction; however, they failed to detect a difference between groups (p > 0.05; ε² = 0.002–0.032). There was a clear preference for hybrid models, while a lack of empathy was identified during remote interactions. While a notable proportion of users indicated a literacy gap towards AI use in healthcare settings, they expressed cautious openness towards AI assistance, contingent upon transparency, human oversight, and data integrity; indicating a potential gap between competence to judge the technology and willingness to use it. Significant differences in views on AI assistance across groups failed to be detected (p > 0.05; ε² = 0.005–0.065). Conclusions: Virtual consultations for chronic conditions are widely usable and acceptable, particularly through hybrid approaches. Addressing empathic engagement, holistic patient status, and transparent AI integration can enhance clinical quality and user experiences during remote interactions. However, the low statistical power and failure to detect a difference between groups (likely due to the small sample size) indicate the need for caution when interpreting the quantitative findings. There is also the implicit need to address potential AI literacy gap among users, indicating the need for robust safeguard measures. This study has also identified evidence-based assistive AI features that can potentially enhance virtual consultations. These insights can inform the co-design of evidence-based virtual care platforms, policies and supportive AI tools to sustain remote chronic care delivery. Full article

This study presents the design and implementation of a full-stack IoT ecosystem based on ESP32 microcontrollers and web-based visualization dashboards to support scientific reasoning in first-year engineering students. The proposed architecture integrates a four-layer model—perception, network, service, and application—enabling students to deploy real-time environmental monitoring systems for agriculture and beekeeping. Through a sixteen-week Project-Based Learning (PBL) intervention with 91 participants, we evaluated how this technological stack influences technical proficiency. Results indicate that the transition from local code execution to cloud-based telemetry increased perceived learning confidence from $\mu =3.9$ (Challenge phase) to $\mu =4.6$ (Reflection phase) on a 5-point scale. Furthermore, 96% of students identified the visualization dashboards as essential Human–Computer Interfaces (HCI) for debugging, effectively bridging the gap between raw sensor data and evidence-based argumentation. These findings demonstrate that integrating open-source IoT architectures provides a scalable mechanism to cultivate data literacy in early engineering education. Full article

As quantum technologies advance, there is growing international interest in integrating quantum concepts into secondary education. However, most K-12 quantum education studies rely on self-reported data or informal assessments lacking documented validity. This study aimed to address this gap by developing and validating the Quantum Literacy Test (QLt), a standardized instrument designed to objectively assess upper-secondary students’ understanding of foundational quantum concepts, practices, and perspectives. Grounded in the computational thinking (CT) framework, the QLt was piloted with 819 senior secondary school students in Nigeria and underwent a multi-phase validation process, including expert review, factor analysis, item-response modeling, differential item functioning analysis, and concurrent validity. The QLt demonstrated high internal consistency (α = 0.87) and structural validity. Strong concurrent validity was observed with the Computational Thinking Test (r = 0.65), and moderate validity was observed with a Spatial Ability Test (r = 0.32). However, machine learning models explained less than 40% of QLt score variance, suggesting the domain-specific nature of quantum literacy. We recommend future research to expand the QLt across diverse cultural contexts and to increase item coverage of quantum practices and perspectives. The QLt offers a valuable tool for evaluating curriculum effectiveness and monitoring equity in quantum education, thereby contributing to a more inclusive quantum-ready workforce. Full article