Search Results (1,936)

Artificial intelligence (AI) is increasingly transforming higher education, evolving from simple personalization tools into a wide range of applications that support teaching, learning, and assessment. This study examines how university instructors in Lebanon perceive and adopt AI in their academic practices, drawing on evidence from two private institutions: Notre Dame University–Louaize (NDU) and the Holy Spirit University of Kaslik (USEK). The study also proposes practical directions for effective institutional implementation. Using a cross-sectional design and convenience sampling, data were collected from 133 faculty members. Although 73.7% of participants reported moderate to high familiarity with AI, their actual classroom use of such tools remained limited. Adoption was primarily centered on chatbots (69.2%) and translation tools (54.9%), while more advanced technologies, such as adaptive learning systems and AI-based tutoring platforms, were seldom utilized (under 7%). Additionally, participants identified efficiency (69.2%), increased student engagement (44.4%), and personalized learning opportunities (42.9%) as the main benefits of AI integration. In contrast, they reported insufficient training (46.6%), restricted access to resources (45.9%), and concerns about the accuracy of AI-generated outputs (29.3%) as major barriers. Moreover, statistical analysis indicated a strong positive relationship between familiarity with AI and frequency of adoption, with no significant differences across gender, age, or academic qualifications. Overall, the results suggest that faculty members in Lebanese higher education currently view AI primarily as a helpful tool for improving efficiency rather than as a transformative pedagogical innovation. To advance integration, higher education institutions should prioritize targeted professional development, ensure equitable access to AI tools, and establish transparent ethical and governance frameworks. Full article

Artificial intelligence (AI) is rapidly transforming anesthesiology practice across perioperative settings. This review explores the evolution and implementation of hybrid decision-making models that integrate AI capabilities with human clinical expertise. From historical foundations to current applications, we examine how machine learning algorithms, deep learning networks, and big data analytics are enhancing anesthetic care. Key applications include perioperative risk prediction, AI-assisted patient education, automated analysis of clinical records, airway management support, predictive hemodynamic monitoring, closed-loop anesthetic delivery systems, and pain management optimization. In procedural contexts, AI demonstrates promising utility in regional anesthesia through anatomical structure identification and needle navigation, monitoring anesthetic depth via EEG analysis, and improving quality control in endoscopic sedation. Educational applications include intelligent simulators for procedural training and academic productivity tools. Despite significant advances, implementation challenges persist, including algorithmic bias, data security concerns, clinical validation requirements, and ethical considerations regarding AI-generated content. The optimal integration model emphasizes a complementary approach where AI augments rather than replaces clinical judgment—combining computational efficiency with the irreplaceable contextual understanding and ethical reasoning of the anesthesiologist. This hybrid paradigm reinforces the anesthesiologist’s leadership role in perioperative care while enhancing safety, precision, and efficiency through technological innovation. As AI integration advances, continued emphasis on algorithmic transparency, rigorous clinical validation, and human oversight remains essential to ensure that these technologies enhance rather than compromise patient-centered anesthetic care. Full article

Background/Objectives: This systematic review examines neuroplasticity-informed approaches to learning under cognitive load, synthesizing evidence from functional imaging, brain stimulation, and educational technology research. As digital learning environments increasingly challenge learners with complex cognitive demands, understanding how neuroplasticity principles can inform adaptive educational design becomes critical. This review examines how neural mechanisms underlying learning under cognitive load can inform the development of evidence-based educational technologies that optimize neuroplastic potential while mitigating cognitive overload. Methods: Following PRISMA guidelines, we synthesized 94 empirical studies published between 2005 and 2025 across PubMed, Scopus, Web of Science, and PsycINFO. Studies were selected based on rigorous inclusion criteria that emphasized functional neuroimaging (fMRI, EEG), non-invasive brain stimulation (tDCS, TMS), and educational technology applications, which examined learning outcomes under varying cognitive load conditions. Priority was given to research with translational implications for adaptive learning systems and personalized educational interventions. Results: Functional imaging studies reveal an inverted-U relationship between cognitive load and neuroplasticity, with a moderate challenge in optimizing prefrontal-parietal network activation and learning-related neural adaptations. Brain stimulation research demonstrates that tDCS and TMS can enhance neuroplastic responses under cognitive load, particularly benefiting learners with lower baseline abilities. Educational technology applications demonstrate that neuroplasticity-informed adaptive systems, which incorporate real-time cognitive load monitoring and dynamic difficulty adjustment, significantly enhance learning outcomes compared to traditional approaches. Individual differences in cognitive capacity, neurodiversity, and baseline brain states substantially moderate these effects, necessitating the development of personalized intervention strategies. Conclusions: Neuroplasticity-informed learning approaches offer a robust framework for educational technology design that respects cognitive load limitations while maximizing adaptive neural changes. Integration of functional imaging insights, brain stimulation protocols, and adaptive algorithms enables the development of inclusive educational technologies that support diverse learners under cognitive stress. Future research should focus on scalable implementations of real-time neuroplasticity monitoring in authentic educational settings, as well as on developing ethical frameworks for deploying neurotechnology-enhanced learning systems across diverse populations. Full article

Background: Children with autism spectrum disorder (ASD) commonly experience persistent difficulties in social communication, emotional regulation, and social engagement. In recent years, artificial intelligence (AI)-based technologies, particularly socially assistive robots and intelligent sensing systems, have been explored as complementary tools to support psychosocial interventions in this population. Objective: This systematic review aimed to critically evaluate recent evidence on the effectiveness of AI-based interventions in improving social, emotional, and cognitive functioning in children with ASD. Methods: A systematic literature search was conducted in PubMed following PRISMA guidelines, targeting English-language studies published between 2020 and 2025. Eligible studies involved children with ASD and implemented AI-driven tools within therapeutic or educational settings. Eight studies met inclusion criteria and were analyzed using the PICO framework. Results: The reviewed interventions included humanoid and non-humanoid robots, gaze-tracking systems, and theory of mind-oriented applications. Across studies, AI-based interventions were associated with improvements in joint attention, social communication and reciprocity, emotion recognition and regulation, theory of mind, and task engagement. Outcomes were assessed using standardized behavioral measures, observational coding, parent or therapist reports, and physiological or sensor-based indices. However, the studies were characterized by small and heterogeneous samples, short intervention durations, and variability in outcome measures. Conclusions: Current evidence suggests that AI-based systems may serve as valuable adjuncts to conventional interventions for children with ASD, particularly for supporting structured social and emotional skill development. Nonetheless, methodological limitations and limited long-term data underscore the need for larger, multi-site trials with standardized protocols to better establish efficacy, generalizability, and ethical integration into clinical practice. Full article

Building Information Modeling (BIM) has become a core competency in architectural practice, prompting increasing efforts to integrate BIM into design education. However, existing pedagogical approaches vary widely across institutions, regions, and curricular structures, ranging from software-focused instruction to more holistic, design-centered applications. This study presents a comprehensive review of BIM education in architecture by synthesizing trends, pedagogical models, and implementation strategies reported between 2010 and early 2025. A hybrid review design was employed by combining PRISMA-based systematic procedures with scoping and comparative analysis. Bibliometric mapping of 399 BIM education publications identified major research clusters and global trends, while an in-depth analysis of 31 architecture-focused studies revealed seven thematic categories encompassing curriculum integration, design studio pedagogy, immersive technologies, collaborative models, and algorithmic approaches. The findings show a gradual shift from tool-based training toward integrated studio environments where BIM supports design creativity, interdisciplinary coordination, and process-based learning. Persistent challenges—such as balancing technological proficiency with design thinking, adapting faculty expertise, and aligning curricula with industry expectations—continue to hinder deeper integration. Based on the synthesis, this study proposes an integrated educational framework that connects technological competence, design creativity, and collaborative cognition, offering guidance for the next stage of BIM-enabled architectural education. Full article

The rapid convergence of artificial intelligence (AI), cloud computing, and 5G communication has positioned extended reality (XR) as a core technology bridging the physical and virtual worlds. Encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), XR has demonstrated transformative potential across sectors such as healthcare, industry, education, and defense. However, the compact architecture and limited computational capabilities of XR devices render conventional cryptographic authentication schemes inefficient, while the real-time transmission of biometric and positional data introduces significant privacy and security vulnerabilities. To overcome these challenges, this study introduces PXRA (PUF-based XR authentication), a lightweight and secure authentication and key distribution protocol optimized for cloud-assisted XR environments. PXRA utilizes a physically unclonable function (PUF) for device-level hardware authentication and offloads elliptic curve cryptography (ECC) operations to the cloud to enhance computational efficiency. Authenticated encryption with associated data (AEAD) ensures message confidentiality and integrity, while formal verification through ProVerif confirms the protocol’s robustness under the Dolev–Yao adversary model. Experimental results demonstrate that PXRA reduces device-side computational overhead by restricting XR terminals to lightweight PUF and hash functions, achieving an average authentication latency below 15 ms sufficient for real-time XR performance. Formal analysis verifies PXRA’s resistance to replay, impersonation, and key compromise attacks, while preserving user anonymity and session unlinkability. These findings establish the feasibility of integrating hardware-based PUF authentication with cloud-assisted cryptographic computation to enable secure, scalable, and real-time XR systems. The proposed framework lays a foundation for future XR applications in telemedicine, remote collaboration, and immersive education, where both performance and privacy preservation are paramount. Our contribution lies in a hybrid PUF–cloud ECC architecture, context-bound AEAD for session-splicing resistance, and a noise-resilient BCH-based fuzzy extractor supporting up to 15% BER. Full article

Artificial intelligence (AI) is rapidly transforming the landscape of dental implantology by enhancing every stage of treatment, from diagnostics and digital planning to intraoperative navigation, outcome prediction, and long-term follow-up. This narrative review explores the current and emerging applications of AI technologies in implant dentistry, with a focus on machine learning, neural networks, and computer vision. It examines how AI is utilized in digital implant planning, surgical navigation, peri-implant disease monitoring, risk assessment, and the prediction of treatment outcomes such as peri-implantitis and implant failure. These innovations contribute to more efficient workflows, more personalized treatment strategies, and improved cost-effectiveness of care. Finally, future perspectives and educational implications of AI integration in clinical implantology are discussed. Full article

Construction 4.0 refers to the integration of automation, artificial intelligence, and the Internet of Things (IoT) in the construction industry, which has changed traditional construction practices. MSc courses play a crucial role in developing the next generation of leaders within the construction industry by equipping graduates of these courses with advanced technical, managerial, and strategic skills, including the arrival of Construction 4.0. As future professionals and construction industry leaders, it is necessary to identify the current level of awareness and understanding of Construction 4.0 amongst master’s level students. As such, this paper studies these areas to help identify the gaps in education and training requirements—essential for matching academic programs with industry needs. Through the use of a survey-based approach with 112 MSc students on various Construction Management courses, a series of revealing results were obtained. The results presented herein indicate that there is a shared definition of what constitutes Construction 4.0 amongst engineering management students. However, while they are relatively aware of Construction 4.0 technologies, they do not differentiate strongly between Industry 4.0 and Construction 4.0. Therein, they are ambivalent as to the role of Education 4.0 in improving this situation. Key to this is the requirement to keep up with industry needs. The lack of application of Construction 4.0 means students lack the necessary ‘practical skills’ to implement innovations on real construction sites. Students advocated for more hands-on training, industry-linked projects, and guest lectures within the curriculum, alongside developing the essential skills of critical thinking and problem-solving. Changes in the curricula are suggested, achievable through readily existing 4.0 Frameworks. Full article

The objective of this study is to assess the extent to which science teachers implement (SERTPs) and to examine whether these practices differ according to selected demographic and professional variables. Using a descriptive–analytical design, data were collected from 225 science teachers enrolled in graduate programs at King Khalid University during the 2025–2026 academic year. The findings reveal a high overall level of SERTPs (M = 2.45; 81.81%). The highest-scoring dimensions were Enhancing Students’ Environmental Awareness (86.59%) and Using Sustainable Resources in Teaching (84.00%), while Encouraging Community Participation showed the lowest application level (77.95%). No significant differences were found across gender, teaching stage, academic qualification, or age; however, a significant difference emerged in favor of teachers with a high level of technology use (p < 0.001). These results underline the vital role of technological integration in strengthening sustainable teaching practices. The study recommends targeted professional development, sustainability-centered curriculum enhancement, and institutional support to align science education with global Education for Sustainable Development (ESD) goals. Full article

This research-to-practice study examines how Generative Artificial Intelligence (GenAI) can be integrated into live case studies to enhance experiential learning in higher education. It explores GenAI’s potential as an agent to learn with scaffolding reflection and engagement and addresses gaps in existing applications that often focus narrowly on content generation. To explore GenAI’s agentive potential, the methodology illustrates this approach in a UK postgraduate operations management module. Students engaged in a live case study of a local ethnic restaurant to refine its business model and operations. The data sources used to examine students’ results included module materials, outputs, and feedback surveys. Thematic analysis was employed to assess how GenAI facilitated experiential learning. The findings suggest that GenAI integration facilitated exploration, reflection, conceptualisation, and experimentation. Students reported that the activity was engaging and relevant, facilitating critical decision-making and understanding of operations management. However, the outcomes varied according to GenAI literacy and student participation. Although GenAI-enriched learning is beneficial, human agency and contextual knowledge remain crucial. Overall, this study integrates GenAI as a cognitive partner throughout Kolb’s ELC. This study offers a transferable framework for active learning, illustrating how technology can enhance critical and reflective learning in authentic educational contexts. However, limitations include uneven student participation and engagement, resource constraints, overreliance on artificial intelligence outputs, differentiated impact on learning outcomes, and a single-case report, which must be addressed before the framework can be scaled up. Future research should test this through multi-case studies while developing GenAI literacy, measuring GenAI impact, and implementing ethical practices in the field. Full article

Background/Objectives: The rapid evolution of digital technologies has significantly transformed prosthodontic workflows, improving clinical precision, communication, and patient satisfaction. However, the extent to which dental professionals perceive, integrate, and evaluate these technologies remains insufficiently standardized. This study aimed to develop and validate a questionnaire for assessing perceptions, attitudes, perceived advantages, barriers, and future intentions regarding the use of digital technologies in prosthodontic practice. Methods: A cross-sectional survey was conducted among 420 dental professionals (305 dentists and 115 dental technicians) from Northeastern Romania. The 27-item questionnaire, structured on five theoretical dimensions, was distributed online via the Survio platform. Internal consistency was assessed using Cronbach’s Alpha, and construct validity was analyzed through Exploratory Factor Analysis (Principal Component Analysis with Varimax rotation). Conclusions: Cronbach’s Alpha coefficients ranged from 0.700 to 0.799 across the five dimensions, indicating acceptable to very good internal reliability. The Kaiser–Meyer–Olkin value (0.646) and Bartlett’s Test of Sphericity (p < 0.001) confirmed data suitability for factor analysis. The validated questionnaire represents a reliable and conceptually coherent tool for evaluating professional perspectives on digitalization in prosthodontics. Its application can inform educational strategies, guide institutional investments, and support a balanced transition toward integrated digital workflows in clinical and laboratory settings. Full article

Memory function is susceptible to decline with age, stress, and neurological diseases, highlighting the importance of exploring effective and sustainable strategies to enhance memory consolidation. Epinephrine plays a key role in memory consolidation; acute, moderate elevations enhance memory, while chronic high levels are inhibitory. Given the limitations of pharmacological interventions, this study aims to investigate exercise as a non-pharmacological means to promote post-learning memory consolidation by inducing acute epinephrine release, focusing on its mechanisms and optimized implementation strategies. This narrative review systematically reviews evidence from neurophysiology, molecular biology, and behavioral experiments and finds that exercise can safely and controllably activate the sympathetic–adrenal system, leading to a rapid rise in epinephrine. The release kinetics align highly with the critical time window for memory consolidation. Moderate-intensity aerobic exercise implemented within 30 min post-learning can significantly improve memory retention. The mechanisms involve not only epinephrine enhancing synaptic plasticity and LTP by activating hippocampal β-adrenergic receptors, but also synergistic effects across multiple systems, such as promoting osteocalcin signaling, upregulating BDNF expression, inducing neurogenesis, and optimizing cerebral metabolism and blood flow. Evidence suggests that exercise, as a non-pharmacological intervention, significantly enhances post-learning memory consolidation through the precise modulation of epinephrine release and multi-system synergy, offering both high efficacy and safety. Future research should focus on developing precise exercise prescriptions based on individual characteristics and leveraging wearable devices and digital technologies to improve intervention adherence and applicability, promoting its widespread use in educational and clinical settings. Full article

Introduction. The proliferation of immersive virtual reality (VR) technologies has transformed the way individuals interact with digital environments, offering unprecedented opportunities in fields ranging from entertainment and education to healthcare and mental health interventions. Immersive VR is increasingly being implemented in motor and cognitive programs in neurorehabilitation, where patient safety and treatment adherence are critical. Despite its relevance, the conceptualization and measurement of cybersickness (CS) remain fragmented across disciplines, with various assessment tools developed in isolation, targeting different symptom domains, populations, and use contexts. Aim. The aim of this systematic review is to identify, categorize, and critically appraise all existing instruments and scales developed to measure CS associated with immersive VR use. The secondary objectives involve examining the psychometric properties of the identified instruments to provide robust evidence for clinicians in assessing CS associated with VR, thereby supporting future scale development and standardization. Additionally, a further objective is to evaluate the specific applicability of these instruments and scales for measuring CS within neurorehabilitation settings, given the growing use of immersive VR in clinical practice with neurological populations. Methods. This systematic review was conducted in accordance with the Guideline for reporting systematic reviews of outcome measurement instruments (PRISMACOSMIN). The review protocol was prospectively registered in the International Prospective Register of Systematic Reviews (PROSPERO). Data extraction was performed independently by the two reviewers, and discrepancies were resolved by consensus or consultation with a third reviewer. To assess the psychometric robustness of existing CS assessment instruments used in virtual reality settings, we applied the methodology proposed by the COSMIN (COnsensus-based Standards for the selection of health Measurement INstruments) initiative for systematic reviews of patient-reported outcome measures (PROMs). The evaluation was structured across three steps: Assessment of risk of bias; Quality of measurement properties and Summary of evidence; and Grading of Recommendations Assessment, Development and Evaluation (GRADE) guidelines. Results. Nine assessment instruments were included in this review. Following our analysis, the CSQ-VR and VRNQ received a Grade A recommendation, as they met sufficient criteria for both internal consistency and structural validity with high methodological quality. Few instruments have reported validations in specific clinical populations related to neurorehabilitation, including individuals with neurological conditions such as brain injury, multiple sclerosis (MS), migraine-associated vestibulopathy, vestibular disorders or cognitive impairment, indicating a critical gap in scale generalizability across clinical contexts. Conclusions. Despite the increasing use of immersive VR, few CS assessment tools have been designed and validated, reaching the CSQ-VR and VRNQ a Grade A recommendation. Among the nine identified assessment instruments, only the SSQ, MSAQ, CSQ-VR, VRNQ MSSQ and SEQ have been employed in samples with neurological disorders. There is a critical need for standardized CS VR-specific tools with robust psychometric properties to ensure safe and effective implementation in neurorehabilitation settings. Full article

Advancements in data storage and data processing technologies has compelled higher education institutions to optimise the use of their data. Many universities globally have begun to implement learning analytics at their institutions to better understand and improve teaching and learning. African higher education institutions have been slow to implement learning analytics despite the continued accumulation of digital data. The research related to this study presents a dataset of Information Systems and Technology (IS&T) students from the University of KwaZulu-Natal, a South African university. The dataset comprises approximately 14,000 registered student records from 10 IS&T courses, primarily consisting of demographic data, academic performance (including past IS&T courses and school records), and Learning Management System (LMS) interaction data. The dataset exhibits an imbalance, characterised by a higher proportion of students who have successfully completed courses compared to those who have not. The dataset will be of interest to researchers engaged in learning analytics application studies, including early pass/fail prediction and grade classification, as well as those who want to test their techniques on a real-world dataset. Full article

Humanity is at a critical juncture in its response to environmental issues. Coastal land spaces are under threat from rising sea levels and storm surges accelerating erosion and degradation. Children have an important role in sustaining a viable environmental future. Education for sustainability in early childhood (EC) nature-based settings has the potential to disrupt the current crisis by deepening children’s understanding of the environment. Many educators who practice nature pedagogy in early childhood education (ECE) shy away from using technological tools despite our existence in a time of artificial intelligence and digitalisation, some of which is becoming evident in EC sustainability education. This paper will consider the use of blue spaces that incorporate the waters, sands, and coastal land adjacent to the water’s age for EC sustainability teaching and learning. It will focus on questioning the role of technologization, particularly technological tools, on the forms of sustainability education that preschool children experience while in nature-based settings. Interrogating recent research of nature-based kindergartens undertaken at Australian coastal contexts, and drawing on seminal international documentation, it will focus on the development of young children’s empathy and ‘ethos of care’ for living things, their considerations of local ecosystems, and their growing understandings of the interrelationships between elements of their environment. The paper will then consider how the application of technological tools intersects with sustainability education in the context of blue spaces. The research highlights the importance of the educator in the development of interactive, learner-centred opportunities that not only enable investigative, action-adapted learning but also fosters independent learners who are responsive to their natural environment. The implication of this research is that further considerations of technologization and children’s environmental agency through a play-based, emergent curriculum are necessary. Full article