Search Results (5,764)

Generative Artificial Intelligence (GenAI) has the potential to bring substantial benefits to language education, making it essential to examine how teachers engage with these technologies in practice. This exploratory qualitative case study draws on semi-structured interviews with four in-service upper-secondary English teachers in Norway to examine the factors shaping their engagement with GenAI. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT), the study examined factors shaping teachers’ engagement with GenAI, including performance expectancy, effort expectancy, social influence, and facilitating conditions. Thematic analysis revealed a pattern of selective, context-sensitive use rather than straightforward adoption. While teachers recognised the potential of GenAI to support planning, idea generation, and formative feedback, their engagement was constrained by concerns about assessment validity, academic integrity, privacy, and institutional guidance. The findings suggest that teachers’ use of GenAI is shaped not only by perceptions of usefulness and ease of use but also by trust, assessment considerations, and the availability of clear policy frameworks. By using UTAUT as a qualitative analytical lens, this study contributes to research on technology acceptance and teacher agency by showing how teachers negotiate the use of GenAI in ways that reshape assessment practices and professional roles. The findings point to the need for clear institutional guidance, AI-resilient assessment practices, and targeted teacher education that supports ethical, pedagogically grounded use of GenAI. Full article

This study investigates the factors influencing employee acceptance and actual AI use intensity (frequency and routinization) by integrating the Technology Acceptance Model with organizational and psychosocial variables. Data were collected via an online survey of Romanian Generation Z participants with work experience (N = 272) between 10 May and 25 May 2025, and analyzed using PLS-SEM with a moderated mediation model. Perceived usefulness emerged as the strongest driver of attitude, intention, and AI use intensity. Organizational AI readiness increased perceived usefulness and was positively associated with psychological safety. Trust influenced both intention and AI use intensity and partially mediated the relationship between perceived usefulness and intention. Technostress was negatively associated with attitudes and weakened the positive relationship between psychological safety and perceived ease of use. By shifting the focus from intention to AI use intensity, the study refines acceptance theory for AI-enabled work and clarifies how organizational context, trust, and digital strain shape sustained and routinized AI use in daily work. Practically, the findings suggest that organizations should communicate AI value and task fit, foster psychologically safe learning climates, build trust through transparency and guidance, and actively mitigate technostress through training, workload design, and clear expectations. Full article

The adoption of sustainable technologies is strongly influenced by psychological and social factors, particularly for emerging solutions such as hydrogen fuel cell vehicles (HFCVs). These technologies embody the promise of environmental responsibility while simultaneously raising safety concerns. This study examines how value conflict—the internal tension between environmental attitudes and technological risk perception—influences the intention to adopt HFCVs. Data were collected through an online survey (N = 1330) using snowball sampling. Three attitudinal dimensions were examined—environmental commitment, technological risk perception, and adoption intention. Environmental commitment and risk perception represent the two underlying evaluative orientations whose discrepancy may generate internal value conflict. Based on these dimensions, a novel composite index, the Value Conflict Index (VCI), was constructed to capture the extent of this internal tension and its effect on adoption intention. Regression analyses show that both environmental attitudes and lower perceived risks are significant positive predictors of adoption intention. At the same time, VCI exerts an independent negative effect, confirming that internal dissonance reduces willingness to adopt. Women reported more substantial environmental commitment and higher perceived risks, leading to higher VCI values; however, moderation analysis indicates that gender does not change the behavioral impact of value conflict. These findings suggest that value conflict represents a general psychological barrier to the acceptance of sustainable technologies. Full article

AI-enabled technology (AI) has a transformational role in our modern society because it is increasingly used as an interaction partner, making anthropomorphism (tendency to ascribe human features to non-human agents) a central mechanism shaping how people evaluate, accept or resist AI systems. Existing technology acceptance models and anthropomorphism frameworks, however, offer limited guidance on how human-like attributes of AI translate into perceptions of usefulness, perceived control, perceived opportunity or threats, particularly across different levels of AI autonomy. Building on the theory of planned behavior, the technology acceptance model and threat rigidity model, this paper develops a mid-range conceptual framework of AI anthropomorphism grounded in universal social perception dimensions of warmth and competence. We integrate fragmented research to derive three core propositions and four corollaries that specify how warmth and competence attributions shape evaluative cognitions in relation to AI. The framework further identifies AI autonomy as a boundary condition under which anthropomorphic cues may either facilitate acceptance or trigger perceptions of pseudo-empathy, cognitive superiority and identity threat. By offering a parsimonious, theoretically informed model, this paper clarifies when anthropomorphism fosters acceptance versus resistance in human–AI interaction and provides a structured agenda for future empirical research and AI design aimed at fostering synergies and resilience in human–AI ecosystems. Full article

Drones have long been explored by commercial and military users for supply. While several systems offering small payloads in drone delivery have seen operational use, large-scale supply drones have yet to be adopted. A range of setbacks cause this, including technological and operational challenges that hinder their adoption. Here, we evaluate these challenges from a conceptual modelling perspective and forecast their applicability once these barriers are overcome. This study uses technology trend modelling and bibliometric activity mapping methodologies to predict the applicability of specific technologies that are currently identified as operational challenges. Specifically for supply drones, we model trends in technological improvements of battery technology and aircraft control, and project its focus on landing zone autonomy and powertrain. The prediction also focuses on the current state of hybrid power and higher levels of automation required for landing zone operations. These models are validated through several published case studies of small delivery drones and then applied to assess the feasibility and constraints of larger supply drones. A case study involving the conceptual design of a supply drone large enough to move a shipping container is presented to illustrate the critical technologies required to transition large supply drones from concept to operational reality. Key technologies required for large-scale supply drones have yet to build up a critical mass of research activity, particularly on landing zone autonomy and powertrain. Moreover, additional constraints beyond technological and operational challenges could include limitations in autonomy, certification hurdles, regulatory complexity, and the need for greater social trust and acceptance. Full article

Background: The dynamic development of information technologies, particularly in the fields of computer image analysis and artificial intelligence (AI) algorithms, plays an increasingly important role in orthodontic diagnostics. Cephalometric images constitute a fundamental element in orthodontic treatment planning. They contain encoded information related to the assessment of craniofacial growth and development, which is the focus of algorithms employing machine learning and process automation. Objectives: The aim of this paper is to present the current state of knowledge regarding the application of artificial intelligence methods in cephalometric image analysis, with particular emphasis on studies published between 2020 and 2025 in the Scopus and Web of Science databases. Results: Twenty key studies were included. The most commonly used models were convolutional neural networks (CNN), You Only Look Once (YOLO), Bayesian convolutional neural networks (BCNN), artificial neural networks (ANN), stacked hourglass networks, and Deep Neural Patchworks (DNP). In landmark detection tasks, the average location errors ranged from 1 to 2 mm compared to expert annotations, remaining within clinically acceptable limits. YOLO- and CNN-based systems achieved accuracy comparable to that of experienced orthodontists, while BCNN models additionally provided uncertainty estimates that improved clinical interpretability. In classification tasks, artificial neural network (ANN) models assessing cervical vertebral maturity (CVM) achieved an accuracy of up to 95%. In screening studies prior to orthognathic surgery, a multilayer perceptron combined with a regional convolutional neural network achieved 96.3% agreement with expert decisions. Conclusions: AI-based tools provide clinically acceptable accuracy in cephalometric analysis, with landmark detection errors typically ranging from 1 to 2 mm compared to expert assessment. These systems improve repeatability and significantly reduce analysis time, especially when used in semi-automated workflows. AI-based assessment of cervical vertebral maturity and surgical eligibility shows high agreement with expert decisions, confirming their role as reliable tools to support clinical decision-making. Nevertheless, broader validation in different patient populations is necessary before routine clinical implementation. Full article

The growing demand for functional and value-added foods has prompted interest in integrating nutrient-rich ingredients and novel encapsulated systems into traditional pasta products. This study aimed to develop and optimize a ravioli dough formulated with elephant foot yam flour (EFYF), wheat flour (WF) and amaranth flour (AF) using mixture design in response surface methodology and to create an innovative filling using encapsulated edible pearls produced from okra mucilage and moringa leaf powder through ionotropic gelation. The pearls and ravioli dough were analyzed for physicochemical, textural, color and nutritional characteristics. Cooked ravioli was investigated for cooking quality and sensory attributes. The optimized dough formulation (46.67 g EFYF, 43.32 g WF, 10 g AF) exhibited desirable hardness (4.64 ± 0.28 N), chewiness (0.40 ± 0.02 N), nutritional, physicochemical and color attributes. The edible pearls demonstrated moderate moisture content (21.18 ± 0.26%), high protein (26.25 ± 0.02%), crude fiber (2.60 ± 0.01%), dietary fiber (8.60 ± 0.52%), high ash content (14 ± 0.62%) and soft gel-like texture. The cooked ravioli showed a cooking time of 8 ± 1 min, high water absorption capacity (209.9 ± 0.34%), minimal solid loss (1.30 ± 0.21%) and favorable sensory scores across appearance, taste, texture and overall acceptability. The study concludes that incorporating encapsulated pearls and nutrient-dense flours can produce a functional, nutritionally enriched ravioli with good technological performance and consumer appeal. Full article

Digital transformation in public institutions is increasingly understood as a socio-technical and organizational process rather than a purely technological upgrade. This study presents the design of an ICT-based digital transformation roadmap aimed at improving administrative efficiency and citizen service delivery in a municipal public utility in Ecuador. A mixed-methods diagnostic approach was adopted, combining qualitative evidence from direct observation and a semi-structured interview with the head of the IT department, and quantitative data from a structured online survey administered to citizens. Baseline Key Performance Indicators (KPIs) were established using institutional records, service logs, and workflow analysis conducted over a three-month diagnostic window. Post-implementation KPI values are explicitly treated as ex ante projections, derived from process redesign analysis, benchmarking with comparable public utilities, and scenario-based assumptions, rather than empirically observed outcomes. The empirical results demonstrate high citizen readiness and acceptance of proposed digital services, including remote service portals, electronic invoicing, and automated support channels. The projected operational improvements—such as reductions in response and administrative processing times and increased digital transaction rates—are therefore presented as expected performance scenarios. A risk and alternative scenario analysis further examines how organizational constraints, resource availability, governance capacity, and change-management factors may moderate these outcomes. The study contributes a transparent and replicable framework for diagnosing digital readiness and planning ICT-driven transformation initiatives in resource-constrained public utilities, while emphasizing the need for future longitudinal validation using post-implementation data. Full article

The rapid evolution of technology characteristics has significantly influenced various sectors, including fashion, in which technology-enabled platforms have increasingly been utilized to enhance personalization and consumer engagement. This study investigates the effect of these characteristics on consumer behavior within fashion curation platforms. Integrating the task–technology fit and the unified theory of acceptance and use of technology models, this study examines key constructs using structural equation modeling. Data were collected via a week-long survey of 300 Korean consumers using fashion curation platforms. The findings reveal that technology characteristics exert a significant influence on task–technology fit and effort expectancy. Additionally, hedonic motivation, social influence, and facilitating conditions were pivotal in shaping behavioral intention. The novelty of this work lies in the fact that it extends the integrated model framework to a fashion curation context to offer a more nuanced understanding. Moreover, the findings provide practical insights for optimizing technology-enabled fashion platforms to boost user adoption and engagement. Full article

The accelerated integration of Generative Artificial Intelligence (GenAI) tools such as ChatGPT is transforming learner engagement, instructional design, and institutional governance in education. This systematic literature review synthesizes theory-driven scholarship on GenAI adoption and pedagogical use through the Antecedents–Decisions–Outcomes (ADO) framework, examining how cognitive, motivational, technological, and institutional factors collectively shape implementation and learning outcomes. Drawing primarily on the Technology Acceptance Model (TAM), Self-Determination Theory (SDT), and Institutional Theory, the review integrates complementary insights from Constructivist Learning and Diffusion of Innovations perspectives to conceptualize how antecedents influence decision-making and outcomes across educational settings. The findings indicate that learner motivation, perceived usefulness, digital literacy, and institutional readiness constitute key antecedents affecting GenAI adoption. Decision processes—spanning instructional design, ethical regulation, and pedagogical adaptation—mediate how these antecedents translate into practice. Outcomes reveal a dual trajectory: GenAI enhances personalization, feedback, and self-regulated learning, yet introduces challenges related to ethical ambiguity and overreliance. The review offers a conceptually integrated synthesis that bridges motivational, technological, and organizational perspectives, advancing a theoretical roadmap for ethical and sustainable GenAI adoption. For educators and policymakers, the findings emphasize transparent governance, faculty capacity-building, and equitable access to ensure that innovation remains aligned with pedagogical integrity and human-centered values. Full article

Integration of computer-based testing (CBT) in higher education has gained momentum globally, particularly in response to increasing demands for efficiency, scalability, and technological innovation in assessments. However, limited research explores how lecturers experience and make sense of CBT adoption, especially within resource-constrained educational systems. Grounded in the technology acceptance model (TAM), we employed a phenomenological approach to investigate lecturers’ perceptions of CBT. Eight lecturers from the largest university in Sub-Saharan Africa were purposively selected and individually interviewed. Thematic analysis, supported by human-AI collaboration, revealed diverse perspectives. The results show that lecturers perceived CBT as useful for improving efficiency, feedback speed, and assessment management, though concerns remained about infrastructure, authenticity, and equity. Ease of use strongly shaped these perceptions, with digitally skilled lecturers reporting a more positive experience. Attitudes toward CBT varied by discipline and pedagogical beliefs while influencing lecturers’ intention to adopt CBT. Thus, lecturers showed cautious but positive behavioural intention, particularly where CBT aligned with assessment needs and institutional support was adequate. The study contributes theoretically by extending the applicability of TAM to qualitative inquiry and practically by informing institutional strategies for improvement. Full article

This study examines intelligent judgment formation in blockchain-based public digital wallet systems within smart city environments. Drawing on an integrated framework that combines cognitive evaluation, social influence, and trust–risk appraisal, this research conceptualizes intelligent decision-making as a socially embedded and contextually enacted evaluative process rather than a fixed cognitive attribute. A structural equation modeling approach is employed to analyze the interrelationships among perceived usefulness, perceived ease of use, subjective norms, social electronic word of mouth, trust–risk appraisal, attitude, and behavioral intention. The findings indicate that socially distributed information signals play a dominant role in shaping evaluative integration and decision readiness, while cognitive and institutional appraisals operate primarily through mediated pathways. The results suggest that intelligent action in public digital service ecosystems emerges from the coordinated interaction of usability perception, institutional confidence, and socially calibrated information flows. These findings contribute to theoretical extensions of technology acceptance models in public governance contexts and offer implications for the design of socially responsive digital service infrastructures. 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/Objectives: Wearable technologies become increasingly important in surveillance of biometric parameters in pregnant women; however, early-stage usability data on wearable form factors specifically designed for pregnant women remain limited. This study evaluated the usability and acceptance of three non-functional wearable garment prototypes intended for future breathing exercise guidance and sleep-related applications. The prototypes incorporated sensor dummies that were technically capable of operation but intentionally deactivated for this usability pilot study. Methods: Eighteen pregnant women (second and third trimester) and twelve non-pregnant women tested three prototypes (Bra, Strap, Maternity Belt (hereafter Belt)) for 24 h. Usability was assessed using structured, participant-completed questionnaires addressing fit, material properties, comfort, and wear-related issues immediately after fitting (T0) and after 24 h of wear (T24). Analyses were descriptive and exploratory. Results: Among pregnant women, the Bra prototype showed consistently favorable usability ratings across multiple domains, particularly after extended wear, whereas the Belt demonstrated declining ratings related to fit and comfort over time. The Strap showed intermediate usability with specific strengths related to pressure and friction. In non-pregnant women, usability ratings were largely comparable between the Bra and Strap, with no clear preference pattern. No systematic differences were observed between pregnant and non-pregnant groups. Conclusions: This exploratory usability study suggests that garment form factor plays a critical role in acceptability during pregnancy. The Bra prototype demonstrated the most favorable usability profile among pregnant women, while the Belt revealed design limitations that warrant further modification. These findings provide formative guidance for the development of functional maternal wearables, with future studies integrating objective testing and validated measures to optimize performance and evaluate adherence in larger cohorts. Full article

As additive manufacturing (AM) expands into high-end industries, ensuring both technical performance and dimensional accuracy remains a challenge. This paper addresses the challenge of integrating recycled materials into the field of plastic extrusion additive manufacturing technologies by conducting a study on the evaluation and optimization of thermoplastic extrusion parameters to improve the dimensional accuracy of additively manufactured parts from virgin and recycled polyethylene terephthalate glycol (PETG, rPETG) and acrylonitrile styrene acrylate (ASA), both in virgin and recycled form. To carry out the study, 180 three-point bending specimens were additively manufactured on the QIDI Q1 Pro 3D printer by thermoplastic extrusion of PETG, rPETG, ASA, rASA (45 specimens for each type of material), using the following variable parameters: layer height deposited in one pass L_h = (0.10–0.20) mm and filling percentage—I_d = (50–100)%. After manufacturing the specimens, the dimensional characteristics that will be determined by measurement were defined: L—length, WA—width A, HA—height A, WA’—width A’, and HA’—height A’. Dimensional accuracy was assessed through 900 measurements using a DeMeet 400 coordinate measuring machine and analyzing the arithmetic means, dispersions, and mean square deviations. The results of the study confirm the superior dimensional stability of virgin materials (18.77–20.04%) compared to recycled materials. The analysis demonstrates that by optimizing the process parameters, filaments from recycled materials (rPETG and rASA) can achieve acceptable precision, with average deviations of 0.25–0.78% from the nominal dimensions. The present study validates the use of rPETG and rASA as a viable alternative for applications that do not require critical tolerances. Full article