Search Results (403)

Recent advances in large language models, tool-using agents, and financial machine learning are shifting financial automation from isolated prediction tasks to integrated decision systems that can perceive information, reason over objectives, and generate or execute actions. The paper develops an integrative framework for analysing agentic finance: financial market environments in which autonomous or semi-autonomous AI systems participate in information processing, decision support, monitoring, and execution workflows. The analysis proceeds in three steps. First, the paper proposes a four-layer architecture of financial AI agents covering data perception, reasoning engines, strategy generation, and execution with control. Second, it introduces the Agentic Financial Market Model (AFMM), a stylised agent-based representation linking agent design parameters such as autonomy depth, heterogeneity, execution coupling, infrastructure concentration, and supervisory observability to market-level outcomes including efficiency, liquidity resilience, volatility, and systemic risk. Third, it presents an illustrative empirical application based on event studies of AI-agent capability disclosures and heterogeneous market repricing. It argues that the systemic implications of AI in finance depend less on model intelligence alone than on how agent architectures are distributed, coupled, and governed across institutions. The empirical application is intentionally exploratory: it does not validate the full AFMM but shows how one observable expectations channel can be studied using public data. In the near term, the most plausible equilibrium is bounded autonomy, in which AI agents operate as supervised co-pilots, monitoring systems, and constrained execution modules embedded within human decision processes. Full article

As AI-powered learning tools become more common in educational settings, understanding their acceptance mechanisms is increasingly important. This study examines how the design attributes of AI museum guides—anthropomorphism, interactivity, and personalization—are associated with the acceptance intention and perceived learning outcomes among Chinese high school students with prior museum experience. Using structural equation modeling with 324 participants, we test whether these features relate to acceptance through two affective pathways: perceived warmth and anxiety reduction. The results reveal distinct patterns: anthropomorphism shows an indirect-only association with anxiety reduction through perceived warmth; interactivity is associated with anxiety reduction through responsive feedback; and personalization serves dual functions, enhancing both pathways. Anxiety reduction shows strong positive associations with both acceptance intention and perceived learning outcomes. The multi-group analysis shows that most pathways function equivalently across genders, with one exception where anxiety reduction more strongly predicts learning outcomes for females than males. These findings reveal distinct psychological functions within the Chinese educational context: anthropomorphism influences anxiety reduction exclusively through perceived warmth, while personalization and interactivity provide both affective and cognitive support. The implications for AI museum guide design in similar contexts are discussed. The generalizability to other cultural contexts and populations, such as Western students or adult learners, requires further investigation. Full article

Learning Analytics (LA) can support student success through dashboards and early-support interventions, but adoption depends on students’ willingness to allow educational data use under privacy and data-protection requirements. This study examines predictors of students’ willingness to allow educational data use for LA in higher education, focusing on perceived benefits, perceived risks, control and transparency expectations, and institutional trust. A cross-sectional survey was administered to engineering students (N = 109); after an instructed-response attention check, N = 102 valid responses were retained. Composite Likert constructs (BENEFIT, RISK, CONTROL, TRANSPARENCY, TRUST) and two willingness outcomes were analyzed: academic-support LA (WILL_ACAD) and broader aggregated institutional reporting under safeguards (WILL_BROAD). Willingness was high in both scenarios, and the paired difference did not reach statistical significance. Regression models showed that institutional trust was the strongest predictor of willingness across both use cases; perceived benefits additionally predicted willingness for academic-support LA, while perceived risk was a positive predictor in the broader-use model. Descriptive results indicated that students prioritize human review before any action affecting a student and strong security measures as key safeguards. These provide initial evidence to inform privacy-aware learning analytics governance in similar technical-university contexts; broader generalization across higher education requires replication across disciplines and institutions. Full article

Although interactive technologies such as interactive whiteboards are increasingly used in higher education, empirical evidence regarding their pedagogical role in statistics education remains limited. Existing studies often focus on technology adoption rather than instructional design. This study examines the effectiveness of interactive whiteboards when embedded within a pedagogically designed instructional framework aimed at supporting statistical thinking. A mixed-methods, quasi-experimental design with pre- and post-test measures (N = 126) was employed to compare learning outcomes and student perceptions in an introductory university statistics course taught either through traditional lectures or through an interactive approach emphasizing dynamic visualization, collective interpretation, and formative feedback. Mediation was tested using bootstrapped indirect effects and complemented by qualitative thematic analysis. Students in the interactive condition demonstrated significantly greater gains in statistical reasoning (Cohen’s d = 0.94, 95% CI [0.57, 1.31]), particularly in tasks involving data interpretation and reasoning about variability. Mediation analysis indicated that two student self-report measures—perceived clarity of instruction and formative feedback quality—together accounted for 63% of the total effect. The interactive format was especially beneficial for students with lower prior knowledge, reducing achievement gaps by 34%. These findings are consistent with the view that interactive technologies support conceptual learning most effectively when embedded in deliberate pedagogical designs promoting visualization, collective reasoning, and real-time feedback, highlighting the central role of instructional design over technological presence. Full article

Internationally, there are increased pressures for primary schools to meet academic curriculum outcomes primarily driven by performance metrics and targets. Sitting alongside this context are competing concerns for the decline in children’s play opportunities to bolster their overall health and wellbeing. Adopting play-based pedagogies in primary schools can infuse more play into children’s lives whilst meeting curriculum outcomes. Despite the perceived importance of play during childhood, play-based pedagogies are still mostly positioned as legitimate pedagogical approaches in prior to school settings. Given this landscape, this research seeks to understand contemporary educational research of play-based pedagogies in primary schools by conducting a scoping review. Through presenting a narrative account of the literature, and synthesising these ideas into broader themes, the research identified that there remains international interest in play-based pedagogies in the primary years of school but despite this, questions surrounding its legitimacy remain. This review and subsequent discussion surface potential next steps including a recommendation to increase empirical research on the adoption of play-based pedagogies in schools with consideration of using a ’Mosaic approach’ to data collection, as well as research focusing on the active and intentional role of the teacher. Lastly, as a way forward, the research brings to light the potential of creating a ‘space’ for the merging of two knowledge systems from two often siloed approaches to education—early childhood and primary—to create a new pathway. Such a pathway has potential to support continuity of learning, student engagement, children’s health, and wellbeing. Full article

Background: Oral and cranio-maxillofacial (OCMF) surgery is a complex specialty that requires detailed anatomical knowledge and, in fracture care, the ability to interpret imaging accurately. Mixed reality (MR) may improve spatial understanding in anatomy-based disciplines, but its value for teaching mandibular fracture classification remains uncertain. Methods: Medical and dental students at the University of Zurich were randomized 1:1 to classify four unilateral mandibular fractures using either MR or conventional two-dimensional (2D) imaging. Primary outcomes were perceived usefulness, ease of use, learning, and user satisfaction, assessed with a 15-item usability questionnaire. Secondary outcomes were fracture-classification accuracy and time to fracture classification. Results: Forty medical and dental students were included. Baseline characteristics were comparable between groups, and overall fracture-classification accuracy did not differ significantly between MR and 2D. Both groups became faster across successive cases, indicating a learning effect, although the 2D group completed classifications more quickly overall. MR participants reported higher scores for learning and user satisfaction, whereas the 2D group rated ease of use more favorably. Conclusions: MR increased user satisfaction but did not improve fracture-classification accuracy compared with 2D imaging. When integrated thoughtfully into OCMF education, MR may complement, rather than replace, conventional imaging approaches. Full article

Generative artificial intelligence (GenAI) is rapidly transforming design education by enabling new forms of human–AI collaborative learning. However, how GenAI relates to cognitive and motivational processes in design learning contexts remains insufficiently understood. This study examines whether integrating GenAI into visual design instruction is associated with improvements in domain-specific creative performance and explores the relationships among cognitive load, learning motivation, and learning outcomes. A six-week randomized instructional experiment was conducted with 120 undergraduate students majoring in visual communication design. Creative performance was evaluated through blind expert ratings, and the relationships among key variables were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that GenAI-integrated instruction is associated with higher levels of learning motivation, engagement, and expert-rated creative performance compared with traditional instruction, whereas cognitive-load indicators show comparatively limited predictive strength within the overall model. In addition, Integrated Teaching Alignment (ITA) significantly moderates the relationship between perceived relevance and learning satisfaction. These findings suggest that GenAI may function as an external cognitive support tool, with learning outcomes appearing to be associated with motivational and instructional factors, while cognitive-load indicators show comparatively limited associations within this instructional context. Full article

The rapid adoption of immersive and adaptive digital technologies is redefining sustainability education, but the mechanisms by which these technologies support perceived sustainability outcomes remain unclear. This paper models the Digital Experiential Learning Ecosystem (DELE), including simulation, AR/VR, gamification, AI personalization, and collaborative digital platforms, as a higher-order construct. It discusses its role in Perceived Sustainability Outcomes through learning engagement. Basing the study on the Stimulus-Organism-Response framework, the study hypothesizes that the digital ecosystem design can be viewed as an environmental stimulus, engagement as the organismic processing state, and Perceived Sustainability Outcomes as the developmental response. The results, obtained using Partial Least Squares Structural Equation Modeling (PLS-SEM), indicate that DELE is positively associated with learning engagement and Perceived Sustainability Outcomes. Learning engagement is found to be the leading mechanism through which digital experiential environments are converted into perceived sustainability outcomes, but a smaller yet significant direct structural relationship also remains. These findings indicate that digital transformation within the education sector creates sustainable value not only through technological sophistication but also through carefully planned engagement-based learning environments that support systems thinking, applied problem-solving, and adaptive readiness to work in multifaceted environments. The research also advances the body of research on sustainability education by developing a model of digital learning as an integrated ecosystem and by explaining the psychological and structural processes of perceived sustainability outcomes. Full article

Psychological stress is a major public health problem associated with adverse outcomes in physical and mental health. This study proposes an approach to predicting clinical stress levels using metabolic and endocrine biomarkers combined with machine learning models based on genetic algorithms. Data were obtained from 87 university students, including measurements of glucose, insulin, and cortisol, as well as perceived stress scores assessed using the Perceived Stress Scale (PSS). Stress levels were categorized into low ($n=5$), moderate ($n=22$), and high ($n=60$) classes, reflecting an imbalanced dataset. Feature engineering and genetic algorithm–based selection identified glucose concentration, the insulin–glucose ratio, and the insulin–cortisol ratio as the most relevant features. These were used to train XGBoost and Elastic Net models, which were evaluated using leave-one-out cross-validation. The XGBoost model achieved the best performance, with an accuracy of 0.77 and strong predictive capability for high stress levels. The results demonstrate the usefulness of machine learning based on metabolic biomarkers as an objective tool for stress assessment in psychological and clinical research. Full article

The rapid advancement of digital and mobile technologies has reshaped the educational landscape, fostering the adoption of interactive and learner-centered methodologies. Among these, immersive technologies such as Augmented Reality (AR), when coupled with next-generation wireless communication systems, hold the potential to revolutionize knowledge acquisition and student engagement. In this paper, we present the design and development of an AR-based educational tool specifically oriented to teaching concepts of fifth-generation (5G) mobile networks. The tool provides a real-time interactive visualization of 3D network components on mobile devices, enabling learners to explore 5G NSA/SA architectures in an accessible manner with real-world environments through mobile devices and their integrated cameras. The application was developed using Blender for 3D modeling and Unity as the rendering engine, incorporating the Vuforia SDK for marker-based AR tracking, and it was deployed on the Android operating system. Unlike traditional static approaches, the proposed solution enables learners to explore complex network architectures and key functionalities of 5G in an interactive and accessible manner. To assess its perceived effectiveness, quantitative surveys were conducted with both university and high school students, focusing on usability, engagement, and perceived learning outcomes. Results indicate that the tool is user-friendly, enhances motivation, and supports conceptual understanding as perceived by participants of 5G technologies. These findings highlight the potential of AR, supported by advanced wireless networks, as a pedagogical strategy to improve STEM education and foster technological literacy in the era of digital transformation. Full article

Cryptocurrency trading platforms operate in highly volatile, technology-intensive, and risk-sensitive environments, yet empirical evaluations of their performance from an information systems perspective remain limited. Prior studies applying the DeLone and McLean Information Systems Success Model (ISSM) have largely focused on traditional e-commerce and e-learning contexts, leaving its applicability to cryptocurrency exchanges underexplored. This study addresses this gap by examining how system quality, information quality, and service quality influence system use, user satisfaction, and net benefits in cryptocurrency trading platforms. This study employs a quantitative research design using survey data collected from 389 active Binance users in Indonesia through purposive sampling. The proposed ISSM-based research model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi Group Analysis (MGA) to assess the relationships among system quality, information quality, service quality, system use, user satisfaction, and perceived net benefits. The findings indicate that four of the nine hypothesized relationships are statistically supported. System quality emerges as the most influential determinant of both system use and user satisfaction, highlighting the importance of platform reliability, performance, and usability. Information quality also demonstrates a significant effect, whereas service quality exhibits a limited direct influence on user outcomes. Overall, system use and performance-related factors play a more critical role in driving perceived net benefits than service-related attributes. This study extends the DeLone and McLean ISSM to the context of cryptocurrency trading platforms and demonstrates its relevance in high-risk, blockchain-based financial environments. The results offer theoretical insights by refining the relative importance of ISSM constructs in fintech settings and provide practical guidance for developers and platform architects to prioritize system robustness, efficiency, and usability to enhance user satisfaction and engagement. Full article

Serious games and negotiation simulations such as the Phosphorus Negotiation Game (P-Game) are increasingly used to support sustainability-oriented education. To broaden accessibility, a smartphone-based version of the face-to-face P-Game was developed and is presented here. A comparative design integrating quantitative pre–post survey measures with analysis of open-ended responses was employed to examine self-reported knowledge gains and learning experiences among participants who completed the P-Game in face-to-face workshops and those who played the virtual version. Both formats were associated with significant increases in participants’ perceived understanding of phosphorus science and negotiation science/practice. Self-reported knowledge of phosphorus science increased by 92.3% (global face-to-face), 70.7% (Hungarian face-to-face), and 88.4% (online), with comparable gains observed in negotiation science and practice across groups. Qualitative findings complemented these results, indicating that while learning gains were broadly similar, the modes differed in experiential emphasis: face-to-face delivery elicited performance-oriented and socially embedded reflections, whereas the online format was more frequently described in terms of structured participation and reflective processing. User satisfaction with the virtual P-Game was high, reflected by a System Usability Scale (SUS) score above 80. Overall, the findings suggest that the virtual P-Game represents a viable and accessible complement to traditional face-to-face implementation, maintaining educational impact while extending reach. Further research with larger and more diverse participant samples is recommended to strengthen generalizability and explore long-term learning outcomes in sustainability contexts. Full article

Initial teacher education is increasingly expected to prepare pre-service teachers for ongoing professional development and teacher research, yet it remains unclear how systematically these competences are embedded in programmes and how prepared pre-service teachers feel. We used a mixed-methods design, combining a content analysis of 47 teacher education programmes at the University of Ljubljana with two cross-sectional surveys involving 443 pre-service (303 enrolled in first-cycle programmes and 140 in second-cycle programmes) and 138 in-service teachers. Programme documents frequently referenced professional development and research; however, research was more often stated as a competence outcome than as an explicit programme goal. In the surveys, pre-service teachers rated teaching competences highly, whereas perceived current competence was lower for professional development and lowest for teacher research, particularly for active research engagement. Across all items, pre-service teachers reported substantial gaps between current and required competence. Perceived current competence increased with study stage, while required competence varied less by stage. Required competence ratings were largely aligned between pre-service and in-service teachers, although pre-service teachers assigned higher ratings to selected research engagement items. The findings indicate a misalignment between curricular emphasis and perceived preparedness, and support stronger integration of practice-embedded professional learning and inquiry across coursework. Full article

Background: Artificial intelligence in nephrology has largely focused on predictive models for outcomes such as acute kidney injury (AKI), chronic kidney disease (CKD) progression, and transplant complications. Although these models demonstrate technical performance, their real-world clinical impact has remained limited because prediction alone rarely translates into coordinated clinical action. Clinical artificial intelligence agents represent workflow-native systems that operate in real time, interact bidirectionally with clinical environments, adapt to evolving patient and workflow states, and support coordinated clinical action rather than generating isolated predictions. This review proposes clinical artificial intelligence agents as a new paradigm for integrating artificial intelligence directly into nephrology workflows. Methods: We conducted a narrative synthesis of emerging literature on artificial intelligence systems, agentic artificial intelligence architectures, clinical decision support, and digital health infrastructures relevant to kidney care. Drawing from interdisciplinary sources in medicine, health informatics, and artificial intelligence research, we developed a conceptual framework describing the architecture, governance requirements, and evaluation principles of clinical artificial intelligence agents in nephrology. Results: Clinical artificial intelligence agents represent workflow-integrated systems capable of continuously perceiving patient data, reasoning under clinical constraints, planning tasks, and supporting coordinated clinical actions over time. We describe a layered architecture consisting of perception, cognition, planning and control, action, and learning components. Potential applications span the nephrology care continuum, including CKD management, AKI monitoring, dialysis and continuous renal replacement therapy (CRRT) optimization, kidney transplantation care coordination, glomerulonephritis management, and supervised patient-facing systems. Conclusions: Clinical artificial intelligence agents shift the role of artificial intelligence from isolated prediction toward longitudinal clinical orchestration. Future evaluation should prioritize workflow integration, time-to-action, clinician oversight, safety, and patient-centered outcomes rather than relying solely on traditional model performance metrics. This roadmap provides a conceptual foundation for the responsible development and clinical integration of agentic artificial intelligence systems in nephrology. Full article

This study examines the impact of ChatGPT-assisted learning on the academic achievement of hospitality and tourism students in Egyptian public universities, with particular emphasis on the mediating roles of perceived usefulness and self-regulated learning. Drawing conceptually on the Technology Acceptance Model (TAM), the study adopts a contextualized framework that emphasizes perceived usefulness while incorporating ChatGPT-assisted learning effectiveness as a learning-oriented driver within generative AI-supported educational environments. A quantitative research design was employed using an online survey administered to students who actively used ChatGPT for academic purposes. A total of 689 valid responses were collected from nine public universities and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed hypotheses. The findings indicate that ChatGPT-Assisted Learning Effectiveness (CALE) has a statistically significant and positive direct effect on academic achievement (AA; β = 0.386, T = 3.946, p < 0.001, 95% CI = 0.192–0.561) and strongly predicts perceived usefulness (β = 0.673, T = 9.274, p < 0.001, 95% CI = 0.581–0.742) and self-regulated learning (β = 0.707, T = 10.734, p < 0.001, 95% CI = 0.621–0.779). In turn, PU (β = 0.281, T = 3.854, p < 0.001, 95% CI = 0.142–0.417) and SRL (β = 0.220, T = 2.418, p = 0.016, 95% CI = 0.041–0.356) significantly enhance academic achievement. Mediation analyses further confirm that PU (β = 0.189, T = 2.366, p = 0.018, 95% CI = 0.031–0.284) and SRL (β = 0.156, T = 3.699, p < 0.001, 95% CI = 0.102–0.301) partially mediate the relationship between CALE and academic achievement. These findings offer important theoretical insights by contextualizing TAM’s performance-related logic within generative AI-driven learning environments and refining its application to academic outcome settings, while highlighting self-regulated learning as a critical explanatory mechanism. From a practical perspective, the study provides valuable implications for educators and policymakers by emphasizing the need to promote students’ perceived usefulness of ChatGPT and foster learner autonomy, positioning generative AI as a powerful pedagogical support tool for enhancing academic success in hospitality and tourism education. Full article