Search Results (333)

The rapid development of generative artificial intelligence (GenAI) is transforming both the health sector and health profession education, although AI-based systems have existed in these sectors for decades. GenAI-integrated virtual agents and simulations now play novel and critical roles in simulation-based education and are potential solutions to enhance the adaptability of health profession education. This systematic review was conducted using the PRISMA guidelines and explores how GenAI-integrated virtual agents and simulations are being applied in health profession education, with a particular focus on their educational impact, technical features and functionalities, and current limitations. This review aims to synthesize the pedagogical value and technological design of GenAI-integrated simulations and to inform health professionals and educators about the effective use, impact, and challenges of GenAI in health education simulations. A total of 16 papers were reviewed. Results show that GenAI-integrated virtual agents and simulations have potential to enhance clinical communication, diagnostic accuracy, multilingual interactions, and learner confidence for health profession education. Related theoretical, technological, and educational implications of generative AI-integrated virtual agents and simulations are discussed to inform future design and application. Limitations include insufficient educational effectiveness, response accuracy issues, and unresolved ethical and privacy concerns. Future studies should focus on long-term efficacy, ethical considerations, and optimizing AI–human collaboration in various health profession education contexts. Full article

Generative Artificial Intelligence (GenAI) in cardiac surgery refers to the integration of advanced computational models, such as Large Language Models (LLMs), to automate and enhance clinical decision-making, preoperative risk assessment, and surgical education. In the context of surgical training, it functions as a personalized pedagogical tool that supports various learning activities, ranging from information acquisition and clinical inquiry to procedural practice, while requiring rigorous human oversight to ensure patient safety and clinical accuracy. (1) Background: Generative Artificial Intelligence (GenAI) is increasingly integrated into health professions education, offering new opportunities for learning; however, its specific application and pedagogical mapping in high-stakes fields such as cardiac surgery remain underexplored. This systematic review investigates how GenAI is utilized in cardiac surgery and surgical education, aligning these uses with Laurillard’s six learning types. (2) Methods: Following the PRISMA 2020 guidelines, we searched the Web of Science Core Collection for studies on GenAI in cardiac surgery, resulting in 42 studies that met the inclusion criteria. Study quality was appraised using the Medical Education Research Study Quality Instrument (MERSQI). (3) Results: GenAI applications most frequently supported clinical inquiry (93.8%) and practice (68.8%), demonstrating expanding efficiency across commercial and open-source models (including ChatGPT-4o, Gemini AI, and emerging reasoning architectures such as DeepSeek) for knowledge acquisition and medical production. While it significantly improves individualized learning and preoperative assessment workflows, its practical role in Discussion and Collaboration remains heavily underutilized, highlighting a distinct shift toward individualized solo professional workflows. (4) Conclusions: GenAI provides a transformative and scalable approach to cardiac surgical training by offering personalized and accessible knowledge retrieval. However, clinical educators and governance bodies must deliberately balance these immediate productivity benefits with long-term concerns regarding structural “hallucinations,” data verifiability, and the preservation of collaborative competencies within modern multidisciplinary Heart Teams. Full article

The construction industry remains one of the most perilous, where hazard identification is often inconsistent. Hazards are still missed when teams rely mainly on traditional approaches like checklists, Job Safety Analysis (JSAs)/Job Hazard Analysis (JHAs), and individual experience. This study evaluated whether a GEN-AI-assisted approach improved hazard identification performance compared with traditional approaches, using expert-verified ground truth for scoring. A quantitative within-subjects experiment was conducted with 51 participants. Each participant completed hazard identification in four conditions: traditional–pre, GEN-AI–pre, traditional–post, and GEN-AI–post, with a short training session on hazard identification delivered between the pre- and post-stages. Effectiveness was measured using the F1 score, combining both precision and recall. For analysis, traditional and GEN-AI performance were compared at each stage using paired-sample t-tests, and the overall pattern was tested using a 2 × 2 repeated measures ANOVA. The results showed that GEN-AI support produced significantly higher performance than the traditional approaches at both stages (p < 0.05). The repeated measures ANOVA confirmed a strong overall method effect. However, the overall intervention effect was small, and the method × intervention interaction was negligible, with no statistically significant change over time (p > 0.05). Overall, the findings indicated that GEN-AI support improved hazard identification accuracy relative to traditional approaches in this dataset, with limited evidence of additional gains from the training intervention. This study contributes towards providing empirical evidence that GEN-AI improves hazard identification and strengthens proactive prevention, but final outputs need human validation. Full article

Generative artificial intelligence (genAI) enables educators to build custom learning tools, but the feasibility and impact of educator-driven, AI-assisted development (“vibe coding”) in medical education remain unclear. This study describes the rapid development of a custom ECG learning application using Gemini 3.1 Pro, evaluates its association with exam performance using difference-in-differences (DiD) and triple-difference (DDD) analyses, and assesses student perceptions with the user version of the Mobile App Rating Scale (uMARS). The app was implemented at one WWAMI site (intervention) with five sites as controls; aggregate performance from two first-year medical student cohorts (E24 vs. E25) was analyzed, comparing ECG-focused (focal) to non-ECG (baseline) exam items. DDD effects were inconsistent across exams, with no overall pooled effect on focal performance relative to baseline versus controls. In contrast, students rated the app highly (overall uMARS 4.57/5), particularly for quiz customization and waveform annotations. These findings support the feasibility of rapidly building and deploying tailored educational tools via genAI-assisted workflows and suggest strong perceived usability and acceptability among students. However, the study did not demonstrate a definitive short-term learning effectiveness effect on exam performance. Vibe coding is therefore positioned as a practical model for faculty-driven, context-specific educational innovation that requires further evaluation across broader implementations. Full article

The rapid adoption of Generative Artificial Intelligence (GenAI) tools is transforming learning practices in higher education, raising important questions about their educational value and impact on student learning. This study examines how university students use GenAI tools in both academic and everyday contexts, with emphasis on usage patterns, applications and motivations. A large-scale voluntary survey was conducted with 788 undergraduate students from a single public university in Greece, with respondents drawn from multiple schools and disciplines. Data were collected through an online questionnaire and analyzed using descriptive and inferential statistical methods to explore frequency of use, application categories and motivations for engagement with GenAI tools. The results indicate a high level of reported GenAI engagement among the participants, with ChatGPT emerging as the most frequently used tool. Students primarily rely on GenAI tools for information searching, understanding academic content and supporting academic tasks, while creative and entertainment-related uses are less frequent. Overall, the findings suggest that students perceive GenAI tools as useful for learning support and efficiency improvement. The results indicate that GenAI tools are becoming integrated into students’ reported learning practices. They also highlight the need for clear pedagogical guidelines and systematic AI literacy integration in teaching and learning. Full article

The integration of Generative Artificial Intelligence (GenAI) in higher education has opened new possibilities for optimizing administrative and academic services, particularly in contexts characterized by high-demand communication processes. Within the framework of service science, this study addresses the challenge of efficiently managing high volumes of email inquiries in a university master’s program, aiming to improve service quality and operational efficiency. The study examines the implementation of GenAI-based assistants, specifically NotebookLM and custom Gem AI assistants, trained in regulatory, curricular, and historical data from the University Master’s in Teacher Training at Rey Juan Carlos University. A mixed analytical approach is adopted, combining elements of data science to quantify efficiency gains and service science to analyze organizational and service-related transformations. The implementation of GenAI assistants contributes to improved response times, enhanced accuracy of information provided, and a reduction in administrative workload. The results suggest that GenAI can support the scalability and quality of academic administrative services when integrated within a structured service framework. However, its effective adoption requires careful consideration of ethical, organizational, and governance dimensions to ensure sustainable and responsible implementation. Full article

In early childhood initial teacher education, a growing number of mature-aged students with diploma qualifications and years of professional experience are undertaking their early childhood teacher degrees through online modes. Given the national staff shortage of early childhood teachers and the important role of higher education in professional development, it is crucial to support these students’ success in their online learning. Drawing on the critical reflection theory and the notions of “reflection-in-action” and “reflection-on-action”, this autoethnographic study examines a university lecturer’s perspective on the challenges of teaching mature-aged students in online Bachelor of Early Childhood Education programs. Four themes have been identified from the current study: the need for step-by-step technical support for the online learning system; acknowledgment of students’ practical experience contributes to online tutorial classrooms; the need for guidance for ethical and responsible use of Generative Artificial Intelligence (GenAI) in class discussions; and interactive dialogic guidance to support their assessment preparation. This study also included specific pedagogical adaptations to support these students, including offering technical support to assist mature-aged students in transitioning to university study, drawing on students’ professional knowledge to promote active engagement, providing interactive guidance to support understanding of assignment instructions, integrating open discussions about the use of GenAI in online class activities, and asking follow-up questions to encourage critical thinking. This study deepens our understanding of how university educators support mature-aged ECE students in their online learning through tailored pedagogical adaptations that align with their unique needs. Full article

The study examines discrepancies between personally reported and declared use of generative artificial intelligence (GenAI) among university students from a public university located in the Peruvian Andes, operationalized as the AI Use Gap, an exploratory discrepancy indicator based on two self-reported measures. Drawing on a sequential explanatory mixed-methods design, the study combines survey data (N = 150), experimental vignette evaluations, and qualitative follow-up interviews to explore how students manage the visibility and disclosure of AI use in academic contexts. Findings indicate relatively high levels of AI use alongside a consistent discrepancy between personally reported and declared use, suggesting patterns of differential reporting across contexts. Quantitative analyses did not show clearly differentiated exploratory relational patterns between the AI Use Gap and the psychosocial/contextual indicators examined, including perceived stigma, concealment, normative ambiguity, and peer pressure. Given the exploratory nature and limited internal consistency of the contextual indicators, these findings were interpreted cautiously as provisional exploratory patterns rather than as evidence of stable psychosocial relationships. Qualitative findings suggest that AI disclosure practices are shaped by socially evaluative and context-dependent processes, including fear of judgment, uncertainty regarding acceptable AI use, and selective disclosure strategies. Participants frequently described AI use as widespread but not consistently disclosed across academic settings. Overall, the findings suggest that discrepancies between AI use and disclosure may be better understood as forms of visibility management shaped by institutional ambiguity and social expectations rather than by stable individual-level characteristics alone. Rather than validating stable psychosocial mechanisms, the study explores an emerging and context-sensitive phenomenon using provisional contextual indicators intended to capture heterogeneous patterns of perception and disclosure. The study contributes to ongoing discussions regarding transparency, academic integrity, and the social regulation of AI use in higher education, particularly in under-researched Global South contexts. Full article

Despite the widespread adoption of educational technologies, a critical gap remains in Generative Artificial Intelligence (GenAI) tools that are both pedagogically grounded and responsive to the practical needs of teachers and students. This study examines the use of the Scaffolding Cognitive Artificial Intelligence (SCAI) chatbot to support students’ pre-class preparation and classroom engagement in secondary-school physics. The SCAI chatbot was designed by applying scaffolding theory and aligning chatbot interactions with the educational curriculum, with the aim of providing adaptive explanations, formative questioning, and teacher-facing preparation analytics. A Design Science Research (DSR) approach with a mixed-methods design was employed. Quantitative data were collected through pre-class preparation tests, while system-generated log data captured student interaction patterns. Qualitative data were obtained through semi-structured interviews with both teachers and students. The findings suggest that SCAI-supported preparation was associated with higher student preparation and more active engagement when compared with textbook-based preparation. Interview and log data further indicated that the chatbot helped simplify difficult concepts, support students’ confidence, and provide teachers with useful information about students’ progress before classroom instruction. However, because the study was conducted in a limited educational context and the intervention combined AI-supported interaction, scaffolding, flipped preparation, and teacher analytics, the findings should be interpreted as exploratory evidence of a meaningful association rather than definitive proof of causation. Overall, the study contributes to understanding how a scaffolding-based GenAI chatbot can support pre-class preparation and teacher-informed instructional planning. Full article

The rapid integration of generative artificial intelligence (GenAI) into education across all sectors has prompted a proliferating body of scholarship addressing the ethical, social, and environmental implications of these technologies. This narrative literature review synthesises international empirical, conceptual, and policy literature published between 2022 and 2026 to trace the evolving story of ethical concerns surrounding GenAI in education. Drawing on the moral philosophy of consequentialism, particularly the utilitarian ethics of John Stuart Mill, the review analyses six interconnected domains of ethical concern: environmental sustainability and the carbon footprint of AI infrastructure; algorithmic bias, ideological encoding, and the reproduction of misinformation; user dependency and the erosion of learner agency; the displacement of critical and creative thinking; data privacy and surveillance; and the orientation of major GenAI platforms toward profit-driven and capitalistic outcomes. Unlike systematic reviews that privilege methodological replicability, this narrative review foregrounds interpretive synthesis, tracing how the ethical discourse has shifted from early alarm and prohibition toward more nuanced frameworks for responsible integration. The review identifies a consequentialist tension at the heart of the debate: while GenAI offers measurable benefits in personalisation, accessibility, and efficiency, these gains must be weighed against distributed harms that disproportionately affect vulnerable populations, the natural environment, and the epistemic foundations of education itself. The review concludes with a set of guidelines for the ethical use of GenAI in educational contexts, grounded in the literature synthesised in the article. Full article

The rapid advancement of Generative Artificial Intelligence (Gen-AI) is fundamentally reshaping labor markets, creating an urgent need for higher education institutions to adapt their program capacities and curricula. This paper proposes a data-driven Robust Multi-Objective Planning (RMOP) framework to translate heterogeneous Gen-AI labor shocks into actionable, program-level decisions regarding enrollment scaling and curriculum design. Grounded in O*NET micro-task structures, we model occupational evolution as a dynamic system of substitution, augmentation, and insulation driven by logistic technology diffusion. Our simulations across STEM, trade, and arts occupations reveal sharply divergent trajectories: Information Security Engineers face a 62% total impact dominated by substitution, whereas Electricians retain over 80% insulation, and Musicians experience high exposure but low substitution. To bridge these macro-level forecasts with immediate institutional maneuvers, the framework couples an AI-adjusted Grey Model (GM(1,1)) demand model with a Program Effectiveness Index (PEI) to yield discrete enrollment policy levers (Expand, Contract, and Adjust). For curriculum optimization, we employ Ridge regression to rank employability-related curriculum drivers and NSGA-II to generate Pareto portfolios under competing institutional objectives, including employability, instructional cost, ethics, and environmental impact. Final implementable recommendations are selected through entropy-weighted TOPSIS, where student well-being and education equity are treated as supplementary decision criteria rather than direct prediction targets. In addition, an Automation Risk Score (ARS) and a K-means TC clustering module are used to illustrate potential transfer paths across broader institutional settings. Internal scenario checks show that the AI-adjusted GM(1,1) reduces average hold-out MAPE from 7.0% to 5.8% relative to the baseline GM(1,1), and that NSGA-II achieves slightly stronger Pareto coverage than MOPSO and MODE under the same curriculum-portfolio setting. These checks are interpreted as preliminary decision-support evidence rather than external predictive validation. Overall, RMOP is presented as a scenario-based decision-support framework that links Gen-AI occupational exposure, enrollment adjustment, and curriculum portfolio design. Full article

Artificial Intelligence (AI) is revolutionizing Construction Management (CM) through automation, predictive analytics, and real-time decision-making throughout the project lifecycle.This study aims to provide a comprehensive and structured synthesis of AI models and their applications in CM. This paper presents a systematic review of 191 peer-reviewed articles published between 2020 and 2025, aiming to integrate the current state of AI implementation in CM, focusing on AI methods and models and their applications in CM. Compared to previous reviews that take these factors individually or focus narrowly on specific techniques, this study offers a comprehensive taxonomy that systematically maps AI techniques against CM functions and integration platforms. The results reveal that AI applications are primarily concentrated in risk and safety management, decision support, and monitoring and control, while domains such as legal analytics, robotics, and cybersecurity remain underexplored. Furthermore, Computer Vision (CV) and Deep Learning (DL) dominate tasks such as safety monitoring and defect detection, whereas Machine Learning (ML) and optimization algorithms are widely applied in cost estimation and scheduling. It also addresses developments rarely covered in construction research, including Generative AI (Gen-AI), Explainable AI (XAI), and transformer models, presenting a strategic framework for the widespread adoption of AI in the construction environment. This study contributes a structured taxonomy that systematically links AI models with CM functions and enabling technologies, providing a comprehensive synthesis of emerging trends and research gaps. Full article

In this paper, we describe and analyse the characteristics of news articles published in Spanish media which were written with assistance, of different types and to varying extents, from generative AI. We pursue three objectives: to identify how AI is being used, to examine the formal and textual characteristics of articles produced in this way, and to determine the degree of transparency with which the use of generative AI is communicated to readers. Using content analysis techniques, 120 articles published in different newspapers were studied, including articles written entirely by AI, written with the help of AI, and resulting from other uses of AI. Our analysis shows different results depending on the use of AI. The first group exhibits standardised writing, repetitive structures, and fewer sources. The second group shows less standardisation, more contextual information, and more journalistic errors. Despite relying on a repetitive structure, the third group does not display standardised writing. Regarding transparency, this study reveals that not all texts disclose AI use, and when they do, it appears separately from the byline or within the text. This study is pioneering due to the breadth of the analysed sample and our examination of different uses of AI in the Spanish news media. Full article

Generative artificial intelligence (GenAI) is rapidly reshaping higher education, yet barriers to its adoption across different disciplines and institutional roles remain underexplored. The existing literature frequently attributes adoption barriers to individual-level factors such as perceived usefulness and ease of use. This study instead investigates how such barriers are associated with structural conditions. Drawing on a multi-method survey analysis of 272 academic and professional service (PS) staff at Russell Group university, we examine how disciplinary contexts and institutional roles influence perceived barriers. By integrating multinomial logistic regression (MLR), structural equation modelling (SEM), and semantic clustering of open-ended responses, we move beyond descriptive accounts to develop a multi-level account of GenAI adoption. Our findings reveal patterned differences: non-STEM academics primarily report ethical and cultural barriers related to academic integrity, whereas STEM and PS staff disproportionately emphasize institutional, governance, and infrastructure constraints. We conclude that GenAI adoption barriers are deeply embedded in organizational ecosystems and epistemic norms, while also reflecting individual experiences and other unmeasured factors, suggesting that universities must move beyond generalized training to develop role-specific governance and support frameworks. Full article