Search Results (1,404)

Background/Objectives: The adoption of artificial intelligence (AI) in healthcare, particularly in high-stakes environments such as operating rooms (ORs), is expanding rapidly. While AI has the potential to enhance patient safety and clinical efficiency, it may also trigger anxiety among healthcare professionals due to uncertainties around job displacement, ethical concerns, and system reliability. This study aimed to examine the relationship between AI-related anxiety and patient safety attitudes among OR professionals. Methods: A descriptive, cross-sectional research design was employed. The sample included 155 OR professionals from a university and a city hospital in Turkey. Data were collected using a demographic questionnaire, the Artificial Intelligence Anxiety Scale (AIAS), and the Safety Attitudes Questionnaire–Operating Room version (SAQ-OR). Statistical analyses included t-tests, ANOVA, Pearson correlation, and multiple regression. Results: The mean AIAS score was 3.25 ± 0.8, and the mean SAQ score was 43.2 ± 10.5. Higher AI anxiety was reported by males and those with postgraduate education. Participants who believed AI could improve patient safety scored significantly higher on AIAS subscales related to learning, job change, and AI configuration. No significant correlation was found between AI anxiety and safety attitudes (r = −0.064, p > 0.05). Conclusions: Although no direct association was found between AI anxiety and patient safety attitudes, belief in AI’s potential was linked to greater openness to change. These findings suggest a need for targeted training and policy support to promote safe and confident AI adoption in surgical practice. Full article

In this study, bone detection was performed using the YOLO algorithm on a dataset comprising photographs of the scapula, humerus, and femur from cattle, horses, and dogs. Subsequently, convolutional neural networks (CNNs) were employed to classify both the bone type and the species. Trained on a total of 26,148 images, the model achieved an accuracy rate of up to 97.6%. The system was designed to operate not only on mobile devices but also in an offline, “closed model” version, thereby enhancing its applicability in forensic medicine settings where data security is critical. Additionally, the application was structured as a virtual assistant capable of responding to users in both written and spoken formats and of generating output in PDF format. In this regard, this study presents a significant example of digital transformation in fields such as veterinary anatomy education, forensic medicine, archaeology, and crime scene investigation, providing a solid foundation for future applications. Full article

This study critically examines the integration of artificial intelligence (AI) into education through the lens of Marx’s theory of alienation, engaging with contemporary critiques of digital capitalism and academic labour. Drawing on an exploratory survey of 395 educators in Northern Cyprus, a context of early-stage AI adoption, the paper identifies four distinct forms of alienation exacerbated by AI: from the product of academic labour, from the educational process, from professional identity (species-being), and from interpersonal relations. Findings suggest that while educators who view AI more positively tend to report lower levels of alienation, particularly with respect to their pedagogical outputs, this association is tentative due to the low reliability of the AI perception scale (Cronbach’s α = 0.42). The results, therefore, serve as hypothesis-generating rather than conclusive. Situating the empirical findings within broader critiques by Noble, Hall, Preston, and Komljenovic, the study highlights how algorithmic governance, commercial platform logics, and data-driven performance regimes threaten teacher autonomy, creativity, and relationality. The paper concludes with a call for participatory governance, ethical oversight, and human-centred design to ensure that AI integration supports, not supplants, educators. In doing so, it contributes to critical debates on the ethical sustainability of digital education under conditions of intensifying automation. Full article

This paper describes a crowdsourced experiment in which participants were asked to judge which of two simultaneously presented facial images (one real, one AI-generated) was fake. With the growing presence of synthetic imagery in digital environments, cognitive systems must adapt to novel and often deceptive visual stimuli. Recent developments in cognitive science propose that some mental processes may exhibit quantum-like characteristics, particularly in their context sensitivity. Drawing on Tezzin’s “generalized fair coin” model, this study applied Contextuality-by-Default (CbD) theory to investigate whether human judgments of human faces exhibit quantum-like contextuality. Across 20 trials, each treated as a “generalized coin”, bootstrap resampling (10,000 iterations per coin) revealed that nine trials demonstrated quantum-like contextuality. Notably, Coin 4 exhibited strong context-sensitive causal asymmetry, where both the real and synthetic faces elicited inverse judgments due to their unusually strong resemblance to one another. These results support the growing evidence that cognitive judgments are sometimes quantum-like contextual, suggesting that adopting comparative strategies, such as evaluating unfamiliar faces alongside known-real exemplars, may enhance accuracy in detecting synthetic images. Such pairwise methods align with the strengths of human perception and may inform future interventions, user interfaces, or educational tools aimed at improving visual judgment under uncertainty. Full article

Phishing attacks are an increasingly common cybersecurity threat and are characterized by deceiving people into giving out their private credentials via emails, websites, and messages. An insight into students’ challenges in recognizing phishing threats can provide valuable information on how AI-based detection systems can be improved to enhance accuracy, reduce false positives, and build user trust in cybersecurity. This study focuses on students’ awareness of phishing attempts and evaluates AI-based phishing detection systems. Questionnaires were circulated amongst students, and responses were evaluated to uncover prevailing patterns and issues. The results indicate that most college students are knowledgeable about phishing methods, but many do not recognize the dangers of phishing. Because of this, AI-based detection systems have potential but also face issues relating to accuracy, false positives, and user faith. This research highlights the importance of bolstering cybersecurity education and ongoing enhancements to AI models to improve phishing detection. Future studies should include a more representative sample, evaluate AI detection systems in real-world settings, and assess longer-term changes in phishing-related awareness. By combining AI-driven solutions with education a safer digital world can created. Full article

Artificial general intelligence (AGI) is often depicted as a transformative breakthrough, yet debates persist on whether current advancements truly represent general intelligence or remain limited to domain-specific applications. This study empirically maps AGI-related research across subject areas, geographies, and United Nations Sustainable Development Goals (SDGs) via machine learning-based analysis. The findings reveal that while the AGI discourse remains anchored in computing and engineering, it has diversified significantly into human-centered domains such as healthcare (SDG 3), education (SDG 4), clean energy (SDG 7), industrial innovation (SDG 9), and public governance (SDG 16). Geographically, research remains concentrated in the United States, China, and Europe, but emerging contributions from countries such as India, Pakistan, and Costa Rica suggest a gradual democratization of AGI exploration. Thematic expansion into legal systems, governance, and environmental sustainability points to AGI’s growing relevance for systemic societal challenges, even if true AGI remains aspirational. Funding patterns show strong private and public sector interest in general-purpose AI systems, whereas institutional collaborations are increasingly global and interdisciplinary. However, challenges persist in cross-sectoral data interoperability, infrastructure readiness, equitable funding distribution, and regulatory oversight. Addressing these issues requires anticipatory governance, international cooperation, and capacity-building strategies to ensure that the evolving AGI landscape aligns with inclusive, sustainable, and socially responsible futures. Full article

In recent years, the rapid growth of artificial intelligence (AI) has significantly transformed higher education, particularly among Generation Z students who are more open to new technologies. Tools such as ChatGPT are increasingly being used for learning, yet empirical research on their acceptance, especially in Hungary, is limited. This study aims to explore the psychological, technological, and social factors that influence the acceptance of AI among Hungarian university students and to identify different user groups based on their attitudes. The methodological novelty lies in combining two approaches: partial least-squares structural equation modelling (PLS-SEM) and cluster analysis. The survey, based on the TAM and UTAUT models, involved 302 Hungarian students and examined six dimensions of AI acceptance: perceived usefulness, ease of use, attitude, social influence, enjoyment and behavioural intention. The PLS-SEM results show that enjoyment (β = 0.605) is the strongest predictor of the intention to use AI, followed by usefulness (β = 0.167). All other factors also had significant effects. Cluster analysis revealed four groups: AI sceptics, moderately open users, positive acceptors, and AI innovators. The findings highlight that the acceptance of AI is shaped not only by functionality but also by user experience. Educational institutions should, therefore, provide enjoyable and user-friendly AI tools and tailor support to students’ attitude profiles. Full article

This study examines the role of algorithmic fairness in alleviating digital financial stress among consumers across OECD countries, utilizing panel data spanning from 2010 to 2023. By introducing a digital financial stress index—constructed from indicators such as household credit dependence, digital debt penetration, digital default rates, and financial complaint frequencies—the research quantitatively captures consumer financial anxieties within AI-driven e-commerce platforms. Employing two-way fixed-effects regression and system-GMM methods to address endogeneity and dynamic panel biases, findings robustly indicate that increased algorithmic fairness significantly reduces digital financial stress. Furthermore, the moderating analysis highlights digital literacy as a critical factor amplifying fairness effectiveness, revealing that digitally proficient societies derive greater psychological and economic benefits from equitable algorithmic practices. These results contribute to existing scholarship by extending discussions of algorithmic ethics from individual-level analyses to a macroeconomic perspective. Ultimately, this research underscores algorithmic fairness as a crucial policy lever for promoting consumer welfare, calling for integrated national strategies encompassing ethical algorithm governance alongside enhanced digital education initiatives within OECD contexts. Full article

This article critically analyses the use of generative Artificial Intelligence (GenAI)—specifically, the DALL·E system within the ChatGPT-4o environment—for creating visualisations of biblical scenes for teaching purposes. As part of a case study examining the Baptism of Jesus in the Jordan (Mt 3:13–17; cf. Mark 1:9–11; Luke 3:21–22; John 1:31, 34) and the Last Supper (Mt 26:17–30; cf. Mark 14:12–16; Luke 22:7–13), four AI-generated images are analysed. Two were created using general, non-specific prompts, while the other two were based on more precise queries containing references to Catholic symbolism and the images’ intended educational use. A comparison of these variants reveals a lack of theological depth and symbolic oversimplification in AI-generated images, as well as a tendency to reproduce Western cultural stereotypes. Despite their aesthetic appeal and quick availability, these images do not reflect the complexity of the biblical or spiritual contexts of the scenes depicted. This study aims to evaluate the theological, symbolic, and pedagogical value of AI-generated images and to provide practical recommendations for their responsible use in Bible didactics. In conclusion, the authors argue that GenAI can support biblical teaching when used consciously, critically, and reflectively. Full article

We developed and validated an animation-based assessment (ABA) method for evaluating high school students’ inquiry competencies in Taiwan’s 12-Year Curriculum. Contextualized in atmospheric chemistry involving methane and hydroxyl radicals, ABA integrated dynamic simulations, tiered multiple-choice and open-ended tasks, and process tracking on the CloudClassRoom platform, the assessment focused on measuring two inquiry skills: causal reasoning and critical thinking. The results of 26,823 students revealed that the ABA effectively differentiated student performance across ability levels and academic disciplines, with open-ended items sensitive to higher-order reasoning. Gender difference was not observed, indicating the gender-free design of the developed ABA. While the ABA supports diagnostic insights, limitations need to be addressed, including the underassessment of modeling and creative experimentation skills. Therefore, it is necessary to include open modeling tasks and AI-powered semantic scoring. The developed ABA contributes a scalable, competency-aligned framework for inquiry-based science assessments. Full article

Emerging technologies present significant challenges for society as a whole. Among these, Artificial Intelligence (AI) stands out for its transformative potential, with the capacity to fundamentally reshape human thought, behavior, and lifestyle. This article seeks to explore the business-oriented perspective on how AI should be approached in Higher Education (HE) in order to serve the commercial objectives of companies. The motivation for this inquiry stems from recurrent criticisms directed at HE institutions, particularly their perceived inertia in adopting innovations, resistance to change, and delayed responsiveness to evolving labor market demands. In this context, the study examines what businesses deem essential for universities to provide in the context of AI familiarity and examines how companies envision future collaboration between the business sector and Higher Education institutions in using AI for business applications. Adopting a qualitative research methodology, this study conducted interviews with 16 middle-management representatives from international corporations operating across diverse industries. The data were analyzed using Gioia’s methodology, which facilitated a structured identification of first-order concepts, second-order themes, and aggregate dimensions. This analytical framework enabled a nuanced understanding of business expectations regarding the role of HE institutions in preparing graduates capable of meeting economic and commercial imperatives under the pressure of AI diffusion. Full article

This study investigates students’ perceptions of Generative Artificial Intelligence (GenAI), with a focus on Higher Education institutions in Northern Ireland and India. We collect quantitative Likert ratings and qualitative comments from 1211 students on their awareness and perceptions of AI and investigate variations in attitudes toward AI across institutions and subject areas, as well as interactions between these variables with demographic variables (focusing on gender). We found the following: (a) while perceptions varied across institutions, responses for Computer Sciences students were similar, both in terms of topics and degree of positivity; and (b) after controlling for institution and subject area, we observed no effect of gender. These results are consistent with previous studies, which find that students’ perceptions are predicted by prior experience; crucially, however, the results of this study contribute to the literature by identifying important interactions between key factors that can influence experience, revealing a more nuanced picture of students’ perceptions and the role of experience. We consider the implications of these relations, and further considerations for the role of experience. Full article

Undergraduate design education faces a structural contradiction characterized by high cognitive load (CL) and relatively low innovation output. Meanwhile, existing generative AI tools predominantly emphasize the generation of visual outcomes, often overlooking the logical guidance mechanisms inherent in design thinking. This study proposes a Dual-Path teaching model integrating critical reconstruction behaviors to examine how AI enhances design thinking. It adopts structured interactions with the DeepSeek large language model, CL theory, and Structural Equation Modeling for analysis. Quantitative results indicate that AI-assisted paths significantly enhance design quality (72.43 vs. 65.60 in traditional paths). This improvement is attributed to a “direct effect + multiple mediators” model: specifically, AI reduced the mediating role of Extraneous Cognitive Load from 0.907 to 0.017, while simultaneously enhancing its investment in Germane Cognitive Load to support deep, innovative thinking. Theoretically, this study is among the first to integrate AI-driven critical reconstruction behaviors (e.g., iteration count, cross-domain terms) into CL theory, validating the “logical chain externalization → load optimization” mechanism in design education contexts. Practically, it provides actionable strategies for the digital transformation of design education, fostering interdisciplinary thinking and advancing a teaching paradigm where low-order cognition is outsourced to reinforce high-order creative thinking. Full article

As digital transformation continues to redefine education, understanding how emerging technologies can enhance self-directed learning (SDL) becomes essential for learners, educators, instructional designers, and policymakers, as this approach supports personalized learning, strengthens student autonomy, and responds to the demands of more flexible and dynamic educational environments. This systematic review examines how artificial intelligence (AI) tools enhance SDL by offering personalized, adaptive, and real-time support for learners in online environments. Following the PRISMA 2020 methodology, a literature search was conducted to identify relevant studies published between 2020 and 2025. After applying inclusion, exclusion, and quality criteria, 77 studies were selected for in-depth analysis. The findings indicate that AI-powered tools such as intelligent tutoring systems, chatbots, conversational agents, and natural language processing applications promote learner autonomy, enable self-regulation, provide real-time feedback, and support individualized learning paths. However, several challenges persist, including overreliance on technology, cognitive overload, and diminished human interaction. These insights suggest that, while AI plays a transformative role in the evolution of education, its integration must be guided by thoughtful pedagogical design, ethical considerations, and a learner-centered approach to fully support the future of education through the internet. Full article

Creativity is the primary driver of our cultural evolution. The astonishing potential of artificial intelligence (AI) and its possible application in the creative process poses an urgent and dramatic challenge for humanity; how can we maximize the benefits of AI while minimizing the associated risks? In this article, we identify all forms of human–AI collaboration in this realm as cyber-creativity. We introduce the following two forward-looking scenarios: a utopian vision for cyber-creativity, in which AI serves to enhance and not replace human creativity, and a dystopian view associated with the pre-emption of all human creative agency caused by the rise of AI. In our view, the scientific community is called to bring its contribution, however small, to help humanity make steps towards the utopian scenario, while avoiding the dystopian one. Here, we present a decalogue of research challenges identified for this purpose, touching upon the following dimensions: (1) the theoretical framework for cyber-creativity; (2) sociocultural perspectives; (3) the cyber-creative process; (4) the creative agent; (5) the co-creative team; (6) cyber-creative products; (7) cyber-creative domains; (8) cyber-creative education; (9) ethical aspects; and (10) the dark side of cyber-creativity. For each dimension, a brief review of the state-of-the-art is provided, followed by the identification of a main research challenge, then specified into a list of research questions. Whereas there is no claim that this decalogue of research challenges represents an exhaustive classification, which would be an impossible objective, it still should serve as a valid starting point for future (but urgent) research endeavors, with the ambition to provide a significant contribution to the understanding, development, and alignment of AI to human values the realm of creativity. Full article