Search Results (51)

Indigenous students remain significantly underrepresented in medical education, contributing to persistent health inequities in their communities. Systemic barriers, including cultural isolation, inadequate resources, and biased curricula, hinder their success. But what if generative artificial intelligence (GAI) could be the game-changer? This scoping review explores the potential of generative artificial intelligence (GAI) in making medical education more inclusive and supportive for Indigenous students through a comprehensive analysis of existing literature. From AI-powered engagement platforms to personalised learning systems and immersive simulations, GAI can be harnessed to bridge the gap. While GAI holds promise, challenges like biased datasets and limited access to technology must be addressed. To unlock GAI’s potential, we recommend faculty development, expansion of digital infrastructure, and Indigenous-led AI design. By carefully harnessing GAI, medical schools can take a crucial step towards creating a more diverse and equitable healthcare workforce, ultimately improving health outcomes for Indigenous communities. Full article

This paper is a case study of supporting students in developing their Generative Artificial Intelligence (GAI) literacy as well as guiding them to use it ethically, appropriately, and responsibly in their studies. As part of the study, a law coursework assignment was designed utilising a four-step Problem, AI, Interaction, Reflection (PAIR) framework that included a problem-solving task that required the students to use GAI tools. The students were asked to use one or two GAI tools of their choice early in their assessment preparation to research and were given a set questionnaire to reflect on their experience. They were instructed to apply Gibbs’ or Rolfe’s reflective cycles to write about their experience in the reflective part of the assessment. This study found that a GAI-enabled assessment reinforced students’ understanding of the importance of academic integrity, enhanced their research skills, and helped them understand complex legal issues and terminologies. It also found that the students did not rely on GAI outputs but evaluated and critiqued them for their accuracy and depth referring to primary and secondary legal sources—a process that enhanced their critical thinking and problem-solving skills. Full article

Generative artificial intelligence (GAI) is emerging as a disruptive force, both economically and socially, with its use spanning from the provision of goods and services to everyday activities such as healthcare and household management. This study analyzes the enabling and inhibiting factors of GAI use in Spain based on a large-scale survey conducted by the Spanish Center for Sociological Research on the use and perception of artificial intelligence. The proposed model is based on the Theory of Planned Behavior and is fitted using machine learning techniques, specifically decision trees, Random Forest extensions, and extreme gradient boosting. While decision trees allow for detailed visualization of how variables interact to explain usage, Random Forest provides an excellent model fit (R² close to 95%) and predictive performance. The use of Shapley Additive Explanations reveals that knowledge about artificial intelligence, followed by innovation orientation, is the main explanatory variable of GAI use. Among sociodemographic variables, Generation X and Z stood out as the most relevant. It is also noteworthy that the perceived privacy risk does not show a clear inhibitory influence on usage. Factors representing the positive consequences of GAI, such as performance expectancy and social utility, exert a stronger influence than the negative impact of hindering factors such as perceived privacy or social risks. Full article

This paper presents a mathematical formalism for generative artificial intelligence (GAI). Our starting point is an observation that a “histories” approach to physical systems agrees with the compositional nature of deep neural networks. Mathematically, we define a GAI system as a family of sequential joint probabilities associated with input texts and temporal sequences of tokens (as physical event histories). From a physical perspective on modern chips, we then construct physical models realizing GAI systems as open quantum systems. Finally, as an illustration, we construct physical models realizing large language models based on a transformer architecture as open quantum systems in the Fock space over the Hilbert space of tokens. Our physical models underlie the transformer architecture for large language models. Full article

The rapid evolution of the Fourth Industrial Revolution has significantly transformed educational practices, necessitating the integration of advanced technologies into higher education to address contemporary sustainability challenges. This study explores the integration of interactive metaverse environments and generative artificial intelligence (GAI) in promoting the green digital economy and developing e-entrepreneurship skills among graduate students. Grounded in a quasi-experimental design, the research was conducted with a sample of 25 postgraduate students enrolled in the “Computers in Education” course at King Khalid University. A 3D immersive learning environment (FrameVR) was combined with GAI platforms (ChatGPT version 4.0, Elai.io version 2.5, Tome version 1.3) to create an innovative educational experience. Data were collected using validated instruments, including the Green Digital Economy Scale, the e-Entrepreneurship Scale, and a digital product evaluation rubric. The findings revealed statistically significant improvements in students’ awareness of green digital concepts, entrepreneurial competencies, and their ability to produce sustainable digital products. The study highlights the potential of immersive virtual learning environments and AI-driven content creation tools in enhancing digital literacy and sustainability-oriented innovation. It also underscores the urgent need to update educational strategies and curricula to prepare future professionals capable of navigating and shaping green digital economies. This research provides a practical and replicable model for universities seeking to embed sustainability through emerging technologies, supporting broader goals such as SDG 4 (Quality Education) and SDG 9 (Industry, Innovation, and Infrastructure). Full article

As globalization deepens, concerns over global fragmentation have intensified, accompanied by rising expectations that the Global South will emerge as a key driver of innovation, competitiveness, advanced markets, and high-quality employment. The widespread diffusion of the Internet and smartphones across developing countries suggests the possibility of leapfrog growth, highlighting the informal economy as a potential source of innovation. Recent developments in generative artificial intelligence (GAI) have further underscored the opportunity for collaborative engagement between developed and developing countries to awaken and harness sleeping innovation resources. This study investigates the dynamism of such international collaboration, focusing on digitalization-related challenges and its contributions to leapfrog growth. The interconnections among Internet usage, smartphone penetration, and economic development are examined, revealing the formation of a self-propagating cycle facilitated by GAI. A mathematical model is constructed to demonstrate the dependency of growth on sleeping resources inherent in the informal economy, which is empirically validated through data from nine African countries. Using the coevolutionary dynamics of Amazon and AWS as a conceptual reference, a novel framework is proposed for international collaborative utilization of sleeping innovation resources, offering new insights into GAI-driven innovation rooted in the informal economy. Full article

As public institutions increasingly adopt AI-driven virtual assistants to support transparency and citizen engagement, the need for explainable, accurate, and context-aware language systems becomes vital. While traditional retrieval-augmented generation (RAG) frameworks effectively integrate external knowledge into Large Language Models (LLMs), their reliance on flat, unstructured document retrieval limits multi-hop reasoning and interpretability, especially with complex, structured e-government datasets. This study introduces a modular, extensible, multi-agent graph retrieval-augmented generation (GraphRAG) framework designed to enhance policy-focused question answering. This research aims to provide an overview of hybrid multi-agent GraphRAG architecture designed for operational deployment in e-government settings to support explainable AI systems. The study focuses on how the hybrid integration of standard RAG, embedding-based retrieval, real-time web search, and LLM-generated structured Graphs can optimize knowledge discovery from public e-government data, thereby reinforcing factual grounding, reducing hallucinations, and enhancing the quality of complex responses. To validate the proposed approach, we implement and evaluate the framework using the European Commission’s Press Corner as a data source, constructing graph-based knowledge representations and embeddings, and incorporating web search. This work establishes a reproducible blueprint for deploying AI systems in e-government that require structured reasoning in comprehensive and factually accurate question answering. Full article

The integration of generative artificial intelligence (GAI) into higher education is transforming students’ learning processes, academic performance, and psychological well-being. Despite the increasing adoption of GAI tools, the mechanisms through which students’ AI literacy and self-regulated learning (SRL) relate to their academic and emotional experiences remain underexplored. This study investigates how AI literacy and SRL are associated with writing performance and digital well-being among university students in GAI-supported higher learning contexts. A survey was administered to 257 students from universities in China, and structural equation modeling was used to examine the hypothesized relationships. Results show that both AI literacy and SRL significantly and positively predict students’ writing performance, with SRL having a stronger effect. Moreover, AI literacy shows a positive association with GAI-driven well-being, with writing performance serving as a partial mediator in this relationship. These findings suggest that fostering both technological competencies and effective learning strategies may support students’ academic outcomes while supporting their psychological well-being in AI-enriched educational environments. By integrating AI literacy and SRL into a unified model, this study contributes to the growing body of research on GAI-driven well-being in higher education and offers practical implications for cultivating balanced and sustainable learning experiences in the age of GAI. Full article

This study examines how generative AI (GAI) impacts primary students’ in-depth learning, focusing on critical thinking and prior knowledge. A quasi-experiment involved 163 sixth-graders divided into three groups: a control group (lecture-based instruction) and two experimental groups using GAI as a cognitive tool (materials generation) or thinking tool (critical analysis), in which 126 participants successfully completed all the tests and were included in the analysis. ANOVA revealed the thinking-tool group and cognitive-tool group both outperformed the control group in in-depth learning, which was reflected by the knowledge transfer. Hierarchical regression showed students’ critical thinking skills and use of generative artificial intelligence significantly contributed to their in-depth learning, while prior knowledge did not. Further analysis found that significant interaction effects existed between the use of generative artificial intelligence and critical thinking skills, while no significant interaction was found between the use of generative artificial intelligence and students’ prior knowledge. In sum, critical thinking amplified GAI’s impact, while prior knowledge showed no interaction. The results suggest GAI enhances deep learning when integrated with critical thinking, reducing reliance on prior knowledge. Educators should prioritize fostering critical thinking to maximize GAI’s benefits. The findings underscore the need for pedagogical designs that balance GAI’s cognitive support with metacognitive skill development. Full article

As manufacturing transitions from Industry 4.0 to Industry 5.0, a critical challenge emerges in integrating Generative Artificial Intelligence (GAI) into adaptive social manufacturing to achieve sustainability goals. This transition reflects a paradigmatic shift from a technology-centric model focused on automation and efficiency toward a more holistic framework that embeds human-centricity and environmental responsibility into industrial systems. Whereas Industry 4.0 emphasizes digital innovation and productivity, Industry 5.0 seeks to align technological advancement with broader ecological and societal objectives. Despite advancements in automation and digitalization, existing frameworks lack a structured approach to leveraging GAI for environmental, social, and economic sustainability. This study explores the transformative role of GAI in adaptive social manufacturing, addressing the gap in the existing frameworks. Employing a multi-method research design, including content analysis, expert-driven validation, and system dynamics modeling, the study identifies nine key sustainability dimensions of Industry 5.0 and maps them to 17 GAI functions. The findings reveal that GAI significantly enhances adaptive social manufacturing by optimizing resource efficiency, promoting inclusivity, and supporting ethical governance. System dynamics analysis highlights the complex interdependencies between GAI-driven functions and sustainability outcomes, underscoring the need to balance technological innovation with human values. The research provides a novel framework for industries seeking to implement GAI in sustainable production systems, bridging theoretical insights with practical applications. Additionally, it offers actionable strategies to address challenges such as workforce adaptation, ethical AI governance, and adoption barriers, ultimately facilitating the transition toward Industry 5.0’s sustainability goals. Full article

Engineers’ work impacts society and the environment and plays a central role in delivering on the United Nations Sustainable Development Goals. However, developing sustainability skills in engineering programs competes with a dense technical curriculum and has proven challenging. The mainstream adoption of generative AI (GAI) tools has prompted a review of teaching and learning, with expanding possibilities as new use cases emerge. This study reviews the impact that GAI is having on engineering education and proposes a framework for the use of GAI to facilitate greater socio-enviro-technical integration in the engineering curriculum. Based on a scoping review of the literature and a conceptual analysis, this paper provides a forward-looking perspective. Artificial intelligence (AI) is also transforming the practice of engineering, triggering the need to adjust graduate attributes accordingly. The increased productivity expected with the rise of AI in the workplace can scale-up the impact of engineering developments and underscores the need for graduates’ sustainability skills. Furthermore, engineers have a prominent role in the development of AI systems. Therefore, in advocating for the need to enhance graduate’s sustainability skills, we emphasize understanding its limitations and the sustainability of AI systems to address the paradox of AI for sustainability and the sustainability of AI itself. Full article

The advent of generative artificial intelligence (GAI) technologies has significantly influenced the educational landscape. However, public perceptions and the underlying emotions toward artificial intelligence-generated content (AIGC) applications in education remain complex issues. To address this issue, this study employs LDA network public opinion topic mining and SnowNLP sentiment analysis to comprehensively analyze over 40,000 comments collected from multiple social media platforms in China. Through a detailed analysis of the data, this study examines the distribution of positive and negative emotions and identifies six topics. The study further utilizes visual tools such as word clouds and heatmaps to present the research findings. The results indicate that the emotional polarity across all topics is characterized by a predominance of positive emotions over negative ones. Moreover, an analysis of the keywords across the six topics reveals that each has its own emphasis, yet there are overlaps between them. Therefore, this study, through quantitative methods, also reflects the complex interconnections among the elements within the educational ecosystem. Additionally, this study integrates the six identified topics with the Technology–Organization–Environment (TOE) framework to explore the broad impact of AIGC on education from the perspectives of technology, organization, and environment. This research provides a novel perspective on the emotional attitudes and key concerns of the Chinese public regarding the use of AIGC in education. Full article

Background: Anatomically accurate illustrations are imperative in medical education, serving as crucial tools to facilitate comprehension of complex anatomical structures. While traditional illustration methods involving human artists remain the gold standard, the rapid advancement of Generative Artificial Intelligence (GAI) models presents a new opportunity to automate and accelerate this process. This study evaluated the potential of GAI models to produce craniofacial anatomy illustrations for educational purposes. Methods: Four GAI models, including Midjourney v6.0, DALL-E 3, Gemini Ultra 1.0, and Stable Diffusion 2.0 were used to generate 736 images across multiple views of surface anatomy, bones, muscles, blood vessels, and nerves of the cranium in both oil painting and realistic photograph styles. Four reviewers evaluated the images for anatomical detail, aesthetic quality, usability, and cost-effectiveness. Inter-rater reliability analysis assessed evaluation consistency. Results: Midjourney v6.0 scored highest for aesthetic quality and cost-effectiveness, and DALL-E 3 performed best for anatomical detail and usability. The inter-rater reliability analysis demonstrated a high level of agreement among reviewers (ICC = 0.858, 95% CI). However, all models showed significant flaws in depicting crucial anatomical details such as foramina, suture lines, muscular origins/insertions, and neurovascular structures. These limitations were further characterized by abstract depictions, mixing of layers, shadowing, abnormal muscle arrangements, and labeling errors. Conclusions: These findings highlight GAI’s potential for rapidly creating craniofacial anatomy illustrations but also its current limitations due to inadequate training data and incomplete understanding of complex anatomy. Refining these models through precise training data and expert feedback is vital. Ethical considerations, such as potential biases, copyright challenges, and the risks of propagating inaccurate information, must also be carefully navigated. Further refinement of GAI models and ethical safeguards are essential for safe use. Full article

This study examines the security implications of generative artificial intelligence (GAI), focusing on models such as ChatGPT. As GAI technologies are increasingly integrated into industries like healthcare, education, and media, concerns are growing regarding security vulnerabilities, ethical challenges, and potential for misuse. This study not only synthesizes existing research but also conducts an original scientometric analysis using text mining techniques. To address these concerns, this research analyzes 1047 peer-reviewed academic articles from the SCOPUS database using scientometric methods, including Term Frequency–Inverse Document Frequency (TF-IDF) analysis, keyword centrality analysis, and Latent Dirichlet Allocation (LDA) topic modeling. The results highlight significant contributions from countries such as the United States, China, and India, with leading institutions like the Chinese Academy of Sciences and the National University of Singapore driving research on GAI security. In the keyword centrality analysis, “ChatGPT” emerged as a highly central term, reflecting its prominence in the research discourse. However, despite its frequent mention, “ChatGPT” showed lower proximity centrality than terms like “model” and “AI”. This suggests that while ChatGPT is broadly associated with other key themes, it has a less direct connection to specific research subfields. Topic modeling identified six major themes, including AI and security in education, language models, data processing, and risk management. The analysis emphasizes the need for robust security frameworks to address technical vulnerabilities, ensure ethical responsibility, and manage risks in the safe deployment of AI systems. These frameworks must incorporate not only technical solutions but also ethical accountability, regulatory compliance, and continuous risk management. This study underscores the importance of interdisciplinary research that integrates technical, legal, and ethical perspectives to ensure the responsible and secure deployment of GAI technologies. Full article

This study introduces a multimodal framework integrating retrieval-augmented generation (RAG) with multimodal large language models (MLLMs) to enhance the accessibility, interpretability, and analysis of Eurobarometer survey data. Traditional approaches often struggle with the diverse formats and large-scale nature of these datasets, which include textual and visual elements. The proposed framework leverages multimodal indexing and targeted retrieval to enable focused queries, trend analysis, and visualization, across multiple survey editions. The integration of LLMs facilitates advanced synthesis of insights, providing a more comprehensive understanding of public opinion trends. The proposed framework offers prospective benefits for different types of stakeholders, including policymakers, journalists, nongovernmental organizations (NGOs), researchers, and citizens, while highlighting the need for performance assessment to evaluate its effectiveness based on specific business requirements and practical applications. The framework’s modular design supports applications, such as survey studies, comparative analyses, and domain-specific investigations, while its scalability and reproducibility make it suitable for e-governance and public sector deployment. The results indicate potential enhancements in data interpretation and data analysis by providing stakeholders with the capability not only to utilize raw text data for knowledge extraction but also to conduct image analysis based on indexed content, paving the way for informed policymaking and advanced research in the social sciences, while emphasizing the need for performance assessment to validate the framework’s output and functionality, based on the selected architectural components. Future research will explore expanded functionalities and real-time applications, ensuring the framework remains adaptable to evolving needs in public opinion analysis and multimodal data integration. Full article