Search Results (1,509)

In this paper, we present a conceptual design framework for developing immersive learning experiences at scale with generative AI and extended reality (XR) for primary and secondary education. Based on the synthesis of current literature, our framework asserts a practical five-step pipeline: curriculum ingestion, AI-powered blueprinting, asset assembly, educator review, and classroom deployment with formative assessment. The model is designed to be flexible, focusing on narrative and gamification for primary students, moving on to sophisticated simulations and analytical activities for secondary students. We place this framework into the context of recent developments in generative 3D models, bridging fundamental technical and ethical gaps between concept and classroom practice. Finally, we summarize a prioritized research agenda around evaluation, access, and teacher workflows to enable near-term pilot studies. This work is intended to inform educators, researchers, and stakeholders who are interested in implementing effective AI-XR solutions in schools in a pedagogically sound way. Full article

The increasing complexity of networked research demands approaches that combine rigor, efficiency, and collaboration. In this context, artificial intelligence (AI) emerges as a strategic ally in the analysis and organization of scientific literature, facilitating the construction of a robust state-of-the-art framework to support decisions. The present study focuses on evaluating a model for the use of AI that facilitates collaborative literature review by integrating AI tools. The present study employed a descriptive, non-experimental, cross-sectional design. Participants (N = 10) completed a purpose-built questionnaire comprising twenty-five indicators on specific aspects of the model’s use. The participants indicated a high level of knowledge regarding ICT use (M = 8.3; SD = 1.25). The results showed that the System Usability Scale for the tools demonstrated variability; Google Drive scored the highest (M = 77.75; SD = 11.45), while Rayyan.AI scored the lowest (M = 66.00; SD = 20.69). While the findings indicated that AI enhances the efficiency of documentary research and the development of ethical and digital competencies, the participants expressed a need for further training in AI tools to optimize the usability of those integrated into the model. The proposed model CAIM-REL proves to be replicable and holds potential for collaborative research. Full article

Organoids are self-organizing three-dimensional (3D) cellular structures derived from stem cells. They can mimic the anatomical and functional properties of real organs. They have transformed in vitro disease modeling by closely replicating the structural and functional characteristics of human tissues. The complexity and variability of organoid-derived data pose significant challenges for analysis and clinical translation. Artificial Intelligence (AI) has emerged as a crucial enabler, offering scalable and high-throughput tools for interpreting imaging data, integrating multi-omics profiles, and guiding experimental workflows. This review aims to discuss how AI is reshaping organoid-based research by enhancing morphological image analysis, enabling dynamic modeling of organoid development, and facilitating the integration of genomics, transcriptomics, and proteomics for disease classification. Moreover, AI is increasingly used to support drug screening and personalize therapeutic strategies by analyzing patient-derived organoids. The integration of AI with organoid-on-chip systems further allows for real-time feedback and physiologically relevant modeling. Drawing on peer-reviewed literature from the past decade, Furthermore, CNNs have been used to analyze colonoscopy and histopathological images in colorectal cancer with over 95% diagnostic accuracy. We examine key tools, innovations, and case studies that illustrate this evolving interface. As this interdisciplinary field matures, the future of AI-integrated organoid platforms depends on establishing open data standards, advancing algorithms, and addressing ethical and regulatory considerations to unlock their clinical and translational potential. Full article

This paper examines the effects of AI-based digital nudges on consumers’ sustainable purchase intentions and behaviors, using an integrated framework that combines the Technology Acceptance Model (TAM) and the Nudge Theory. Previous studies have demonstrated that digital nudges can stimulate eco-friendly behavior. However, the interaction between personalization, timing, message framing, cognitive variables like perceived usefulness, and psychological variables such as environmental concern has not been explained. The study employs quantitative research based on SEM-PLS, which explores the relationships between these constructs with a valid response of 810 samples. Personalization, timing of nudges, and framing enhance perceived utility and sustainable purchase intention. Perceived usefulness mediated the relationship between digital nudging and sustainable purchase intention, moderated by environmental concern as a psychological catalyst. These results support the validation of the combined TAM Nudge model, illustrating the role of technology and behavior in fostering sustainability. The implication of the study can support policymakers, marketers, and digital designers in creating ethical AI-based interventions to meet SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action), transforming sustainability awareness into a quantifiable behavioral change. Full article

Big data and artificial intelligence technologies are driving a paradigm shift in smart farming, yet intelligent decision-making faces critical bottlenecks. At the data level, challenges include fragmentation, high acquisition costs, and inadequate secure sharing; at the model level, issues involve regional heterogeneity, weak adaptability, and insufficient explainability. To address these, this paper systematically reviews global research to establish a theoretical framework spanning the entire production cycle. Regarding data governance, trends favor federated systems with unified metadata and layered storage, utilizing technologies like federated learning for secure lifecycle management. For decision-making, approaches are evolving from experience-based to data-driven intelligence. Pre-harvest planning now integrates mechanistic models and transfer learning for suitability and variety optimization. In-season management leverages deep reinforcement learning (DRL) and model predictive control (MPC) for precise regulation of seedlings, water, fertilizer, and pests. Post-harvest evaluation strategies utilize spatio-temporal deep learning architectures (e.g., Transformers or LSTMs) and intelligent optimization algorithms for yield prediction and machinery scheduling. Finally, a staged development pathway is proposed: prioritizing standardized data governance and foundation models in the short term; advancing federated learning and human–machine collaboration in the mid-term; and achieving real-time, ethical edge AI in the long term. This framework supports the transition toward precise, transparent, and sustainable smart agriculture. Full article

Jailbreak attacks (JAs) represent a sophisticated subclass of adversarial threats wherein malicious actors craft strategically engineered prompts that subvert the intended operational boundaries of large language models (LLMs). These attacks exploit latent vulnerabilities in generative AI architectures, allowing adversaries to circumvent established safety protocols and illicitly induce the model to output prohibited, unethical, or harmful content. The emergence of such exploits underscores critical gaps in the security and controllability of modern AI systems, raising profound concerns about their societal impact and deployment in sensitive environments. In response, this study introduces an innovative defense framework that synergistically integrates language model perplexity analysis with a Multi-Agent System (MAS)-oriented detection architecture. This hybrid design aims to fortify the resilience of LLMs by proactively identifying and neutralizing jailbreak attempts, thereby ensuring the protection of user privacy and ethical integrity. The experimental setup adopts a query-driven adversarial probing strategy, in which jailbreak prompts are dynamically generated and injected into the open-source LLaMA-2 model to systematically explore potential vulnerabilities. To ensure rigorous validation, the proposed framework will be evaluated using a custom jailbreak detection benchmark encompassing metrics such as Attack Success Rate (ASR), Defense Success Rate (DSR), Defense Pass Rate (DPR), False Positive Rate, Benign Pass Rate (BPR), and End-to-End Latency. Through iterative experimentation and continuous refinement, this work endeavors to advance the defensive capabilities of LLM-based systems, enabling more trustworthy, secure, and ethically aligned deployment of generative AI in real-world environments. Full article

Generative AI technologies are rapidly permeating higher education as innovative tools that support teaching and learning processes. This study investigates the integration of GenAI tools into academic learning and examines their influence on students’ learning effectiveness, attitudes, and satisfaction. A quantitative survey was administered to 485 college students. The findings indicate that students’ attitudes, satisfaction, and accumulated experience with GenAI constitute the most influential factors in promoting effective learning. Perceived advantages and disadvantages also play a substantial role in shaping students’ attitudes, satisfaction, and learning outcomes. Ethical knowledge demonstrates only modest positive effects, whereas institutional training shows no meaningful impact, largely due to its limited availability. The results suggest that higher education institutions should not focus solely on tool accessibility and technical training, but should prioritize fostering positive perceptions, maximizing the perceived benefits of GenAI, offering applied instruction and practical ethical guidance, and reducing concerns and negative perceptions among students. Full article

This study investigates how core digital technologies—artificial intelligence (AI) and blockchain—can foster green innovation and sustainable consumption through circular platform design in high-value resale markets. Using Design Science Research (DSR) methodology, including its iterative cycles, we developed and evaluated TRUCE (Trust, Resale Logic, User Centricity, Circular Infrastructure, Ecosystem Governance), a sustainability-oriented digital architecture designed to promote ethical, energy-efficient consumption. TRUCE aims to leverage AI-driven authentication, blockchain-based transparency, and consumer data analytics, aiming to embed circularity and traceability into platform governance. Aligned with the EU Green Deal’s digital agenda, it is intended to support waste reduction, lifecycle extension, and responsible consumption, contributing to Sustainable Development Goal 12 and the broader 2030 Agenda. Full article

This narrative review explores how specialised clinical competencies and artificial intelligence (AI) technologies converge in the context of perioperative care, with a focus on their combined potential to improve patient safety. Considering the growing complexity of surgical care and rising demands on healthcare professionals, the study aims to understand how human expertise and digital tools can complement each other in this high-stakes environment. Methods: A narrative review methodology was adopted to integrate insights from diverse sources, including empirical studies, policy documents, and expert analyses published over the last decade. Findings reveal that AI can support clinical decision-making, streamline workflows, and enable earlier identification of complications across all perioperative phases. These technologies enhance, rather than replace, the roles of nurses, anesthetists, and surgeons. However, their effective use depends on critical factors such as digital literacy, interdisciplinary collaboration, and ethical awareness. Issues related to data privacy, algorithmic bias, and unequal access to technology highlight the need for thoughtful, inclusive implementation. The future of perioperative care will likely depend on hybrid models where human judgment and AI-based tools are integrated in ways that uphold safety, equity, and person-centred values. Full article

Artificial Intelligence, now commonly called AI, is having an increasingly big impact on society. There are fears that may be negatives or downsides, especially when Artificial Intelligence is used unethically. But how are humans guiding these machines to know whether the choice, the decision, is ethical? Since 2007, one way to check the ethicality of any choice has been to apply the JUSTICE model. This framework helps practitioners decide whether a specific action is or is not ethical by looking through one or more of the seven JUSTICE lenses: Justice, Utilitarian, Spiritual Values, TV rule or Transparency, Influence, Core, and Emergency. Now, in this era of increasing prevalence of Artificial Intelligence, with humans making decisions often together with machines, can the JUSTICE framework still be useful? Yes, it can. We look at each of those seven components. Each may give guidance in some situations. Of the seven, it seems that T or the TV test is most likely to give guidance in this new era. Full article

Background: This narrative review synthesizes evidence on AI for orthodontic malocclusion diagnosis across five imaging modalities and maps diagnostic metrics to validation tiers and regulatory readiness, with focused appraisal of Class III detection (2019–2025). Key algorithms, datasets, clinical validation, and ethical/regulatory considerations are synthesized. Methods: PubMed, Scopus, and Web of Science were searched for studies published January 2019–October 2025 using (“artificial intelligence”) AND (“malocclusion” OR “skeletal class”) AND “cephalometric.” Records were screened independently by two reviewers, with disagreements resolved by consensus. Eligible studies reported diagnostic performance (accuracy, area under the receiver operating characteristic curve (AUC), sensitivity/specificity) or landmark-localization error for AI-based malocclusion diagnosis. Data on dataset size and validation design were extracted; no formal quality appraisal or risk-of-bias assessments were undertaken, consistent with a narrative review. Results: Deep learning models show high diagnostic accuracy: cephalogram classifiers reach 90–96% for skeletal Class I/II/III; intraoral photograph models achieve 89–93% for Angle molar relationships; automated landmarkers localize ~75% of points within 2 mm. On 9870 multicenter cephalograms, landmarking achieved 0.94 ± 0.74 mm with ≈89% skeletal-class accuracy when landmarks fed a classifier. Conclusion: AI can reduce cephalometric tracing time by ~70–80% and provide consistent skeletal classification. Regulator-aligned benchmarks (multicenter external tests, subgroup reporting, explainability) and pragmatic open-data priorities are outlined, positioning AI as a dependable co-pilot once these gaps are closed. Full article

As global infrastructure systems face increasing environmental, social, and operational challenges, enhancing their resilience through digital and intelligent technologies has become a strategic priority. Digital Twin (DT) and Artificial Intelligence (AI) technologies offer transformative capabilities for monitoring, predicting, and optimizing infrastructure performance under stress. However, research on their integration within resilience frameworks remains fragmented. This study presents a comprehensive bibliometric analysis to clarify how DT and AI are being applied to strengthen infrastructure resilience (IR). Using data exclusively from the Web of Science (WoS) database, co-occurrence and overlay visualizations were employed to map thematic structures, identify research clusters, and track emerging trends. The analysis revealed six interconnected research domains linking DT, AI, and resilience, including artificial intelligence and industrial applications, digital twins and machine learning, cyber–physical systems, smart cities and sustainability, data-driven resilience modeling, and methodological frameworks. Overlay mapping revealed a temporal shift from early work on sensors and cyber–physical systems toward integrated, sustainability-oriented applications, including predictive maintenance, urban digital twins, and environmental resilience. The findings underscore the need for adaptive and interoperable DT ecosystems incorporating AI-driven analytics, ethical data governance, and sustainability metrics, providing a unified foundation for advancing resilient and intelligent infrastructure systems. Full article

Background/Objectives: As the primary cause of cancer-related death globally, lung cancer highlights the critical need for early identification, precise staging, and individualized treatment planning. By enabling automated diagnosis, staging, and prognostic evaluation, recent developments in artificial intelligence (AI) and machine learning (ML) have completely changed the treatment of lung cancer. The goal of this narrative review is to compile the most recent data on uses of AI and ML throughout the lung cancer care continuum. Methods: A comprehensive literature search was conducted across major scientific databases to identify peer-reviewed studies focused on AI-based imaging, detection, and prognostic modeling in lung cancer. Studies were categorized into three thematic domains: (1) detection and screening, (2) staging and diagnosis, and (3) risk prediction and prognosis. Results: Convolutional neural networks (CNNs), in particular, have shown significant sensitivity and specificity in nodule recognition, segmentation, and false-positive reduction. Radiomics-based models and other multimodal frameworks combining imaging and clinical data have great promise for forecasting treatment outcomes and survival rates. The accuracy of non-small-cell lung cancer (NSCLC) staging, lymph node evaluation, and malignancy classification were regularly improved by AI algorithms, frequently matching or exceeding radiologist performance. Conclusions: There are still issues with data heterogeneity, interpretability, repeatability, and clinical acceptability despite significant advancements. Standardized datasets, ethical AI implementation, and transparent model evaluation should be the top priorities for future initiatives. AI and ML have revolutionary potential for intelligent, personalized, and real-time lung cancer treatment by connecting computational innovation with precision oncology. Full article

Artificial Intelligence (AI) is a transformational force reshaping business processes, financial decision-making, and enabling firms to create, deliver and capture value more effectively. While large corporations in South Asian countries, particularly Bangladesh, India, Pakistan and Sri Lanka have started leveraging AI to drive Business Model Innovation (BMI), Small and Medium Enterprises (SMEs) continue to face significant challenges. These include limited infrastructure, poor bandwidth penetration, unreliable electricity, weak institutional capacity and governance immaturity, along with ethics and compliance concerns. These challenges hinder SMEs from fully exploiting AI-driven BMI and reduce their financial resilience and competitiveness in increasingly digital and globalised markets. This paper examines how South Asian countries are adopting AI technologies in SMEs by comparing patterns and variations in adoption, capability, ethics, risks, compliance, and financial outcomes. The paper proposes a tailored, actionable framework, called TRIAD (Target, Restructure, Integrate, Accelerate, and Democratise)-AI, designed to address technical, organisational and institutional challenges that shape AI-driven BMI across South Asian SMEs and to meet regional and global SME needs. The framework integrates the best practices from global AI leaders such as China, Estonia and Singapore, emphasising responsible AI adoption through robust ethics and compliance standards, and risk management, and offering practical guidance for South Asian SMEs. By adopting this framework, South Asian countries can gain a competitive advantage, enhance operational efficiency, support GDP growth across the region and ensure adherence to all relevant international AI standards for responsible, sustainable, and financially sound innovation. Full article