Search Results (398)

With the rapid advancement of artificial intelligence (AI) technology, various scientific research fields are exploring the application of potential of new technologies. Counseling psychology, closely intertwined with natural language processing, is one of the most suitable domains for such exploration. This study focuses on investigating the effectiveness of large language models (LLMs) in assisting novice counselors with case-formulation processes. Based on supervisors’ ratings of novice counselors’ performance before and after using LLMs, the results indicate that AI assistance significantly enhances the comprehensiveness and coherence of case formulation, while its effects on complexity and specificity are not statistically significant. Additionally, through novice counselors’ subjective evaluations of the AI-assisted process, the value of AI tools in broadening perspectives and facilitating convenient operations is identified. Overall, the findings suggest that AI can moderately assist novice counselors in understanding clients. However, to better integrate AI with professional practice and improve case formulation competence, it is essential for novice counselors to maintain critical thinking and strengthen their learning of counseling theories and techniques during AI application. Full article

In the field of coal mine safety, traditional large language models (LLMs) face issues such as poor knowledge timeliness, lack of traceable evidence, and susceptibility to hallucinations when handling complex knowledge-intensive tasks. To align with the human-centric principles of Industry 5.0 and meet the strict compliance requirements of Industrial Regulatory Technologies (RegTechs), this study aims to enhance the performance of a Retrieval-Augmented Generation (RAG)-based coal mine safety compliance system. A comprehensive coal mine knowledge dataset (CMK) is constructed to serve as a dynamic RegTech knowledge base. Based on the RAG framework, a two-stage retrieval structure is designed and optimized, transforming the generative AI into a safe, constrained reasoning core. Also, a hard-negative ranking dataset (CMK-R) is developed to support the evaluation of the reranking model and mitigate the risk of semantic hallucinations. The proposed approach adopts Dmeta-embedding-base and BGE-rerank-base as backbone models and applies fine-tuning, teacher–student structured distillation, and Matryoshka Representation Learning (MRL) to improve efficiency and reduce energy consumption. Experimental results demonstrate that the proposed CMS-base model achieves FAHR, MRR@10, and mAP scores of 93.48, 95.59, and 95.63, respectively, indicating that the system can retrieve the correct safety regulation or operational clause at the top ranks with high reliability. Furthermore, the lightweight variant reduces inference time by over 60%, and accuracy degradation remains below 1% even when the vector dimension is compressed to 64, significantly facilitating Edge Intelligence deployments. This robust, human-centric framework provides a scalable industrial AI solution for real-time safety compliance and decision support. Full article

This study analyses the evolution of the financial literature on climate risk, examining the integration of artificial intelligence techniques into its measurement and management. To this end, a bibliometric approach is employed based on 221 articles indexed in the Web of Science Core Collection, using the Bibliometrix package. Moving beyond existing descriptive bibliometric reviews on ESG and green finance, the novelty of this paper lies in its analytical focus on how financial science operationalises quantitative AI mechanisms to price and integrate climate transition risk into asset and portfolio valuation. The structural analysis reveals that natural language processing (NLP) and digital transformation acting as driving motor themes, suggesting that the reviewed literature associates AI innovation policies with the mitigation of corporate greenwashing and enhance information transparency. Furthermore, while machine learning algorithms establish the cross-cutting predictive foundation for risk assessment, empirical evidence unveils a critical academic shift of traditional ‘financial performance’ towards a declining quadrant, indicating that empirical studies frequently find that that multi-phase investments in risk technologies do not yield immediate financial returns. Finally, the study maps a persistent geographical gap where emerging markets lack the data infrastructure of advanced economies, alongside isolated high-dimensional causal econometric niches like double machine learning. This analytical mapping provides key implications for global risk management and future quantitative research avenues. Full article

This paper presents a systematic literature review examining how blockchain technologies can enhance the security and performance of multi-cloud systems. Multi-cloud architectures offer resilience, scalability, and flexibility; however, they also pose complex security challenges related to APIs, service-level agreements (SLAs), orchestration, and authentication. The promise of blockchain technology to improve the security and transparency of numerous applications, including cloud storage systems, has attracted considerable attention in recent years. Much research has focused on decentralized storage in cloud environments, spanning supply chains, FinTech, healthcare, and education. Still, the integration of blockchain with the cloud and its potential to enhance security and performance warrant an in-depth study. Using the PRISMA methodology, a structured search was conducted across six major scientific databases, including IEEE, ACM Digital Library, ScienceDirect, Scopus, Web of Science, and IJIMAI. Twenty-four primary papers published between 2019 and 2025 were selected for analysis after clear inclusion and exclusion criteria were applied. This review examines the security dimensions in multi-cloud environments—architectural vulnerabilities, API security, authentication, orchestration and automation vulnerabilities, SLAs, and cybersecurity compliance issues—in relation to blockchain technology. Based on the identified gaps, we propose a SOC-LLM-augmented security architecture that integrates blockchain-based evidence integrity, statistical anomaly detection, machine learning, large language models, and autonomous AI agents to enable intelligent attack detection and response. The proposed framework introduces specialized agents for detection, correlation, threat intelligence retrieval, blockchain evidence validation, explanation generation, and response planning. The analysis shows that integrating SOC-LLM capabilities with blockchain can move multi-cloud security from passive auditability toward proactive, explainable, and human-in-the-loop cyber defense. Finally, this paper discusses open challenges, including LLM hallucination, data scarcity, real-time scalability, evaluation standardization, and trustworthy deployment in critical multi-cloud infrastructures. The study’s conclusion highlights research gaps and suggests future lines of inquiry concerning scalable blockchain architectures and the incorporation of AI for proactive cloud security monitoring. Full article

Artificial intelligence (AI)-based support systems are transforming the educational landscape by enhancing teaching efficiency, personalized learning, and accessibility. Despite rapid technological progress, educational institutions face persistent challenges such as unequal access to quality learning resources, limited teacher support, and the need for individualized student engagement. These issues hinder effective learning outcomes and inclusivity in modern classrooms. This study presents a comprehensive literature review and a methodology grounded in constructivist learning theory to develop an AI-based educational support framework. The study is situated within the context of a higher education course integrating AI-supported learning. The proposed framework is developed by synthesizing theoretical and empirical evidence and is subsequently evaluated by experts in educational technology and artificial intelligence. Data are collected through structured expert questionnaires and qualitative feedback. Quantitative data are analyzed using descriptive statistics, while qualitative responses are examined through thematic analysis to inform framework refinement. The study adheres to established ethical principles, including informed consent, voluntary participation, confidentiality, anonymity, and secure data management. Moreover, the paper explores the design and implementation of sustainable and AI-based educational support systems that address these challenges through intelligent tutoring, adaptive learning analytics, and automated feedback mechanisms. By integrating natural language processing, machine learning, and predictive modelling, the proposed framework provides real-time assistance to educators and learners, fostering data-driven decision-making and inclusive pedagogy. Qualitative expert evaluation suggests that an AI-based educational support framework has the potential to improve teaching support, learner engagement, and personalized learning while providing a scalable and equitable approach for higher education. Full article

Artificial intelligence (AI) is deeply integrated into daily life. Emerging evidence suggests AI may help change the dynamics of social relationships by influencing social interactions, connectivity, and interpersonal relationships, and by providing new avenues for communication and contributing to improved social well-being. Therefore, this review aims to explore the potential of artificial intelligence (AI) technologies as a tool to enhance social health, focusing on current applications, advantages, challenges, and ethical considerations associated with their implementation, as well as opportunities for future development. The literature on the relationship between the connectedness of social health dimensions and AI as a tool to better understand how interactions with AI technologies may influence social well-being. In this current review, key terms such as “Artificial Intelligence”, “Social Health”, “social inequalities”, “AI algorithm”, “AI technology”, “social connection”, “digital communication”, “social participation”, “social support”, “social isolation”, “loneliness”, “mental wellbeing”, were used to search relevant literature on Google Scholar, PubMed, Scopus and Web of Sciences. In addition, relevant aspects of the multidimensional impacts of AI on social health dimensions are also discussed. The use of AI technologies by individuals within societies was found to hold profound potential to reshape social health through enhancing social relationships, bridging communication gaps in diverse populations, stimulating social dynamics, and understanding human emotions. It may contribute to reducing social inequalities, promoting equity, accommodating individual differences, and enhancing the effectiveness of many tasks in the social and health care systems through deep learning, natural language processing, and machine learning techniques. This reduces social exclusion and increases accessibility and quality of health and social services. However, AI has also posed distinguishable challenges to its adoption, specifically in terms of data quality, privacy and security, algorithmic bias, ethical issues, public trust and acceptance, and regulatory and policy gaps. Evidence suggests that building public trust in the future of AI in social health requires interdisciplinary collaboration among health providers and professionals, social scientists, community members, and policymakers. Such collaboration is crucial to ensure that AI platforms do not perpetuate social inequalities or biases by maintaining transparency, explainability, and demonstrated effectiveness. In conclusion, the integration of AI into social health dimensions holds promise for social health transformation. As we move forward, several key areas need to be addressed to develop a robust governance and regulatory framework, along with ethical guidelines to ensure privacy protection, respect for human rights, transparency, and the promotion of the common good. Full article

Artificial intelligence (AI) is increasingly recognized as a transformative technology in healthcare, with growing evidence supporting its applicability across time-critical clinical environments. This perspective aims to evaluate the integration of AI and machine learning (ML) into pediatric emergency departments (PEDs) across three core domains: clinical decision support, stakeholder engagement, and medical education. Within clinical decision support, ML architectures have demonstrated high predictive performance across several high-acuity clinical scenarios, including triage stratification, pediatric traumatic brain injury risk classification, early sepsis detection and clinical deterioration prediction, and dermatological assessment. Model interpretability and real-world implementability remain critical prerequisites for clinical adoption, with explainability methods representing fundamental instruments to enhance transparency and stakeholder trust. Regarding stakeholder engagement, the triadic dynamic among clinicians, caregivers, and patients defines a unique communication challenge in PEDs, with large language models (LLMs) showing preliminary utility; however, stakeholder-inclusive model validation and robust data privacy protections for minors remain key challenges, particularly regarding legal ambiguities of LLM deployment in clinical pipelines. In medical education, AI-driven simulation platforms and LLM-generated adaptive curricula represent promising tools for competency-based training across pediatric emergency scenarios. Future directions emphasize the imperative of prospective multicenter validation in pediatric-specific cohorts, rigorous data quality standards addressing conformance, completeness, and plausibility, and the development of pediatric-tailored governance frameworks. Real-world implementation will require the systematic involvement of all stakeholders—including children, caregivers, clinicians, developers, and institutions—as co-designers of equitable, transparent, and safe AI systems for this uniquely vulnerable population. Full article

Recent advancements in Large Language Models (LLMs) hold significant promise for reshaping programming education. However, critical instructional challenges such as high failure and dropout rates, insufficient feedback, and inadequate support for students’ independent analytical thinking remain prevalent. Addressing these gaps, this study introduces the Guided Programming and Analysis System (GPAS), an innovative educational approach leveraging LLM-based technology to enhance programming instruction. GPAS is designed to support autonomous learning processes by integrating multi-turn interactive thought guidance, code polishing, semantic annotation generation, and structured scoring mechanisms across multiple programming languages. Experimental results demonstrate significant effects were observed in correlation analysis with expert evaluations ($r=0.668$, $p=6.84\times {10}^{-12}$) and paired-sample tests on code and report-level improvements (effect sizes Cohen’s $d=0.92$ and $1.56$, respectively, $p=3.56\times {10}^{-6}$ and $p=1.64\times {10}^{-10}$). Additionally, findings revealed that the GPAS platform significantly improved code quality, particularly benefiting lower-achieving students, and effectively captured nuanced improvements in readability, structure, and boundary handling. Moreover, GPAS emphasizes the supportive role of educators, enabling them to focus more effectively on higher-order teaching tasks while the platform handles routine instructional feedback. Collectively, these results suggest that GPAS provides a promising framework for supporting learner-centered programming education and independent problem-solving processes. Full article

As the core automated execution components of blockchain technology, smart contracts enable programmatic control over digital assets; however, their immutable characteristics and inherent logical vulnerabilities give rise to substantial security risks. Although smart contract vulnerability detection methods based on large language models (LLMs) have exhibited certain potential in vulnerability detection and explanation, the coarse-grained modeling of traditional binary preference optimization paradigms hinders the model ability to learn the priority of domain-specific requirements, frequently leading to extreme optimization at the cost of detection accuracy. Furthermore, existing approaches fail to consider non-ideal factors in real-world application scenarios and overlook noise interference induced by missing prompts, which results in inadequate detection stability and reliability, making them challenging to adapt to complex practical scenarios. To address these critical issues, this study proposes a Noise-Aware Preference Reinforcement Large Language Model for Smart Contract Vulnerability Detection (NAPO-SCVD). This method adopts a four-stage framework consisting of data construction, continuous pre-training, supervised fine-tuning, and noise-aware preference optimization. Specifically, it enhances the model’s comprehension of contract syntax and semantics through domain-specific pre-training, improves its detection and explanation capabilities using high-quality datasets, constructs deliberately guided biased explanations to simulate noisy samples, refines preference gradients, and strengthens the model’s anti-interference ability. Consequently, this approach achieves high-precision and high-reliability smart contract vulnerability detection, along with fine-grained explanations. Full article

Significant progress in legal natural language processing (NLP) has enabled advancements in tasks such as legal judgment prediction, case retrieval, and question answering. However, the development of analogous technologies for Arabic legal texts remains severely constrained by the scarcity of large-scale, publicly available benchmarks for summarisation and classification. This paper addresses this gap by introducing a novel, comprehensive dataset of 9699 Arabic legal cases sourced from the Saudi Board of Grievances. This corpus is unique in pairing full-length court decisions with expertly human-crafted abstractive summaries and multi-class category labels (Administrative, Commercial, and Criminal), establishing a dedicated benchmark for Arabic legal NLP. The dataset was constructed via a robust, reproducible pipeline that ensures high textual fidelity, incorporating specialised optical character recognition (OCR) via Google Document AI and precise structural segmentation into facts, reasons, and summaries. To establish robust baselines, we conduct an extensive empirical evaluation of seven summarisation models—encompassing four extractive algorithms (TextRank, LexRank, Latent Semantic Analysis, and Luhn) and three transformer-based abstractive architectures (AraT5v2, AraBART, and mBART)—each evaluated in both base and fine-tuned configurations. Results across ROUGE, BERTScore, BLEU metrics and human evaluation demonstrate substantial performance gains achieved through domain-specific fine-tuning, with the fine-tuned AraBART model achieving the strongest performance among all evaluated models. Furthermore, we present a novel analysis of the downstream utility of generated summaries by evaluating their performance on legal category classification using five machine learning models. This investigation reveals a strong positive correlation between summarisation quality and classification accuracy, empirically demonstrating that domain-adapted abstractive summarisation not only enhances intrinsic evaluation scores but also significantly boosts extrinsic task performance. By providing this essential dataset and comprehensive benchmarking, our work contributes a much-needed resource to the field, facilitating future research and innovations in Arabic legal text analysis. Full article

This study explores the multifaceted attitudes of high school students toward the use of artificial intelligence (AI) and large language models (LLMs) like ChatGPT in educational contexts. Drawing upon a tripartite model of attitudes, our research evaluates affective, cognitive, and behavioral dimensions to offer a nuanced understanding of students’ perceptions. The affective dimension assesses emotional responses to AI tools, the cognitive dimension examines beliefs about the utility and ethical considerations of AI, and the behavioral dimension evaluates actual usage patterns of AI technologies. Utilizing a newly developed survey instrument tailored for the educational context, data was collected from 93 high school students across different regions of Italy in the period that ranged from February 2024–March 2024. Exploratory factor analysis (EFA) was employed to explore the underlying structure of the survey instrument and identify underlying factors influencing AI acceptance. The analysis reveals three distinct factors—Mindful AI Learning, Embracing AI Effects, and LLM as Learning Companion, highlighting the complexity of students’ attitudes toward AI. Results indicate a cautious but optimistic reception of AI in education, offering crucial insights into Information Intelligence for enhanced learning and the design of personalized learning pathways. The study contributes to the literature by offering a novel scale to measure attitudes toward artificial intelligence, specifically focusing on both general AI and Generative AI large language models, such as ChatGPT. Moreover, it highlights the critical need for AI literacy, ethical digital learning frameworks, and robust institutional policies to bridge the digital divide. Consequently, this work is framed as a preliminary exploratory investigation. Ultimately, these findings advance our knowledge of transformative digital learning processes and inform future strategies for human–machine integration in educational systems. Full article

Mixed-reality (MR) technologies have enhanced anatomy education through immersive three-dimensional visualization; however, most existing systems lack tutoring capabilities that respond contextually during anatomical exploration. This paper presents a reproducible MR anatomy learning platform implemented on the Apple Vision Pro that integrates the open-source Z-Anatomy atlas, with cardiovascular anatomy as the case domain. The system supports interactive exploration through hand gestures and eye tracking, alongside natural-language voice interaction. To provide context-grounded tutoring, we incorporate a retrieval-augmented generation (RAG) voice assistant whose responses are bounded by the Terminologia Anatomica knowledge base and weighted by the learner’s current spatial focus, with spatially anchored labels supporting contextual understanding. The platform was profiled on Apple Vision Pro hardware using Xcode Instruments and exercised through scenario-based walkthroughs of representative anatomical exploration tasks; the system met its real-time interaction and rendering thresholds across eight integrated anatomical systems. By leveraging open-source content and a substitutable AI backend, the architecture reduces software-licensing and development costs by an estimated one to two orders of magnitude relative to comparable proprietary systems and ports across XR platforms via a single bridge layer. Full article

The rise in large language models (LLMs) has sparked renewed interest in how firms, particularly multinational aid organizations, can enhance learning related to meta-dynamic capabilities (DCs), such as agility, sensing, and adaptation, in response to disasters and humanitarian crises. A key strategic priority is developing meta-rules that combine general engagement frameworks with locally tailored action plans, grounded in cultural and institutional contexts. LLMs offer potential in supporting this need, but premature deployment risks harmful or misleading outcomes. This underscores the critical importance of collaboration between artificial and human intelligence (AI-HI). While AI brings computational power, it lacks the tacit knowledge—encompassing cultural, contextual, and intuitive understanding—that is essential in high-stakes, unpredictable environments. Our experimental study provides two core insights: (1) AI alone cannot effectively handle tasks requiring tacit knowledge, and (2) AI-HI collaboration thrives when human input guides AI using deep awareness of local social and political dynamics. We contribute to the discourse on dynamic capabilities in multinational contexts during catastrophic situations by offering practical strategies to support successful AI-HI partnerships and a framework for organizations aiming to enhance meta-DCs through responsible, human-centered use of disruptive technologies. Our findings clarify how the international dimensions of these capabilities influence their effectiveness across diverse cultural and institutional environments. Full article

Machine learning (ML) methods for network anomaly detection are emerging as effective proactive strategies in threat hunting, substantially reducing the time required for threat detection and response. However, the challenges in training and maintaining ML models, coupled with frequent false positives, diminish their acceptance and trustworthiness. In response, Explainable AI (XAI) techniques have been introduced to enable cybersecurity operations teams to assess alerts generated by AI systems more confidently. Despite these advancements, XAI tools have encountered limited acceptance from incident responders and have struggled to meet the decision-making needs of both analysts and model maintainers. Large Language Models (LLMs) offer a unique approach to tackling these challenges. Through tuning, LLMs have the ability to discern patterns across vast amounts of information and meet varying functional requirements. In this research, we introduce the development of HuntGPT, a specialized intrusion detection dashboard created to implement a Random Forest classifier trained utilizing the KDD99 dataset. The tool incorporates XAI frameworks like SHAP and Lime, enhancing user-friendliness and intuitiveness of the model. When combined with a GPT-3.5 Turbo conversational agent, HuntGPT aims to deliver detected threats in an easily explainable format, emphasizing user understanding and offering a smooth interactive experience. We investigate the system’s comprehensive architecture and its diverse components, assess the prototype’s technical accuracy using the Certified Information Security Manager (CISM) Practice Exams, and analyze the quality of response readability across six unique metrics. Our results indicate that conversational agents, underpinned by LLM technology and integrated with XAI, can enable a robust mechanism for generating explainable and actionable AI solutions, especially within the realm of intrusion detection systems. Full article

Minimally invasive spine surgery (MISS) has progressively transformed the management of spinal disorders by reducing soft-tissue disruption, perioperative morbidity, and recovery time while maintaining clinical outcomes comparable to conventional open techniques. Beyond its technical evolution, MISS has increasingly assumed a central role in the treatment of bone-related spinal conditions, including vertebral fractures, degenerative instability, metastatic disease, and osteoporosis-associated pathology. This narrative review provides a comprehensive overview of the evolution of MISS with a specific focus on its interaction with vertebral bone biology, implant stability, and fusion processes. A structured literature search of the PubMed/MEDLINE database was conducted, including English-language studies published between 1980 and June 2025 addressing MISS techniques, enabling technologies, and bone-related clinical outcomes. Current evidence suggests that MISS may preserve paraspinal vascularization and soft tissue integrity, potentially supporting bone healing and fusion, although high-quality comparative data remain limited. The effectiveness of MISS in osteoporotic and metastatic vertebral disease is closely linked to bone quality, implant anchorage, and biomechanical considerations, particularly in the context of pedicle screw fixation and interbody support. Emerging technologies—including navigation, robotics, and artificial intelligence—may enhance accuracy in implant placement and reduce bone-related complications, but robust evidence of long-term benefit is still lacking. Despite its advantages, MISS presents important limitations, including a steep learning curve, increased costs, and uncertain superiority in terms of fusion rates and long-term biomechanical stability. Future research should prioritize high-quality comparative studies focusing on bone healing, implant integration, and patient-specific factors such as bone density. MISS should therefore be interpreted not only as a surgical paradigm shift but as an evolving strategy for optimizing outcomes in bone-related spinal disorders. Full article