Search Results (77)

This paper proposes a Human-AI collaborative framework for cybersecurity consulting tailored to the needs of small businesses, designed and implemented within a Master of Cybersecurity capstone program. The framework outlines a structured four-stage development model that integrates students into real-world consulting tasks while aligning with academic and industry objectives. Human–AI collaboration is embedded throughout the process, combining generative AI tools and domain-specific AI agents with human expertise to support the design, delivery, and refinement of consulting resources. The four stages include (1) AI agent development; (2) cybersecurity roadmap creation; (3) resource development; and (4) industry application. Each stage supports both development-oriented outputs—such as templates, training materials, and client deliverables—and research-oriented projects that explore design practices, collaboration models, and consulting strategies. This dual-track structure enables iterative learning and improvement while addressing educational standards and the evolving cybersecurity landscape for small businesses. This framework provides a scalable foundation for capstone-based consulting initiatives that bridge academic learning and industry impact through Human–AI collaboration. Full article

This paper critically examines artificial intelligence applications in stock market forecasting, addressing significant gaps in the existing literature that often overlook the tension between theoretical market efficiency and empirical predictability. While numerous reviews catalog methodologies, they frequently fail to rigorously evaluate model performance across different market regimes or reconcile statistical significance with economic relevance. We analyze techniques ranging from traditional statistical models to advanced deep learning architectures, finding that ensemble methods like Extra Trees, Random Forest, and XGBoost consistently outperform single classifiers, achieving directional accuracy of up to 86% in specific market conditions. Our analysis reveals that hybrid approaches integrating multiple data sources demonstrate superior performance by capturing complementary market signals, yet many models showing statistical significance fail to generate economic value after accounting for transaction costs and market impact. By addressing methodological challenges including backtest overfitting, regime changes, and implementation constraints, we provide a novel comprehensive framework for rigorous model assessment that bridges the divide between academic research and practical implementation. This review makes three key contributions: (1) a reconciliation of the Efficient Market Hypothesis with AI-driven predictability through an adaptive market framework, (2) a multi-dimensional evaluation methodology that extends beyond classification accuracy to financial performance, and (3) an identification of promising research directions in explainable AI, transfer learning, causal modeling, and privacy-preserving techniques that address current limitations. Full article

The global construction industry faces serious safety challenges, characterised by high rates of accidents and fatalities. A systematic review that analysed 95 academic articles from the Scopus and Web of Science databases investigated the current use of digital technologies (DTs) in construction safety management across developed and developing countries. The research discovered that digital technology applications in construction safety primarily focus on developing models and simulations. These technologies are making significant contributions by enhancing worker training, improving risk prediction capabilities, enabling real-time monitoring, facilitating better communication, and supporting more proactive safety interventions. The most frequently utilised digital technologies in this domain include virtual reality (VR), building information modelling (BIM), machine learning, and artificial intelligence (AI). Despite the promising potential of these technologies, their actual implementation remains somewhat limited, especially in developing countries. This study identified critical knowledge gaps, specifically the limited understanding of digital technology trends in construction safety management across different economic contexts, the insufficient research on strategies to increase digital technology adoption in the construction sector, and the need for more comprehensive investigations into how the technology adoption divide can be bridged. This research aimed to facilitate future empirical studies that can advance the understanding of digital technologies and the development of strategies to integrate them more comprehensively into construction safety practices. By providing a detailed overview of current digital technology applications, highlighting research limitations, and suggesting future research directions, this review seeks to contribute to both academic understanding and practical improvements in global construction industry safety. Full article

School dropout in higher education remains a significant global challenge with profound socioeconomic consequences. To address this complex issue, educational institutions increasingly rely on business intelligence (BI) and related predictive analytics, such as machine learning and data mining techniques. This systematic review critically examines the application of BI and predictive analytics for analyzing and preventing student dropout, synthesizing evidence from 230 studies published globally between 1996 and 2025. We collected literature from the Google Scholar and Scopus databases using a comprehensive search strategy, incorporating keywords such as “business intelligence”, “machine learning”, and “big data”. The results highlight a wide range of predictive tools and methodologies, notably data visualization platforms (e.g., Power BI) and algorithms like decision trees, Random Forest, and logistic regression, demonstrating effectiveness in identifying dropout patterns and at-risk students. Common predictive variables included personal, socioeconomic, academic, institutional, and engagement-related factors, reflecting dropout’s multifaceted nature. Critical challenges identified include data privacy regulations (e.g., GDPR and FERPA), limited data integration capabilities, interpretability of advanced models, ethical considerations, and educators’ capacity to leverage BI effectively. Despite these challenges, BI applications significantly enhance institutions’ ability to predict dropout accurately and implement timely, targeted interventions. This review emphasizes the need for ongoing research on integrating ethical AI-driven analytics and scaling BI solutions across diverse educational contexts to reduce dropout rates effectively and sustainably. Full article

This review synthesizes current knowledge on the transformative impacts of artificial intelligence (AI)—computational systems capable of performing tasks requiring human-like reasoning—on business innovation. It addresses the potential of AI to reshape strategies, operations, and value creation across various industries. Key themes include AI-driven business model innovation, human–AI collaboration, ethical governance, operational efficiency, customer experience personalization, organizational capability development, and adoption disparities. AI enables scalable product development, personalized service delivery, and data-driven strategic decisions. Successful implementations hinge on overcoming technical, cultural, and ethical barriers, with ethical AI adoption enhancing consumer trust and competitiveness, positioning responsible innovation as a strategic imperative. For practitioners, this review offers evidence-based frameworks for aligning AI with business objectives. For academics, it identifies research frontiers, including longitudinal impacts, context-specific roadmaps for small- and medium-sized enterprises, and sustainable innovation pathways. This review conceptualizes AI as a driver of systemic organizational transformation, requiring continuous learning, ethical foresight, and strategic ability for competitive advantage. Full article

The convergence of Virtual Reality (VR), Artificial Intelligence (AI), and the Internet of Things (IoT) offers transformative potential across numerous sectors. However, existing studies often examine these technologies independently or in limited pairings, which overlooks the synergistic possibilities of their combined usage. This systematic review adheres to the PRISMA guidelines in order to critically analyze peer-reviewed literature from highly recognized academic databases related to the intersection of VR, AI, and IoT, and identify application domains, methodologies, tools, and key challenges. By focusing on real-life implementations and working prototypes, this review highlights state-of-the-art advancements and uncovers gaps that hinder practical adoption, such as data collection issues, interoperability barriers, and user experience challenges. The findings reveal that digital twins (DTs), AIoT systems, and immersive XR environments are promising as emerging technologies (ET), but require further development to achieve scalability and real-world impact, while in certain fields a limited amount of research is conducted until now. This review bridges theory and practice, providing a targeted foundation for future interdisciplinary research aimed at advancing practical, scalable solutions across domains such as healthcare, smart cities, industry, education, cultural heritage, and beyond. The study found that the integration of VR, AI, and IoT holds significant potential across various domains, with DTs, IoT systems, and immersive XR environments showing promising applications, but challenges such as data interoperability, user experience limitations, and scalability barriers hinder widespread adoption. Full article

Recent advances in legal language processing have highlighted limitations in correlation-based artificial intelligence approaches, prompting exploration of Causal Artificial Intelligence (AI) techniques for improved legal reasoning. This systematic review examines the challenges, limitations, and potential impact of Causal AI in legal language processing compared to traditional correlation-based methods. Following the Joanna Briggs Institute methodology, we analyzed 47 papers from 2017 to 2024 across academic databases, private sector publications, and policy documents, evaluating their contributions through a rigorous scoring framework assessing Causal AI implementation, legal relevance, interpretation capabilities, and methodological quality. Our findings reveal that while Causal AI frameworks demonstrate superior capability in capturing legal reasoning compared to correlation-based methods, significant challenges remain in handling legal uncertainty, computational scalability, and potential algorithmic bias. The scarcity of comprehensive real-world implementations and overemphasis on transformer architectures without causal reasoning capabilities represent critical gaps in current research. Future development requires balanced integration of AI innovation with law’s narrative functions, particularly focusing on scalable architectures for maintaining causal coherence while preserving interpretability in legal analysis. Full article

Numerous studies have explored the role of colour in classroom environments and its effects on learning, cognition and motivation. However, research on coloured lighting remains limited, with most studies focusing only on correlated colour temperature (CCT). Addressing this gap, our study examines various chromatic lighting conditions that enhance learning outcomes while allowing for dynamic applications in educational settings. Conducted over three academic years in six primary classrooms, this quasi-experimental study employed a pretest and a control group to assess the effects of three chromatic lighting scenarios (orange, green and purple) on cognitive processes, emotional responses and basic instrumental learning. Descriptive, variance and comparative analyses revealed conclusive evidence of coloured lighting’s impact, though effects varied across different variables. The study highlights the potential of dynamic lighting approaches to support learning and suggests that AI-assisted lighting adjustments could aid teachers. The findings support the broader implementation of coloured lighting in primary classrooms, advocating for cost-effective, sustainable and adaptive solutions beyond conventional lighting. Such advancements are expected to enhance students’ learning, cognition and motivation while providing greater flexibility in educational environments. Full article

In this work, we propose a novel Personalised Large Language Model (PLLM) agent, designed to advance the integration and adaptation of large language models within the field of human–robot interaction and human–computer interaction. While research in this field has primarily focused on the technical deployment of LLMs, critical academic challenges persist regarding their ability to adapt dynamically to user-specific contexts and evolving environments. To address this fundamental gap, we present a methodology for personalising LLMs using domain-specific data and tests using the NeuroSense EEG dataset. By enabling the personalised data interpretation, our approach promotes conventional implementation strategies, contributing to ongoing research on AI adaptability and user-centric application. Furthermore, this study engages with the broader ethical dimensions of PLLM, critically discussing issues of generalisability and data privacy concerns in AI research. Our findings demonstrate the usability of using the PLLM in a human–robot interaction scenario in real-world settings, highlighting its applicability across diverse domains, including healthcare, education, and assistive technologies. We believe the proposed system represents a significant step towards AI adaptability and personalisation, offering substantial benefits across a range of fields. Full article

In contemporary society, electricity has become one of the most prevalent energy sources, with a global distribution network. The significance of energy efficiency has become a prominent subject of interest across various disciplines, leading to a notable surge in research on electricity over the last decade. The present paper explores the significance of examining power losses in power grids and proposes methods to identify and reduce them. The objective of this study is to methodically and systematically review existing databases to identify relevant studies pertinent to electricity losses in power grids. To this end, this study methodically categorizes energy losses into two primary classifications: technical losses and non-technical losses. To this end, two primary analytical approaches have been devised, providing a foundation for the prioritization of the most effective detection methods for each loss category, as well as the most recent findings in the literature on reducing these losses. The issue of power grid instability, stemming from fluctuations and voltage dips, was addressed, with power losses emerging as the primary source of concern. To this end, a comprehensive analysis of major academic databases, including Scopus, Web of Science, IEEE, and Google Scholar, was conducted to identify relevant research articles. This review introduces several important concepts for the energy field by introducing and analyzing the most relevant methods to identify and reduce power losses. The results of our study outlined the comprehensive analysis of 90 relevant studies on the benefits as well as the barriers encountered in the application of methods to identify and reduce electricity losses and the impact they have in the field under review. In conclusion, this paper emphasizes the importance of the in-depth study of the energy domain to achieve performance and contribute to the improvement of power grids. Future research directions are based on the implementation of artificial intelligence (AI) algorithms to achieve this goal. Full article

Bringing artificial intelligence (AI) and living intelligence into higher education has the potential to completely reshape teaching, learning, and administrative processes. Living intelligence is not just about using AI—it is about creating a dynamic partnership between human thinking and AI capabilities. This collaboration allows for continuous adaptation, co-evolution, and real-time learning, making education more responsive to individual student needs and evolving academic environments. AI-driven tools are already enhancing the way students learn by personalizing content, streamlining processes, and introducing innovative teaching methods. Adaptive platforms adjust material based on individual progress, while emotionally intelligent AI systems help support students’ mental well-being by detecting and responding to emotional cues. These advancements also make education more inclusive, helping to bridge accessibility gaps for underserved communities. However, while AI has the potential to improve education significantly, it also introduces challenges, such as ethical concerns, data privacy risks, and algorithmic bias. The real challenge is not just about embracing AI’s benefits but ensuring it is used responsibly, fairly, and in a way that aligns with educational values. From a sustainability perspective, living intelligence supports efficiency, equity, and resilience within educational institutions. AI-driven solutions can help optimize energy use, predict maintenance needs, and reduce waste, all contributing to a smaller environmental footprint. At the same time, adaptive learning systems help minimize resource waste by tailoring education to individual progress, while AI-powered curriculum updates keep programs relevant in a fast-changing world. This paper explores the disconnect between AI’s promise and the real-world difficulties of implementing it responsibly in higher education. While AI and living intelligence have the potential to revolutionize the learning experience, their adoption is often slowed by ethical concerns, regulatory challenges, and the need for institutions to adapt. Addressing these issues requires clear policies, faculty training, and interdisciplinary collaboration. By examining both the benefits and challenges of AI in education, this paper focuses on how institutions can integrate AI in a responsible and sustainable way. The goal is to encourage collaboration between technologists, educators, and policymakers to fully harness AI’s potential while ensuring that it enhances learning experiences, upholds ethical standards, and creates an inclusive, future-ready educational environment. Full article

The manner of achieving high-quality economic development in China through artificial intelligence (AI) has become a focus of academic attention. On the basis of panel data of prefecture-level cities in China from 2010 to 2021, this research utilizes the exogenous impact of the implementation of the National New Generation Artificial Intelligence Innovation and Development Pilot Zone (AIPZ) to explore the causal effect between AI and green total factor productivity (GTFP). The results are as follows: (1) AI has a significant enhancement effect on urban GTFP. After using a series of robustness tests, such as parallel trend sensitivity test, heterogeneity treatment effect test, and machine learning, this conclusion remains robust. (2) Subsequent mechanism analysis shows that the impact of AI on urban GTFP is mainly achieved by enhancing urban green innovation, promoting industrial structure upgrading, and reducing land resource misallocation. (3) Lastly, the effect of AI on urban GTFP is heterogeneous. AI has also markedly significant enhancement effects on high human capital, non-resource-based economies, and high levels of green consumption behavior. This study provides useful insights for China to develop AI and achieve green development. Full article

Personalized learning (PL) has emerged as a promising approach to address diverse educational needs, with artificial intelligence (AI) playing an increasingly pivotal role in its implementation. This systematic literature review examines the landscape of PL across various educational contexts, focusing on the use of AI and associated challenges. Using the PRISMA guidelines, 68 empirical studies published between 2018 and 2024 were analyzed, revealing correlations between academic levels, learning modalities, technologies, and implementation barriers. Key findings include (a) predominant use of AI in higher education PL implementations, (b) preference for blended learning in secondary and elementary education, (c) shift from technological to pedagogical barriers across educational levels, and (d) persistent psychological barriers across all contexts. This review provides valuable insights for educators, policymakers, and researchers, offering a comprehensive understanding of the current state and future directions of AI-driven personalized learning. Full article

This study explores the implementation of artificial intelligence (AI) and data literacy in rural Catalan schools by analyzing teacher knowledge, use, and perceptions. Data were collected from a representative sample of teachers at these schools to examine their understanding of AI and data literacy, how they utilize these technologies, and their perspectives on their applications. The results indicate that although over half of the teachers reported moderate to high AI knowledge, classroom implementation remains limited. Teachers primarily employed AI for text generation and content detection, with less frequent use of video generation or simulations. Common applications included lesson planning and material creation. Concerns centered on ethical implications, academic integrity, and a potential reduction in students’ critical thinking skills. This study reveals a moderate level of AI and data literacy knowledge among teachers in Catalan rural schools, contrasting with its limited practical application in the classroom. Teachers mainly use AI for text generation and content detection. Regarding data literacy, teachers demonstrated knowledge but lacked practical skills. These findings reveal a disconnect between theoretical AI knowledge and its practical application in the classroom, emphasizing the need for enhanced training and support to facilitate effective AI integration in education Full article

The integration of Artificial Intelligence (AI) in education has transformed academic learning, offering both opportunities and challenges for students’ development. This study investigates the impact of AI technologies on students’ learning processes and academic performance, with a focus on their perceptions and the challenges associated with AI adoption. Conducted at the National University of Science and Technology POLITEHNICA Bucharest, this research involved second-year students who had direct experience with AI-enhanced learning environments. Using purposive sampling, 85 participants were selected to ensure relevance. Data were collected through a structured questionnaire comprising 11 items as follows: seven closed-ended questions assessing perceptions, usage, and the effectiveness of AI tools; and four open-ended questions exploring experiences, expectations, and concerns. Quantitative data were analyzed using frequency and percentage calculations, while qualitative responses were subjected to thematic analysis, incorporating both vertical (individual responses) and horizontal (cross-dataset) approaches to ensure comprehensive theme identification. The findings reveal that AI offers significant benefits, including personalized learning, improved academic outcomes, and enhanced student engagement. However, challenges such as over-reliance on AI, diminished critical thinking skills, data privacy risks, and academic dishonesty were also identified. The study underscores the necessity of a structured framework for AI integration, supported by ethical guidelines, to maximize benefits while mitigating risks. In conclusion, while AI holds immense potential to enhance learning efficiency and academic performance, its successful implementation requires addressing concerns related to accuracy, cognitive disengagement, and ethical implications. A balanced approach is essential to ensure equitable, effective, and responsible learning experiences in AI-enhanced educational environments. Full article