International Journal of AI, Engineering and Management Studies (IJAIEMS)
International Journal of AI, Engineering and Management Studies (IJAIEMS) is a peer-reviewed, open-access scholarly journal dedicated to publishing high-quality research in the interdisciplinary fields of Artificial Intelligence, Engineering, and Management. The journal aims to provide a global platform for researchers, academicians, industry experts, and practitioners to share innovative ideas, research findings, and practical insights that contribute to technological advancement and organizational development.
Essay Publication Research and Consultancy
Artificial Intelligence for Smart Renewable Energy Grids: A Structured Literature Review and Integrated Framework
Renewable energy is being added to the grid faster than the grid itself was ever built to handle. Solar and wind depend on the weather, so their output goes up and down in ways that traditional, mostly one-directional grid infrastructure was not designed to absorb. That mismatch can create voltage instability, energy imbalance, and inefficient dispatch [1], [2]. This paper is a structured literature review of how Artificial Intelligence (AI), including classical machine learning, deep learning, graph neural networks, physics-informed learning, explainable AI, and reinforcement learning, can be built into smart grid systems to deal with these problems. Using twenty-three peer-reviewed and technical-report sources found through a systematic search process, this review pulls together forecasting models for renewable generation and demand, topology-aware and physically-constrained state estimation, explainability methods that help build control-room trust, and reinforcement-learning controllers used for battery scheduling and microgrid dispatch. From there, the paper proposes an integrated AI-powered smart grid framework, walks through a real-world case study of duck-curve management at the California Independent System Operator (CAISO), and discusses the trade-offs, deployment challenges, and open research questions tied to each method. The evidence shows that AI-based forecasting and dispatch improve renewable utilization and supply-demand balance compared to conventional rule-based control, though results are context-specific. Cybersecurity, model transferability, data privacy, and computational scalability are still the main things standing in the way of wider deployment.
Smart grid; renewable energy; artificial intelligence; machine learning; deep learning; graph neural networks; physics-informed neural networks; explainable AI; reinforcement learning; literature review.
Copyright (c) 2026 RatibSikder, Abdur R. Sikder (Author) https://creativecommons.org/licenses/by/4.0
The Future of AI Management: From Intelligent Decision Support to Autonomous Organizational Systems
Artificial intelligence (AI) is transforming management by moving organizations from traditional analytical processes toward intelligent, adaptive, and increasingly autonomous systems. This paper examines the evolution of AI management from intelligent decision-support technologies to autonomous organizational systems capable of analyzing information, generating recommendations, coordinating workflows, and executing selected tasks with limited human intervention. The study explores the growing role of generative AI, predictive analytics, AI agents, human–AI collaboration, automated decision-making, and organizational intelligence. Particular attention is given to changes in managerial responsibilities, decision rights, workforce transformation, AI governance, explainability, cybersecurity, accountability, and ethical oversight. The paper proposes a layered approach in which data intelligence, predictive analytics, generative intelligence, agentic execution, governance mechanisms, and human leadership operate as interconnected components. The analysis suggests that AI can improve organizational responsiveness, productivity, strategic planning, and operational efficiency, but increased autonomy also creates risks associated with bias, model failure, security threats, excessive automation, and unclear accountability. The future of AI management is therefore expected to emphasize controlled autonomy rather than unrestricted automation. Effective human–AI collaboration, continuous monitoring, transparent governance, and clearly defined decision boundaries will be essential for developing trustworthy autonomous organizational systems.
Artificial Intelligence, AI Management, Generative AI, Agentic AI, Autonomous Organizations, Decision Support, Human–AI Collaboration, Organizational Transformation, AI Governance, Strategic Management
Copyright (c) 2026 Sai Maneesh (Author) https://creativecommons.org/licenses/by/4.0
Neuro-Symbolic AI for Explainable and Reliable Intelligent Systems
Artificial Intelligence (AI) systems have achieved significant progress in areas such as classification, prediction, natural language processing, computer vision, and autonomous decision-making. However, many modern neural models operate as black-box systems, making it difficult to understand how a particular prediction or decision is generated. Limited interpretability, weak formal reasoning, sensitivity to distribution changes, and difficulties in verifying model behavior create challenges for deploying AI in safety-critical and high-stakes environments. Neuro-Symbolic Artificial Intelligence (Neuro-Symbolic AI) provides a promising approach by integrating the pattern-learning capabilities of neural networks with the structured representation and reasoning capabilities of symbolic AI. Recent research identifies explainability, trustworthiness, reasoning, robustness, and verification as important areas for Neuro-Symbolic AI development. This paper presents a conceptual Neuro-Symbolic AI framework for developing explainable and reliable intelligent systems. The proposed methodology combines neural perception, knowledge representation, symbolic reasoning, decision generation, and explanation mechanisms. The framework is designed to improve transparency by producing reasoning traces and rule-based explanations alongside predictive outputs. A multi-dimensional evaluation strategy based on predictive performance, explainability, robustness, consistency, and reasoning accuracy is proposed. The study demonstrates how the integration of neural learning and symbolic reasoning can provide a stronger foundation for trustworthy intelligent systems while addressing limitations associated with purely data-driven models.
Neuro-Symbolic AI, Explainable AI, Symbolic Reasoning, Neural Networks, Trustworthy AI, Knowledge Representation, Intelligent Systems.
Copyright (c) 2025 Marissa E. Wechsler (Author) https://creativecommons.org/licenses/by/4.0
Cybersafe Portal: A Scalable Web-Based Framework for Cybercrime Reporting and Prevention
Cyber crime has surged due to rapid digitization, yet reporting mechanisms have often failed to keep pace. Existing national portals and research prototypes frequently suffer from limited usability, opaque case tracking, fragmented awareness resources, and unverified scalability under load. The Cyber-Safe Portal is a web-based reporting framework developed to overcome these limitations by providing a unified solution for the secure submission of incidents, encrypted evidence management, transparent case tracking via automated notifications, and personalized awareness materials. This system implements a three-tier architecture utilizing React.js, Node.js/Express, and MongoDB, augmented by Redis caching and Kubernetes orchestration. We evaluated the platform’s preliminary performance, security, and usability in a simulated test environment. Initial load testing indicates that the auto-scaling architecture can accommodate high-concurrency traffic with manageable response times. Security assessments using OWASP ZAP and manual testing confirmed baseline resilience against common vulnerabilities, while a pilot usability study demonstrated high user satisfaction and intuitive navigation. These preliminary findings highlight the potential for adopting integrated, scalable frameworks to improve public engagement with cybercrime reporting.
Cybercrime reporting, incident management, web-based framework, secure architecture, cybersecurity awareness, usability evaluation.
Fatema Tuz Zohora, Pratyay Paul, Aashish Kiphayet, Nafis Wasel
Copyright (c) 2026 Fatema Tuz Zohora, Pratyay Paul, Aashish Kiphayet, Nafis Wasel (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence Adoption and Organizational Performance: A Strategic Management Perspective
Artificial Intelligence (AI) has become an important strategic technology for organizations seeking to improve operational efficiency, decision-making, innovation, customer service, and competitive advantage. Organizations are increasingly adopting machine learning, predictive analytics, natural language processing, intelligent automation, and generative AI across different business functions. However, the adoption of AI does not automatically result in improved organizational performance. The value generated by AI depends on technological readiness, organizational capabilities, leadership support, employee skills, data quality, strategic alignment, and responsible governance. This research examines the relationship between AI adoption and organizational performance from a strategic management perspective. The study adopts a conceptual and literature-based methodology to examine the major factors influencing AI adoption and the organizational outcomes associated with effective AI implementation. The analysis indicates that AI can contribute to operational efficiency, improved decision-making, innovation, customer satisfaction, employee productivity, and competitive advantage when it is strategically integrated with organizational resources and processes. At the same time, organizations face challenges related to implementation costs, data privacy, cybersecurity, employee resistance, skill shortages, ethical concerns, and integration with legacy systems. The study emphasizes that AI should not be considered merely as a technological investment but as a strategic organizational capability. Effective leadership, workforce development, AI governance, and continuous performance evaluation are essential for converting AI adoption into sustainable organizational value.
Artificial Intelligence, AI Adoption, Organizational Performance, Strategic Management, Digital Transformation, AI Capability, Business Innovation, Competitive Advantage, AI Governance.
Copyright (c) 2025 Edson Oliveira (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence Adoption and Its Impact on MSME Business Performance
Micro, Small, and Medium Enterprises (MSMEs) play an important role in employment generation, innovation, income creation, and economic development. However, MSMEs often face challenges related to limited financial resources, skilled workforce, operational efficiency, customer management, competition, and access to advanced technologies. Artificial Intelligence (AI) has emerged as an important digital technology that can help MSMEs address several of these challenges. AI-based applications can support business forecasting, customer relationship management, marketing, financial analysis, inventory management, fraud detection, process automation, and decision-making. This research examines the adoption of Artificial Intelligence and its potential impact on MSME business performance. The study develops a conceptual framework connecting AI adoption with operational efficiency, decision-making capability, customer satisfaction, innovation, cost optimization, and overall business performance. The paper also discusses the major factors influencing AI adoption, including technological readiness, financial capability, employee skills, management support, data availability, perceived benefits, and security concerns. The analysis indicates that effective AI adoption can improve productivity, reduce repetitive work, strengthen customer engagement, enhance decision quality, and support business growth. However, the benefits of AI are dependent on appropriate implementation, employee training, data quality, affordability, and organizational readiness. The study concludes that MSMEs should adopt AI gradually through practical, scalable, and business-oriented solutions rather than treating AI adoption solely as a technological investment
Artificial Intelligence, MSMEs, AI Adoption, Business Performance, Digital Transformation, Productivity, Innovation, Decision-Making
Copyright (c) 2026 Praveen Gande (Author) https://creativecommons.org/licenses/by/4.0
Generative AI-Based Intelligent Information Retrieval and Knowledge Management
The rapid growth of digital information has created significant challenges for organizations in retrieving, organizing, and managing knowledge effectively. Traditional information retrieval systems primarily depend on keyword matching and predefined indexing techniques, which may not adequately understand the context, intent, and relationships within large collections of unstructured information. Generative Artificial Intelligence (GenAI), particularly large language models, provides new opportunities for improving information retrieval and knowledge management by enabling semantic understanding, natural-language interaction, contextual response generation, and automated knowledge organization. This paper proposes a Generative AI-Based Intelligent Information Retrieval and Knowledge Management framework that integrates document processing, semantic representation, intelligent retrieval, generative response generation, and knowledge management. The proposed approach allows users to submit natural-language queries and obtain contextually relevant responses from organizational knowledge repositories. The framework also supports automatic summarization, knowledge classification, question answering, and continuous knowledge updating. The paper discusses the architecture, major components, operational workflow, benefits, challenges, and potential applications of the proposed framework. The study demonstrates that the integration of Generative AI with information retrieval can improve accessibility, reduce information-search effort, and support more effective organizational knowledge utilization. However, issues related to hallucination, data privacy, security, knowledge freshness, and response reliability must be addressed for dependable real-world deployment.
Generative Artificial Intelligence, Information Retrieval, Knowledge Management, Large Language Models, Natural Language Processing, Semantic Search, Intelligent Systems
Copyright (c) 2026 D. Nirosha (Author) https://creativecommons.org/licenses/by/4.0
Predictive AI Models For Sovereign Debt Risk In Emerging Economies: A Machine Learning Framework For Early Warning And Default Prediction
Sovereign debt crises within emerging economies continue to be one of the most destabilising forces in the global financial system, but as illustrated, current risk assessment practices – led by agency-produced credit ratings and linear econometric models – have sadly not guarded against pre-crisis warnings. This study creates and validates Sovereign Debt AI Prediction Framework (SDAIPF), an integrated machine learning architecture that combines gradient-boosted ensemble methods, Long-short-term memory (LSTM) neural networks and eXplainable Artificial Intelligence (XAI) to forecast sovereign default risk for a panel of 48 emerging economies between the years 2000-2023. The SDAIPF is measured using macroeconomic, fiscal, external-sector, institutional quality and financial market indicators from World Bank, IMF and BIS databases with the out-of-sample area under the receiver operator characteristic curve (AUC-ROC) of 0.921 and F1-score of 0.884, significantly better than traditional logit models (AUC-ROC = 0.741) and sovereign credit ratings. Near-term default events are mainly driven by external debt-to-GDP ratio, real effective exchange rate volatility, fiscal primary balance and institutional governance quality, as shown by SHAP (SHapley Additive exPlanations) analysis. The framework exhibits good generalisability across regions and with a good early-warning period of 14-month average crystalisation in advance. This research makes several key contributions to the field of debt distress that are relevant in emerging markets: First, developing an early warning system of the type of direct policy utility of finance ministries, international financial institutions and sovereign bond investors, which is appropriate and understandable in emerging market environments.
Sovereign Debt Risk, Machine Learning, Emerging Economies, Early Warning Systems, Gradient Boosting, LSTM, SHAP, Fiscal Sustainability
Copyright (c) 2026 Rajesh Shahi (Author) https://creativecommons.org/licenses/by/4.0
Comparative Analysis of Advanced Oxidation Processes (AOPs) vs. Granular Activated Carbon (GAC) for PFAS Removal in U.S. Groundwater Suppliers: A Techno-Economic and Life Cycle Assessment
Background Per- and polyfluoroalkyl substances (PFAS) are a major concern in the U.S. for groundwater quality because they are persistent and not easily treated with conventional methods. Advanced Oxidation Processes (AOPs) and Granular Activated Carbon (GAC) have been very well studied of the available remediation technologies, yet there is limited complete comparative evaluations. Objective This study presents a techno-economic and life cycle assessment approach comparing the technical, economic, environmental and operational performance of AOPs and GAC for the removal of PFAS. Methods The survey was a quantitative, cross sectional study that targeted 400 water treatment professionals in the U.S. Data were gathered by means of a structured questionnaire and analysed by descriptive statistics, reliability analysis, Pearson correlation, multiple regression, independent samples t-test, ANOVA and Chi-square tests in IBM SPSS Statistics 29. Results The measurement model showed a high level of reliability (Cronbach's Alpha = 0.956). The overall regression model was strongest for Technical Performance (β = 0.341) and Environmental Sustainability (β = 0.296), and explained 68.7% of the variance (R² = 0.687, p < 0.001). The overall performance of AOPs was significantly better than that of GAC (t = 4.81, p < 0.001). Demographic factors, such as professional experience and organizational affiliation, also had significant effects on technology preferences. Conclusion Overall, AOP showed better performance for PFAS remediation, especially in terms of treatment effectiveness and environmental sustainability. The results indicate the need to use multi-dimensional approaches to decision making that integrate technical, economic, operational and life cycle evaluations to inform sustainable treatment and policy decisions regarding PFAS in U.S. groundwater systems.
PFAS; Advanced Oxidation Processes; Granular Activated Carbon; Groundwater Treatment; Techno-Economic Assessment; Life Cycle Assessment; Environmental Sustainability.
Copyright (c) 2025 Mir Akib Jawed (Author) https://creativecommons.org/licenses/by/4.0
Digital Nomad Visas and Their Macroeconomic Effects on Emerging Economies
This study explores macroeconomic impacts of digital nomad visas (DNV) in emerging markets and looks into the potential for creating novel economic growth through digital nomads, as well as the strengthening of the existing inequalities. This study uses a panel data econometric approach with the use of econometric estimator of the two types, namely panel-corrected standard errors (PCSE) and system generalised method of moments (GMM), on the data of 28 emerging economies over the period of 2019-2024, then followed 5 case studies in Indonesia, Portugal, Mexico, Thailand, and Costa Rica with the use of quality evidence shown in the case studies. Theories providing insights into transmission mechanisms, such as a novel endogenous growth theory-based approach (the Internal Growth Theory) and the dual economy models or knowledge spillover theory, are combined in a novel theoretical construct called the Nomadic Capital Transmission Framework (NCTF). The GDP per capita growth (β = 0.31, p< .01), value-added in services (β = 0.28, p< .01), and investment in digital infrastructure (β = 0.22, p< .05) all grew significantly in the DNV countries. Increasing housing cost inflation was observed (β = 0.44, p < .001), and a growing income Gini coefficient was noted in urban zones (β = 0.19, p < .05); although these were found to be disproportionately affecting coastal and metropolitan areas mainly. Mediation pathways of technology spillovers were validated. By studying nomadic capital as a unique source of foreign income inflow, rather than as either traditional foreign direct investment (FDI), or tourism, this study contributes to the fields of dual economy and endogenous growth theory, by identifying analytical frameworks specifically for the analysis of this northern variant. Emerging economies policymakers should consider introducing such distributional safeguards in the design of DNV programmes, for example including: rental market regulations; community benefit agreements; and digital skills co-investment requirements. Rigorous quantification of DNV effects on growth and inequality is among the first empirical macro-panel studies to break from tourism-focused studies and apply to macro-economic policies
Digital Nomad Visa; Macroeconomic Effects; Emerging Economies; Income Inequality; Knowledge Spillover; Remote Work Migration;
Dynamic Mixture-Of-Experts (Moe) And Human-In-The-Loop Models: Balancing Automation With Hr Oversight In Fraud Detection
Fraud keeps evolving, and old detection systems can't keep up. Rule based models and standalone machine learning tools catch some anomalies, but they also flag too many false positives. They struggle with concept drift, and they miss subtle signs that separate real fraud from honest mistakes. You already know that hiring more investigators isn't the answer either. It costs too much, and it doesn't scale. This paper introduces a new approach. We combine Dynamic Mixture of Experts models with Human in the Loop oversight. This hybrid system lets you keep control over sensitive decisions while giving your team the speed and scale that automation provides. It builds a two-way pipeline that sends each case to the right place, whether that's an expert model or a human analyst, based on how confident the system is and how complex the case looks. Here's how it works. The system splits incoming transactions and behavior signals across expert modules. Each module specializes in a fraud type, like identity theft, collusion, procurement fraud, or payroll manipulation. A gating network decides how much weight each expert gives, and it keeps learning as data patterns shift. This stops the system from falling behind when fraud tactics change. We tested this framework on over 4.7 million transaction records and 12,000 confirmed fraud cases pulled from financial services and corporate HR systems. The results speak for themselves. The system caught 94.3 percent of fraud cases while keeping false positives at just 3.2 percent. That's a 23 percent improvement in F1 score over current deep learning models, and it cut average case resolution time by 41 percent compared to manual review. Beyond the numbers, we looked at what this system means for your team. Routine alerts get handled automatically. Complex but familiar cases go through assisted review, where your investigators get preloaded context to work faster. Genuinely new or high stakes cases get full human investigation. This setup protects the value of investigative work and keeps your team engaged, instead of reducing everyone to rubber stamping machine decisions. We also added a learning loop that updates the expert models using your team's feedback, without erasing what the system already learned. This means the system adapts to new fraud tactics while keeping its memory of past patterns intact. Looking ahead, we want to explore federated learning so organizations can share fraud knowledge without sharing raw data. We also want to build expert modules that reason through your specific policies, and study how working with this system affects your team's skills and job satisfaction over time
Mixture-of-Experts; Human-in-the-Loop; Fraud Detection; Dynamic Routing; Explainable AI; Human-AI Collaboration; Concept Drift; Uncertainty Quantification; Algorithmic Oversight; Organizational Decision-Making.
Tamanna Jahan, Dilruba Kabita, Walter M. Campbell, Farzana Afroz, Ashim Sen Gupta
Copyright (c) 2025 International Journal of AI, Engineering and Management Studies (IJAIEMS) https://creativecommons.org/licenses/by/4.0
Ethical Deployment of Large Language Models in Higher Education
Ethical deployment of Large Language Models (LLMs) in higher education refers to the responsible design, implementation, and governance of AI systems used for teaching, learning, assessment, and academic support. As universities increasingly integrate LLMs into educational workflows, concerns around bias, academic integrity, data privacy, transparency, and accountability have become central. Ethical deployment requires aligning AI outputs with curriculum standards while ensuring fairness, explainability, and human oversight. By integrating Explainable Artificial Intelligence (XAI), Retrieval-Augmented Generation (RAG), curriculum-aware AI, Human-in-the-Loop (HITL) validation, and governance frameworks, institutions can mitigate risks while maximizing educational benefits. This study explores ethical deployment of LLMs in higher education through a qualitative literature review. Findings indicate that ethical frameworks improve trust, academic integrity, learning outcomes, and institutional accountability. Despite challenges such as regulatory uncertainty, model bias, and infrastructure limitations, ethical deployment remains essential for sustainable AI integration in universities.
Large Language Models, higher education, AI ethics, curriculum-aware AI, academic integrity, Explainable AI, governance frameworks
Benjamin Sterling, Victoria Hayes, Samuel Redford
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc/4.0
AI Supported Learning Analytics for Student Performance Prediction
Artificial Intelligence (AI)-supported learning analytics has emerged as a transformative approach for predicting student performance and improving educational decision-making in higher education. By combining learning analytics with advanced Artificial Intelligence techniques such as Large Language Models (LLMs), machine learning, and predictive modeling, educational institutions can identify learning patterns, forecast academic outcomes, and provide timely interventions for students at risk of poor performance. Traditional learning analytics systems often rely on descriptive statistics and historical academic records, limiting their ability to deliver personalized and real-time educational support. AI-supported learning analytics overcomes these limitations by integrating curriculum-aware artificial intelligence, Retrieval-Augmented Generation (RAG), Explainable Artificial Intelligence (XAI), Human-in-the-Loop (HITL) verification, and institutional governance frameworks to improve prediction accuracy and educational transparency. This study investigates AI-supported learning analytics for student performance prediction through a qualitative review of recent scholarly literature. The findings indicate that AI-enhanced learning analytics significantly improve prediction accuracy, learner engagement, personalized learning, curriculum alignment, and institutional decision-making while reducing dropout risks and improving academic success. Although implementation requires technological infrastructure, continuous curriculum updates, data governance, and faculty development, AI-supported learning analytics provides a sustainable framework for intelligent, ethical, and learner-centered educational environments.
Learning analytics, student performance prediction, Artificial Intelligence, curriculum-aware artificial intelligence, Explainable Artificial Intelligence, Retrieval-Augmented Generation, higher education
Lucas Fairchild, Grace Holloway, Henry Blackwood
The Global Impact of Foreign Currency Borrowing on Corporate Valuation: Evidence From Emerging Markets
This paper analyses the effects of foreign currency borrowing on corporate valuation in the emerging markets based on the panel data and econometric models. The value is determined through the Tobin Q and market capitalization, in which foreign currency debt was the most important independent variable. They can be controlled variables, which are exchange rate volatility, interest rate differentials, firm size, profitability, leverage, and GDP growth, as well as inflation. Findings indicate that although foreign debt has the potential to improve valuation by providing accessible and inexpensive financing as well as access to international markets, currency mismatches and currency volatility risks might negatively affect firm value. One of the most effective ways to maximize the benefits and reduce foreign currency borrowing risks is to have good hedging, profitability, and regulatory support.
Foreign Currency Borrowing, Corporate Valuation, Exchange Rate Volatility, Emerging Markets, Financial Risk Management
Dr. Sumaira Gulzar, Dr. Ajaz Ahmad Mistree, Asif Nabi, Irfan Subhan Wani
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc/4.0
Intelligent Academic Support Systems Based on Retrieval-Augmented Generation
Intelligent academic support systems based on Retrieval-Augmented Generation (RAG) represent a significant advancement in educational artificial intelligence by combining the reasoning capabilities of Large Language Models (LLMs) with external knowledge retrieval mechanisms. Traditional academic support tools often rely solely on pre-trained language models, which may produce hallucinations, outdated explanations, or curriculum-inconsistent responses. RAG-based systems address these limitations by retrieving relevant and verified academic content from curriculum repositories, institutional databases, digital libraries, and structured knowledge graphs before generating responses. When integrated with curriculum-aware artificial intelligence, Explainable Artificial Intelligence (XAI), Human-in-the-Loop (HITL) validation, and educational governance frameworks, RAG-based academic support systems significantly enhance instructional accuracy, learner engagement, personalization, and decision-making quality. This study explores intelligent academic support systems based on RAG through a qualitative review of recent scholarly literature. Findings indicate that RAG-driven systems improve academic performance, curriculum alignment, transparency, and learner trust while reducing misinformation and hallucinations. Despite challenges such as infrastructure requirements, knowledge base maintenance, and governance complexity, RAG-based academic support systems provide a scalable and reliable foundation for modern educational environments.
Retrieval-Augmented Generation, academic support systems, educational AI, curriculum-aware AI, knowledge graphs, Explainable AI, personalized learning
Julian Hartley, Amelia Brooks, Oliver Kensington
Trustworthy Retrieval Augmented Learning Systems
Trustworthy Retrieval-Augmented Learning Systems (RALS) have emerged as an important innovation in educational artificial intelligence by combining the reasoning capabilities of Large Language Models (LLMs) with verified knowledge retrieval to deliver accurate, transparent, and curriculum-aligned learning experiences. Traditional language models often generate hallucinated or outdated information because they rely primarily on pre-trained parameters rather than current institutional knowledge. Retrieval-Augmented Generation (RAG) addresses these limitations by retrieving information from trusted educational repositories before producing responses. When integrated with curriculum-aware artificial intelligence, explainable artificial intelligence (XAI), human-in-the-loop validation, and ethical governance, retrieval-augmented learning systems become more reliable, accountable, and suitable for higher education. This study investigates trustworthy retrieval-augmented learning systems through a qualitative review of recent scholarly literature. The findings indicate that these systems significantly improve instructional accuracy, learner engagement, curriculum alignment, and academic decision support while reducing misinformation and hallucinations. Furthermore, explainability, transparency, and institutional governance strengthen learner confidence and educational accountability. Although implementation requires continuous knowledge management, technical infrastructure, and faculty participation, trustworthy retrieval-augmented learning systems provide a sustainable foundation for responsible artificial intelligence in modern higher education.
Retrieval-Augmented Generation, trustworthy artificial intelligence, curriculum-aware artificial intelligence, intelligent tutoring systems, explainable artificial intelligence, educational technology, higher education
Jonathan Mercer, Emily Ashbourne, Christopher Bennett
Labor Market Segmentation and Employment Inequality in the Era of Digital Transformation
The rapid expansion of digital technologies has fundamentally transformed labor markets across the world, creating new opportunities for productivity growth, innovation, and economic development. At the same time, digital transformation has intensified existing forms of labor market segmentation and contributed to widening employment inequalities among different groups of workers. The integration of automation, artificial intelligence, digital platforms, and information technologies has altered the nature of work, reshaped occupational structures, and changed the demand for skills. While highly skilled workers often benefit from increased access to quality employment, higher wages, and greater career mobility, low-skilled workers frequently face job displacement, precarious employment conditions, and limited opportunities for advancement. These developments have generated concerns regarding the equitable distribution of economic gains associated with technological progress. The analysis demonstrates that digital transformation simultaneously creates opportunities and risks. While digitalization contributes to productivity enhancement and the creation of new occupations, it also reinforces structural inequalities by concentrating benefits among workers with advanced skills and digital competencies. The findings highlight the importance of inclusive labor market policies, continuous workforce training, digital literacy development, and social protection mechanisms in addressing emerging forms of employment inequality. The article concludes that achieving equitable labor market outcomes in the digital age requires coordinated efforts from governments, employers, educational institutions, and other stakeholders to ensure that technological progress promotes inclusive and sustainable economic development.
Labor Market Segmentation, Employment Inequality, Digital Transformation, Digital Economy, Automation, Artificial Intelligence, Gig Economy, Wage Inequality, Skill Polarization, Workforce Development, Employment Opportunities, Digitalization
The Impact of Digital Economy Development on Sustainable Urban and Rural Growth in China
The rapid expansion of the digital economy has emerged as a transformative force shaping economic growth, social development, and environmental sustainability across China. As digital technologies such as artificial intelligence, big data, cloud computing, e-commerce, fintech, and digital platforms become increasingly integrated into economic activities, they are creating new opportunities for balanced urban and rural development. This article examines the impact of digital economy development on sustainable urban and rural growth in China, focusing on its contributions to economic productivity, employment creation, industrial upgrading, social inclusion, environmental governance, and regional development. The study explores how digital infrastructure and technological innovation have enhanced resource allocation, improved public service delivery, promoted rural revitalization, and strengthened connections between urban and rural economies. At the same time, it highlights challenges associated with digital inequality, labor market polarization, skill disparities, and uneven access to digital resources that may hinder inclusive and sustainable growth. The analysis reveals that the digital economy serves as a critical driver of modernization by fostering innovation, increasing efficiency, and supporting environmentally responsible development practices. Furthermore, the integration of digital technologies into agriculture, manufacturing, education, and public administration has accelerated economic transformation while improving the quality of life for both urban and rural populations. The findings suggest that sustained investments in digital infrastructure, human capital development, digital literacy, and inclusive policy frameworks are essential for maximizing the benefits of digitalization and ensuring balanced regional development. The article concludes that the digital economy has significant potential to support China's long-term sustainability objectives by promoting coordinated urban-rural development, enhancing economic resilience, and facilitating inclusive growth in the digital era
Digital economy, sustainable development, urban growth, rural revitalization, China, digital transformation, economic development, digital infrastructure, regional development, inclusive growth.
AI-Driven Big Data Policy Systems and Agentic Governance: Evidence from Regional Economic Development in China
This study examines how AI-driven big data policy systems and agentic governance frameworks influence regional economic development in China. It explores how data governance, digital infrastructure, and AI agent architectures collectively shape productivity, innovation, and spatial economic disparities. Positioning big data policy as part of China’s digital transformation strategy, the study evaluates its role in industrial upgrading, resource allocation efficiency, and the development of regional innovation ecosystems. It further considers how AI governance mechanisms and agent-based decision systems improve policy execution, transparency, and trust in digital public infrastructure. At the same time, the analysis addresses structural challenges such as uneven technological adoption, labor market segmentation, and regional digital inequality. By integrating perspectives from the digital economy, AI agent architectures, and trustworthy AI governance, the study provides a unified framework for understanding how intelligent policy systems reshape regional development in China
Big data policy; regional economic development; digital economy; China; data governance; industrial upgrading; economic disparity; digital transformation
Artificial Intelligence Adoption in Higher Education: Opportunities, Risks, and Student Outcomes
Artificial Intelligence (AI) is rapidly transforming higher education by reshaping teaching, learning, assessment, and institutional management practices. The increasing adoption of AI-driven technologies offers significant opportunities to enhance educational accessibility, personalize learning experiences, improve administrative efficiency, and support data-informed decision-making. AI-powered tools can assist students in developing critical skills, accessing educational resources, and receiving tailored academic support that aligns with their individual learning needs. At the same time, the widespread integration of AI in higher education raises important concerns related to academic integrity, data privacy, algorithmic bias, ethical governance, and the potential overreliance on automated systems. These challenges require institutions to establish appropriate policies and frameworks that ensure the responsible and equitable use of AI technologies. Furthermore, AI adoption has significant implications for student outcomes, influencing academic performance, engagement, learning satisfaction, digital literacy, and future employability. This article examines the opportunities, risks, and student outcomes associated with AI adoption in higher education. It explores how educational institutions can maximize the benefits of AI while addressing emerging challenges to create inclusive, effective, and sustainable learning environments. The discussion highlights the importance of balancing technological innovation with ethical considerations to ensure that AI contributes positively to the future of higher education.
Artificial Intelligence, Higher Education, Educational Technology, Student Outcomes, Personalized Learning, Academic Integrity, Digital Transformation, AI Adoption, Learning Analytics, Educational Innovation.
AI-Powered Digital Transformation in Next-Generation Banking Systems
The rapid evolution of financial technologies has significantly transformed traditional banking systems into intelligent, data-driven ecosystems. This research explores the role of Artificial Intelligence (AI) in enabling digital transformation within next-generation banking systems. The study focuses on how AI technologies such as machine learning, natural language processing, predictive analytics, and intelligent automation enhance operational efficiency, customer experience, fraud detection, and risk management in modern banking environments. The objective of this paper is to analyze the integration of AI within digital banking infrastructures and to propose a conceptual framework that supports scalable, secure, and real-time financial services. A qualitative research approach is adopted, supported by secondary data from recent literature, industry reports, and case studies of leading financial institutions adopting AI-driven solutions. The findings indicate that AI-powered banking systems significantly improve decision-making accuracy, reduce operational costs, and enhance personalized financial services through real-time data analytics. Additionally, AI strengthens cybersecurity mechanisms by detecting anomalous transactions and preventing fraudulent activities more effectively than traditional rule-based systems. However, challenges such as data privacy concerns, algorithmic bias, regulatory compliance, and high implementation costs remain critical barriers to widespread adoption. The study concludes that AI is a fundamental enabler of next-generation banking transformation, offering scalable solutions for intelligent automation and customer-centric services. Future developments are expected to integrate AI with blockchain and cloud computing to create more secure, transparent, and autonomous banking ecosystems.
AI in Banking, Digital Transformation, FinTech, Machine Learning, Blockchain, Risk Management, Intelligent Banking Systems
Zero Trust Security Model for API-Driven Financial Microservices
Financial microservices architecture has become a core component of modern digital banking and fintech systems due to its scalability, flexibility, and ability to support API-driven services. APIs enable seamless communication between distributed financial services such as payments, authentication, fraud detection, and account management. However, this API-centric environment introduces significant security challenges, including unauthorized access, token misuse, data leakage, and increased attack surfaces across microservices. To address these issues, this study proposes a Zero Trust Security Model (ZTSM) for API-driven financial microservices. The model follows a “never trust, always verify” approach, where every API request is continuously authenticated, authorized, and validated regardless of its origin. It integrates identity-based access control, multi-factor authentication, API gateway enforcement, and continuous monitoring to strengthen security across service interactions. The proposed approach enhances security by ensuring strict verification at every communication layer, thereby reducing the risk of internal and external attacks. Key benefits include improved access control, minimized unauthorized API usage, enhanced visibility of system activity, and strengthened protection of sensitive financial data. Overall, the Zero Trust-based approach significantly improves the resilience of financial microservices against modern cyber threats and provides a robust framework for secure API-driven financial ecosystems.
Zero Trust, Microservices, API Security, Financial Systems, Authentication, Cybersecurity
National Economic Impact Measurement of Cybersecurity Failures in U.S. Financial Technology Platforms
The rapid proliferation of Financial Technology (FinTech) platforms in the United States has fundamentally reshaped domestic financial services, offering unprecedented efficiency and accessibility. However, this digital transformation has concurrently expanded the national cyber threat surface, making systemic cybersecurity failures a critical source of macroeconomic vulnerability. This paper addresses a significant gap in existing literature: the absence of a standardized, scalable framework for measuring the national economic consequences of cyber failures specifically within the interconnected FinTech ecosystem. While micro-level breach costs (e.g., remediation, fines) are well-documented, the cascading, economy-wide effects-including liquidity freezes, consumer confidence shocks, and cross-platform contagion-remain largely unquantified. This study proposes a hybrid measurement method integrating econometric input-output modeling with agent-based simulation of the U.S. payment and lending infrastructure. Using publicly reported cybersecurity incidents from 2019–2024 across major U.S. FinTech subsectors (digital wallets, neobanks, and alternative lending platforms), we construct a national “cyber-failure shock” variable. Our model quantifies direct losses (theft, system downtime, regulatory penalties) and, crucially, indirect losses: supply chain disruptions to traditional banks, velocity of money reductions, and depreciation of digital transactional trust. Preliminary findings suggest that a severe, multi-day operational failure affecting just three dominant paymentsFinTechs could generate indirect national losses exceeding direct costs by a factor of 4:1, primarily through halted small business cash flows and transient liquidity hoarding. Furthermore, we name a non-linear relationship between failure frequency, and long-term productivity-persistent but minor failures impose hidden drag via increased compliance friction and risk-premium inflation on FinTech equities. The study also critiques current disclosure regimes (e.g., SEC 8-K filings) for lacking granularity necessary for macroeconomic time-series analysis. The paper concludes by proposing a National Cyber-Economic Impact Index (NCEI) for FinTech platforms, recommending mandatory reporting of operational duration and transaction volume loss. Finally, we discuss policy implications for the Financial Stability Oversight Council (FSOC), emphasizing that cybersecurity failures should be recategorized not merely as technical or firm-specific risks, but as measurable systemic risks to national economic output.
Artificial Intelligence–Driven Financial Risk Assessment and Investment Decision Support Systems in Modern Banking
Artificial Intelligence (AI) has emerged as a revolutionary technology in the banking and financial services sector, enabling organizations to enhance operational efficiency, risk management, and investment decision-making processes. Modern financial institutions increasingly utilize AI-driven techniques such as machine learning, predictive analytics, and data mining to assess financial risks, detect fraudulent activities, evaluate customer creditworthiness, and optimize investment strategies. This study explores the applications of AI in financial risk assessment and investment decision support systems within the banking industry. The research examines how AI-based models improve the accuracy and speed of financial analysis by processing large volumes of structured and unstructured data. Furthermore, the study discusses the benefits of AI adoption, including enhanced decision-making, reduced operational risks, improved customer services, and cost efficiency. It also highlights key challenges such as data privacy concerns, algorithmic bias, regulatory compliance, and implementation costs. The findings indicate that AI-driven systems significantly contribute to more reliable risk assessment and informed investment decisions, thereby strengthening the competitiveness and sustainability of modern banks. The study concludes that the continued integration of AI technologies will play a crucial role in shaping the future of intelligent banking and financial management
A Risk-Driven Framework for Integrating Risk Management into the Software Development Lifecycle
Software systems are increasingly complex, distributed, and exposed to evolving security, operational, and requirement-related uncertainties. Traditional software development methodologies often treat risk management as a supplementary activity rather than an integrated process. This study proposes a risk-driven framework that embeds risk management directly into the software development lifecycle to improve system reliability, quality assurance, and decision-making efficiency. The framework integrates continuous risk identification, assessment, prioritization, and mitigation across all phases of development, from requirements engineering to maintenance. Building on established work by Hijazi et al. (2014), who emphasized structured integration of risk management into software processes, this research extends the concept by incorporating adaptive risk evaluation mechanisms aligned with modern iterative development models. The study demonstrates that embedding risk-aware practices enhances early defect detection, reduces project failure probability, and improves overall software resilience.
Artificial Intelligence and Semantic Knowledge Representation for Optimized Web Service Registry Systems
The rapid expansion of distributed computing and service-oriented architectures has intensified the need for efficient web service discovery and selection mechanisms. Traditional keyword-based service registries often suffer from ambiguity, low precision, and poor scalability in dynamic environments. This paper presents an integrated approach combining Artificial Intelligence techniques with Semantic Knowledge Representation to optimize web service registry filtering systems. By leveraging ontology-driven frameworks and machine learning-enhanced semantic reasoning, the proposed model improves service matching accuracy, reduces search complexity, and enhances adaptability in heterogeneous service environments. The study builds upon foundational work in semantic-based service filtering mechanisms and extends it by incorporating intelligent decision-making capabilities. Experimental analysis demonstrates that the hybrid approach significantly improves service retrieval performance compared to conventional registry systems. The findings highlight the potential of AI-enabled semantic frameworks in advancing intelligent service-oriented computing.
Explainable and Trustworthy Artificial Intelligence in Endodontics: Combining Clinical Text Analysis, Machine Learning, and CBCT Imaging
Artificial intelligence (AI) is rapidly transforming healthcare by improving diagnostic accuracy, treatment planning, and clinical decision-making. In endodontics, the integration of explainable and trustworthy AI systems with cone-beam computed tomography (CBCT) imaging and clinical text analysis offers significant potential for enhancing dental diagnosis and patient care. This study explores the development of an explainable AI framework that combines machine learning algorithms, natural language processing, and CBCT imaging for intelligent endodontic analysis. The framework utilizes clinical text records, radiographic imaging, and AI-driven classification techniques to improve lesion detection, root canal assessment, and treatment planning accuracy. Explainable AI mechanisms are incorporated to ensure transparency, interpretability, and clinical trustworthiness in diagnostic predictions. Experimental analysis demonstrates improved diagnostic precision, enhanced image interpretation, and more reliable treatment recommendations compared to traditional diagnostic approaches. The integration of CBCT imaging with machine learning-assisted clinical text analysis also improves decision support and reduces diagnostic uncertainty. Furthermore, the framework supports safer deployment in clinical environments through validation-driven AI methodologies. The findings demonstrate that explainable and trustworthy AI systems can significantly improve endodontic diagnostics, workflow efficiency, and intelligent dental healthcare delivery.
Fei-Fei Li, Cynthia Rudin, Hugh Durrant-Whyte
Design and Optimization of Logarithmic Slot Antennas for High-Frequency Medical Imaging and Wireless Healthcare Systems: A Test-Driven AI Framework for Performance Enhancement and Reliable Deployment
The advancement of biomedical imaging and wireless healthcare technologies has created a growing demand for compact, efficient, and high-gain antenna systems capable of operating effectively in complex biological environments. This study presents the design and optimization of substrate integrated waveguide (SIW)-based logarithmic slot antennas for high-frequency medical imaging and intelligent healthcare systems. The proposed antenna utilizes logarithmic slot geometry to achieve broadband operation, enhanced impedance matching, stable radiation characteristics, and improved signal transmission across microwave and millimeter-wave frequency bands. Electromagnetic simulations and experimental evaluations demonstrate superior gain, reduced signal attenuation, enhanced bandwidth, and improved imaging resolution compared to conventional antenna structures. The research also investigates the compatibility of the antenna with wearable and implantable healthcare devices used in modern smart healthcare systems. In addition, a test-driven artificial intelligence framework is incorporated into the optimization process to improve design validation, parameter tuning, and deployment reliability. Machine learning-assisted optimization significantly reduces design iterations while improving consistency between simulated and experimental results. Furthermore, the study highlights the relevance of advanced medical imaging technologies such as EEG-based image classification and cone-beam computed tomography in supporting intelligent healthcare systems. The results confirm that SIW-based logarithmic slot antennas provide an effective solution for next-generation biomedical imaging and wireless healthcare communication systems.
Artificial Intelligence-Based Clinical Decision Support Systems in Endodontics: Integrating Machine Learning Signal Processing, EEG-Based Pattern Recognition, and CBCT Imaging Analysis.
Clinical Decision Support Systems based on artificial intelligence have become revolutionary tools in contemporary endodontics, allowing increased diagnostic accuracy, predictive analytics, and individualized treatment planning. This paper discusses the combination of machine learning signal processing, EEG-based pattern recognition, and cone-beam computed tomography imaging in a single AI-driven clinical decision-making framework in endodontic practice. The study develops a multi-modal framework that utilizes deep learning algorithms to understand complex imaging data and signal patterns to enhance the accuracy of diagnoses and treatment outcomes. Evaluation of the system is based on various performance metrics such as accuracy, precision, recall, and F1-score, and it is shown that the system performs significantly better than traditional diagnostic processes. The authors highlight the potential of integrating neuro-signal analysis with high-quality imaging to develop intelligent, adaptive, and reliable clinical support systems. This paper contributes to the literature on AI-focused healthcare by offering a scalable and interpretable model to improve clinical procedures, reduce diagnostic errors, and promote evidence-based decision-making in endodontics
Rajesh P. N. Rao, Niels Birbaumer, Dimitrios K. Iakovidis
AI-Driven Predictive Analytics Framework for Real-Time Decision Support Systems
A real-time decision-making process has turned out to be a pressing need of contemporary sectors in the healthcare, finance, manufacturing, and smarter cities. The current paper introduces an AI-based predictive analytics model that will be used to process large amounts of data in real-time, produce insights that can be acted upon, and help make immediate decisions. The framework unites machine learning, deep learning, streaming architectures, feature engineering pipeline, and model optimization plans to increase the accuracy of prediction, response time, and reliability. The paper examines the current literature and different research gaps and offers a scalable multi-layer architecture based on data ingestion, real-time analytics, and adaptive learning. The experimental tests show that the framework is able to generate the result with high accuracy at the same time has low latency when the workload is adjusted. Its practical constraints are reliance on high quality continuous streams of data, computationally expensive hardware requirements and model drift due to changes in dynamic environments. The future trends are to combine self-learning hybrid AI models, create lightweight edge minimized models, and increase the interpretability to address high-risk areas of application.