Artificial Intelligence Research Articles

Leveraging Artificial Intelligence in Linguistics: Innovations in Language Acquisition and Analysis

Leveraging Artificial Intelligence in Linguistics: Innovations in Language Acquisition and Analysis

Impact of Artificial Intelligence on Islamic Education: Effectiveness, Innovation, and Socio Cultural Influence

Objective: This study discusses the role of AI in Islamic education, from the aspects of effectiveness, innovation in learning, and socio-culture. This study includes how artificial intelligence allows better learning for the students, innovative teaching methods, and social and cultural change in Islamic education.Methods: This research is a meta-analysis that aims to synthesize data from several studies related to the application of AI in Islamic education. Cohen’s d was used to compute the effect size quantifying the effect of AI on learning deviations learning effectiveness, learning innovation, and learning socio-culture.Results: The results show that AI has moderate but significant impact on Islamic education. The most influential factors are socio-cultural factors (the influences of social factors) and recent educational innovative reforms (innovation), followed by learning effectiveness (focusing on adaptive learning and the connectedness of the learning environment). The findings provide evidence that the incorporation of AI is beneficial for enhancing learning outcomes and facilitating cultural and social transformation in the context of Islamic education.Novelty: This research sheds light on the effects that AI can have on Islamic education, and presents a unique take on the issue, focusing on improving learning outcomes, and capturing innovation in the socio-cultural context. It also calls for more research into how external forces access to technology, teacher involvement, community buy-in can shape this work.Theory and Policy Implications: The results affirm the importance of integrating AI into Islamic education and recommend that policymakers focus on AI-based projects that align with local cultural contexts and technological capacity. It is recommended in the study to customise the operation of AI-based learning solutions according to general Islamic educational values and the needs of students. Additional studies are needed to capture the broader ramifications of AI on educational policies and practices in different socio-cultural contexts.

Key drivers for the incorporation of artificial intelligence in humanitarian supply chain management

Key drivers for the incorporation of artificial intelligence in humanitarian supply chain management

Cross-Jurisdictional data privacy compliance in the U.S.: developing a new model for managing AI data across state and federal laws

The fragmented landscape of data privacy laws in the United States poses significant challenges for organizations utilizing artificial intelligence (AI) systems that process sensitive and large-scale data. Variations in state laws and the absence of a comprehensive federal framework exacerbate compliance complexities, limiting AI innovation and creating legal uncertainties. This paper proposes a conceptual model to harmonize privacy compliance across U.S. jurisdictions, integrating key interoperability principles, consistency, transparency, and scalability. The framework emphasizes standardized practices for data classification, consent management, risk assessment, and enforcement mechanisms supported by technological enablers such as privacy-enhancing technologies and AI compliance tools. Through case studies in healthcare, e-commerce, and finance, the paper demonstrates the framework’s practical application and effectiveness in resolving multi-jurisdictional compliance challenges. Actionable recommendations for policymakers, organizations, and AI developers are provided to facilitate implementation alongside future research directions to refine the model and address emerging privacy risks. This study offers a roadmap for navigating the complexities of U.S. privacy laws, promoting trust, accountability, and responsible AI innovation. Keywords: AI data governance, U.S. privacy laws, Cross-jurisdictional compliance, Privacy-enhancing technologies, Data protection framework, Ethical AI.

AI and data-driven insights: Transforming customer relationship management (CRM) in financial services

Artificial Intelligence (AI) and data-driven insights are revolutionizing Customer Relationship Management (CRM) in the financial services sector by enhancing customer engagement, streamlining operations, and enabling personalized experiences. By integrating advanced AI technologies such as machine learning, natural language processing (NLP), and predictive analytics, CRM systems can analyze vast amounts of customer data to uncover actionable insights, predict behaviors, and deliver tailored solutions. This transformation helps financial institutions build stronger relationships with customers while improving efficiency and competitiveness in a rapidly evolving market. AI-driven CRM systems provide financial institutions with tools to anticipate customer needs, segment audiences, and automate routine processes. Predictive analytics allows organizations to identify potential opportunities and risks, optimize marketing campaigns, and enhance customer retention. Natural language processing powers chatbots and virtual assistants, enabling real-time, personalized customer support while reducing operational costs. Additionally, data visualization and advanced reporting features enhance decision-making by offering clear and actionable insights to stakeholders. The adoption of AI and data-driven CRM solutions presents significant benefits, including increased customer satisfaction, enhanced loyalty, and improved operational efficiency. However, challenges such as data security concerns, regulatory compliance, and the complexity of integrating AI with existing systems remain critical barriers. Financial institutions must also address ethical considerations, such as ensuring transparency in AI decision-making and avoiding biases in customer interactions. This paper explores the role of AI and data-driven insights in transforming CRM within financial services, highlighting their applications, benefits, and challenges. It also examines successful case studies to provide actionable strategies for effective implementation. By leveraging AI and data-driven insights, financial institutions can revolutionize customer relationship management, drive sustainable growth, and remain resilient in an increasingly digital economy. Keywords: Artificial Intelligence, Data-Driven Insights, Customer Relationship Management, Financial Services, Predictive Analytics, Machine Learning, Natural Language Processing, Customer Engagement, Personalized Experiences, CRM Transformation.

A Cybersecurity framework for fraud detection in financial systems using AI and Microservices

In this review, we propose a cybersecurity framework aimed at enhancing fraud detection in financial systems by leveraging artificial intelligence (AI), microservices, and RESTful architectures. With the increasing sophistication of cyber threats targeting financial institutions, traditional security methods often fall short in providing comprehensive protection. This review outlines how AI and microservices can be integrated to secure sensitive financial data and improve fraud detection. The framework employs AI-driven models for real-time anomaly detection, enabling systems to quickly identify suspicious activities and predict fraud patterns before they escalate. Microservices architecture, built using technologies such as Java Spring Boot, enables scalability, flexibility, and enhanced communication between modular components through secure RESTful APIs. Angular is utilized for building secure user interfaces, ensuring data protection across front-end applications. Additionally, the integration of security testing platforms such as SonarQube and Blackduck plays a critical role in continuously monitoring and inspecting code for vulnerabilities, ensuring that any flaws in the system are promptly addressed. This comprehensive approach not only safeguards financial institutions from potential fraud but also strengthens the U.S. financial infrastructure, contributing to the nation’s defense against cyber threats. By leveraging cutting-edge technologies and best practices, this framework offers a scalable, secure, and adaptive solution for the evolving challenges in cybersecurity and fraud prevention within the financial sector. The proposed framework enhances operational efficiency while mitigating risks, making it a valuable addition to modern cybersecurity strategies Keywords: Cybersecurity Framework, Fraud Detection, Financial Systems, Microservices, Artificial intelligence.

The role of AI in U.S. consumer privacy: Developing new concepts for CCPA and GLBA compliance in smart services

The rapid adoption of artificial intelligence (AI) in U.S. consumer services has transformed customer interactions, operational efficiency, and service delivery. However, this technological shift presents complex challenges in maintaining compliance with data privacy regulations, such as the California Consumer Privacy Act (CCPA) and the Gramm-Leach-Bliley Act (GLBA). This paper explores the role of AI in enhancing smart services while safeguarding consumer privacy, highlighting key risks, compliance challenges, and regulatory gaps. A conceptual model is proposed to guide organizations in integrating privacy-by-design strategies, emphasizing transparency, consent management, and ethical AI principles. The paper also discusses emerging technologies and best practices that support privacy protection while leveraging AI-driven insights. Collaborative efforts between regulators and technology providers are recommended to foster innovation while ensuring robust data privacy. The findings provide practical strategies for balancing technological advancement with regulatory compliance, offering insights for policymakers, industry stakeholders, and service providers. Keywords: Artificial Intelligence, Consumer Privacy, Data Protection, Compliance Strategies, Privacy Regulations, Smart Services.

Agile tax technology development in the U.S.: A conceptual framework for scalable and efficient enterprise solutions

The rapid evolution of tax regulations in the United States, coupled with increasing digital transformation in financial compliance, necessitates the development of agile, scalable, and efficient tax technology solutions. Traditional tax management systems, often rigid and monolithic, struggle to adapt to dynamic regulatory changes, increasing enterprise demand for flexible, technology-driven approaches. This review explores Agile Tax Technology Development as a conceptual framework that integrates agile methodologies with advanced digital solutions to enhance efficiency, scalability, and compliance in enterprise tax management. Agile methodologies, such as Scrum, Kanban, and SAFe, provide iterative and adaptive software development models that allow tax technology teams to rapidly respond to regulatory updates and evolving business needs. The review examines key components of agile tax technology, including cloud-based infrastructure, API-driven architectures, automation, and artificial intelligence, which collectively enable real-time tax processing, predictive analytics, and seamless integration with financial systems. Additionally, it discusses the role of cross-functional collaboration among tax professionals, software developers, and regulatory experts to ensure compliance and continuous improvement. Scalability is a critical factor in enterprise tax solutions, requiring cloud computing, microservices architecture, and distributed ledger technologies to efficiently process vast amounts of tax data. This study highlights best practices in Agile Tax Technology Development by analyzing case studies from leading enterprises and government initiatives. Furthermore, it addresses challenges such as integrating agile frameworks into legacy tax systems, ensuring cybersecurity in financial data processing, and overcoming organizational resistance to agile adoption. Ultimately, this review provides a forward-looking perspective on the future of tax technology in the U.S., emphasizing the need for continuous innovation, automation, and agility in response to an increasingly complex regulatory landscape. The findings serve as a guideline for enterprises seeking to modernize their tax compliance strategies while maintaining operational efficiency and regulatory adherence. Keywords: Agile Tax Technology, U.S, Efficient Enterprise Conceptual Framework.

Sustainable AI Applied to Project Management: A Literature Review

Integrating sustainable practices with emerging technologies, such as Artificial Intelligence, has proven crucial to achieving new sustainable development goals. In the industrial context, project management is important in promoting sustainability. This work aims to review the literature on applying sustainable Artificial Intelligence in project management, focusing on optimizing resources and aligning with the SDGs. As a result, the research highlighted the good performance of the technological transformation of project management, combined with sustainability concepts, as an agent capable of generating the necessary transformations to guarantee a more sustainable future.

القيادة الريادية مدخل لتحقيق التميز المؤسسي في الجامعات السعودية: دراسة تحليلية

The research aims to study the role of entrepreneurial leadership in achieving institutional excellence in Saudi universities, considering the rapid challenges and changes in the higher education sector. It also seeks to analyze the suitability of entrepreneurial leadership for Saudi universities and its impact on institutional performance, focusing on its key elements such as creativity, initiative, and calculated risk-taking. The study adopted a descriptive-analytical approach, analyzing previous literature on entrepreneurial leadership, examining global university experiences, and exploring entrepreneurial leadership practices in some Saudi universities. Additionally, it analyzed institutional excellence standards, including effective leadership, strategic planning, and strengthening community partnerships. The findings indicate that the implementation of entrepreneurial leadership in Saudi universities is still in its early stages. However, efforts are being made to enhance it through various initiatives and development programs. The study also identified key challenges hindering the adoption of entrepreneurial leadership, including insufficient funding, institutional resistance to change, a lack of trained leadership competencies, and unclear policies supporting entrepreneurship. The recommendations emphasize establishing specialized units within universities to support entrepreneurial leadership, developing training programs for academic leaders to enable them to adopt entrepreneurial strategies, enhancing creativity, initiative, and calculated risk-taking by establishing specialized centers within universities to support entrepreneurial ideas and finance academic and research projects focused on innovation and development, and integrating technology and artificial intelligence into entrepreneurial leadership practices to enhance institutional performance efficiency and achieve global leadership.

Artificial Intelligence and Medicine

Artificial Intelligence and Medicine

Innovative cybersecurity strategies for business intelligence: Transforming data protection and driving competitive superiority

This research paper explores innovative cybersecurity strategies for business intelligence (BI), emphasizing their role in transforming data protection and driving competitive superiority. It examines advanced encryption techniques, AI and machine learning, and blockchain technology, highlighting how these strategies enhance data security, operational efficiency, and cost savings. The paper also discusses emerging trends in BI cybersecurity, including AI-driven threat detection, zero-trust architecture, and blockchain advancements, and identifies areas for further research. The findings underscore the importance of robust cybersecurity measures in maintaining a competitive edge and ensuring the integrity and security of BI systems. Keywords: Business Intelligence, Cybersecurity, Data Protection, Artificial Intelligence, Blockchain Technology.

Deep Learning Architectures for Waste Detection: A Systematic Literature Review

Increasing population growth contributes to the increasing amount of waste generated, creating problems in its management. One possible alternative is the usage of deep learning-based artificial intelligence technology, which can be applied for automated waste identification and classification. This research follows Systematic Literature Review (SLR) with the aim of evaluating the performance of various deep learning architectures in waste detection. Out of 547 articles identified, 20 were synthesized with the use of selective criteria. The synthesis of the evidence shows that architectures like YOLO, Faster R-CNN, and EfficientNet have been proven to be effective in the various datasets for waste detection. Furthermore, certain other models, such as ResNet-50B and DenseNet121, performed very well in image classification with accuracy values above 90%. In this research, several challenges were identified, including high computing power and computational magnitude. For future work, the optimization of models as well as the use of standard datasets such as TrashNet and TACO are recommended research directions that might help in developing an effective, efficient, and sustainable waste sorting system.

A Review of the Application and Prospect of Medical Diagnosis System in the Context of Artificial Intelligence

Artificial intelligence (AI) has become a major force for change in the healthcare industry, especially in diagnostic systems, where AI plays a key role in improving the accuracy, speed, and efficiency of diagnosis. The deep integration of AI with internet-based platforms has revolutionized the way healthcare is delivered, especially in low-resource settings, where AI provides a scalable solution for healthcare systems. This article explores the application of artificial intelligence in medical diagnosis, focusing on how AI technologies such as machine learning and deep learning can be integrated into areas such as medical imaging, disease prediction, and telemedicine. The article also discusses the rapid development of the internet-based healthcare system, emphasizing the role of AI in improving diagnostic models through real-time data collection and analysis. At the same time, the article also analyzes current challenges, such as data privacy, ethical issues, and regulatory challenges, which limit the widespread application of AI in clinical practice. Through a comprehensive review of existing research, this article outlines the potential and limitations of AI in medical diagnostics and provides insights into its future development trends. The findings show that although AI has great potential to improve the quality and accessibility of healthcare, its application in clinical practice still needs to carefully consider many factors such as ethics, technology, and regulation.

Postmodern AI: A New Vision of Man's Relationship with Technology

This research explores the impact of postmodern artificial intelligence (AI) on philosophy, arts, politics, and ethics. It aims to analyze how AI reflects postmodern thought, focusing on concepts like relativism and multiplicity. The study also investigates AI's potential effects in various fields, including creativity, decision-making, and societal norms. The methodology includes a critical review of existing literature and case studies on AI applications. Key findings reveal that AI in the postmodern era challenges traditional notions of truth, authority, and creativity, while raising ethical concerns about bias, privacy, and job displacement. The research calls for clear ethical frameworks, education initiatives, and human-AI collaboration to address these challenges and ensure a balanced future between technology and humanity.

Whose [Crime] is it Anyway?

Abstract The concurrent increase in states’ cyber-capabilities and in the potential (mis)uses of artificial intelligence (AI) to further harmful state objectives raise the question: is ICL prepared to grapple with AI-powered cyberattacks conducted by and against states? Or does AI’s conduct run the risk of falling into a legal loophole, shielding from liability the human actors who commissioned its use? Addressing these questions, this article analyses how AI impacts ICL’s fundamental elements of international crimes — namely, the requirements of actus reus and mens rea. Specifically examining the literature’s more recently explored notion of the crime of ‘cyber-aggression’, this article scrutinizes the efficacy of the current text and interpretation of the Rome Statute in according international criminal responsibility where AI causes an act of cyber-aggression. It examines the spectrum on which potential acts of cyber-aggression may be fairly ‘reduced to human activity’, and thus where criminal responsibility may flow to the AI’s human instructor, versus where the act committed was perhaps too removed from human intervention to justify criminal responsibility under the applicable culpable mental state. In this article, the author argues the elements for criminal responsibility of an act of aggression, in their current construction and interpretation, are ill-prepared when faced with the murky implications of cyber-aggression conducted via state-backed AI systems. In order to engage in such a discussion, this article proceeds by providing an overview of the crime of aggression and introducing the emerging concept of ‘cyber-aggression’. Then, the article explores the challenges AI-powered acts of aggression present for the conventional elements of international criminal responsibility. Finally, the article details solutions posed by the literature and proposes an additional approach, before offering concluding remarks.

The effects of an AI feedback coach on students’ peer feedback quality, composition, and feedback experience

This study examines the integration of an Artificial Intelligence (AI) feedback coach in a peer feedback activity. Participants provided peers with feedback on their assignments. While providing feedback, they either received real-time adaptive AI coaching (intervention group) or not (control group). Feedback comments from participants were analyzed concerning content, text complexity, and sentiment. Survey responses were coded for sentiment and themes. Results show adverse effects of the AI feedback coach. Intervention group participants’ feedback included fewer reflective questions and adhered less to criteria. They provided shorter, more complex feedback. Students indicated mixed views on the AI feedback coach, with some finding it helpful and others distracting. A notable subset of students stated overreliance on the AI coach, prioritizing its validation over their own judgment. Results suggest that AI tools need thoughtful integration, possibly with additional scaffolding to counteract overreliance and avoid negative impact on peer feedback quality and feedback experience.

Analysis of Key Factors Shaping the Performance and Reliability of AI Applications

Artificial Intelligence (AI) technologies have revolutionized a wide range of industries by providing cutting-edge solutions that streamline operations, improve decision-making accuracy, and deliver highly personalized user experiences. From automating routine tasks in manufacturing and logistics to enabling advanced data analysis in healthcare and finance, AI has become a critical tool for enhancing productivity and optimizing outcomes.This paper examines three critical AI applicationsautonomous driving, natural language processing (NLP), and facial recognitionto analyze the factors influencing their performance and reliability. The study identifies data quality, algorithm optimization, and deployment environment as pivotal elements that determine the effectiveness and fairness of these systems. Through a comparative analysis, this paper highlights how challenges such as data diversity, algorithmic bias, and environmental constraints impact system outcomes. It also explores strategies for improving accuracy, adaptability, and fairness in real-world settings. Given the rapid evolution of AI, the study emphasizes the importance of continuous innovation and incorporating user feedback into system design. Future research directions include analyzing the adaptive capabilities of AI systems and developing methods for better integrating user insights, ensuring AI's sustained advancement in addressing complex societal needs.

Transforming Marketing Creative Capacities: The Role of Artificial Intelligence in Minimizing Creative Constraints

Artificial intelligence (AI) is poised to transform the way marketing activities are conducted and, as such, how educators prepare students for the ever-changing marketing landscape. As AI becomes increasingly integrated into marketing practices, it is crucial to examine its potential impact on marketing education. This research investigates the use of AI in a marketing Promotions Strategy course, a key component of marketing curriculum and one that poses challenges to students who struggle with creativity. Drawing on literature concerning creative anxiety, this research empirically examines how AI can shape students’ perceptions, experiences, and outcomes when faced with projects that demand creativity. The results of this study suggest that AI can serve as a valuable tool in assisting students to overcome the obstacles and challenges often stemming from a deficiency in creative expertise and proficiency in creative technologies.

Research on the Impact of Artificial Intelligence on Enterprise Production Management

This study investigates the application of Artificial Intelligence (AI) in business management, assessing its contributions to decision-making, operational efficiency, and customer service. It highlights both the opportunities and challenges presented by AI, with an analysis of case studies from companies such as Airbnb and Nordstrom to illustrate AIs influence on business processes and innovation. The research concludes that AI confers substantial benefits, including cost savings and data-informed decision-making, while also identifying critical issues such as labor market disruptions and data security that necessitate attention. The adoption of AI is contingent upon a companys size and resources, necessitating bespoke strategies for both large enterprises and small and medium-sized enterprises (SMEs).