Artificial Intelligence Use in Academic Applicant Screening: A Systematic Review

This research explores the integration and impact of artificial intelligence (AI) within Emirati media organizations, focusing on adoption determinants, obstacles, and interventions. Utilizing a mixed-methods approach, the study incorporates qualitative data from 21 in-depth interviews with media professionals and a thematic analysis of 30 scholarly articles published between 2019 and 2024. The research aims to provide a nuanced understanding of AI's transformative effects on the UAE media landscape. Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations. AI technologies such as machine learning, natural language processing, and predictive analytics are being leveraged to improve operational efficiency, streamline workflows, and deliver personalized content to audiences. These advancements are driving competitive advantages and elevating the overall quality of media output. Despite these benefits, several challenges hinder widespread AI adoption. Technological barriers, including limited access to advanced AI tools and infrastructure, pose significant obstacles. Additionally, there is a pronounced shortage of skilled personnel capable of implementing and managing AI systems, which exacerbates concerns over job displacement and workforce anxiety. Ethical considerations, such as ensuring unbiased AI algorithms and protecting user privacy, are also critical issues that require attention. The study underscores the importance of continuous training and upskilling for media professionals to adapt to the evolving technological landscape. It highlights the role of robust ethical guidelines and governance frameworks in promoting responsible AI use. Government support, through targeted policies, funding initiatives, and incentives, is identified as a crucial factor in facilitating AI integration and addressing adoption barriers. The research concludes by providing strategic recommendations for media organizations and policymakers. It advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders. By addressing these areas, Emirati media organizations can harness the full potential of AI to drive innovation, improve audience engagement, and maintain competitiveness in a rapidly digitizing global media environment. This study contributes to the growing body of knowledge on AI in media, offering valuable insights into the specific context of the UAE. It provides a foundation for future research on AI's role in media and serves as a guide for organizations seeking to navigate the complexities of AI adoption.

Objective. This paper discusses the capabilities and implications of Artificial Intelligence (AI) in Nigerian academic libraries. The study emphasizes the importance of libraries using new technologies to improve their operations and services, especially in developing countries like Nigeria. Methods. The research conducted a literature review to examine the capabilities of AI in libraries and its impact on academic libraries. Results. Various AI tools such as natural language recognition, robotics, big data, and machine learning were identified. AI can revolutionize library services, improve information quality, increase productivity, and provide virtual assistance. However, there are challenges to the adoption of AI in Nigerian academic libraries, including high costs, resistance to change, poor network connectivity, privacy and ethical implications, and a lack of supportive cultures. To fully exploit the benefits of AI, libraries must develop plans and policies, train librarians with the necessary skills, and address the challenges associated with AI adoption. Conclusions. AI holds great advantages to enhancing library services in Nigeria, but careful planning and preparation are needed.

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts.Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information.However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations.The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

While artificial intelligence (AI) offers transformative benefits for businesses, the road to successful adoption is often filled with technical, financial, and cultural hurdles. This chapter explores the most common barriers to AI integration and provides strategic solutions to overcome them, ensuring that businesses—especially small enterprises—can effectively harness AI’s potential. The chapter first examines technical difficulties, including data management issues, AI system integration challenges, and a lack of in-house expertise. It outlines practical solutions, such as data enrichment, phased AI implementation, and workforce upskilling, to help businesses navigate these complexities. Financial constraints, such as high initial costs, uncertain return on investment, and ongoing maintenance expenses, are also analyzed. The chapter provides cost-effective AI adoption strategies, including scalable AI solutions, leveraging cloud-based AI services, and securing grants or partnerships to mitigate financial risks. Additionally, the chapter delves into cultural and organizational resistance, addressing employee concerns, leadership alignment, and the need for AI literacy. Strategies for fostering an AI-ready culture include transparent communication, structured training programs, and leadership-driven AI advocacy. Lastly, ethical considerations—such as AI bias, transparency, data privacy, and regulatory compliance—are discussed to ensure responsible AI adoption. By the end of this chapter, business owners and entrepreneurs will be equipped with actionable insights to overcome AI adoption challenges and drive ethical, efficient, and sustainable AI integration in their organizations.

Artificial intelligence (AI) is transforming the mental health care environment. AI tools are increasingly accessed by clients and service users. Mental health professionals must be prepared not only to use AI but also to have conversations about it when delivering care. Despite the potential for AI to enable more efficient and reliable and higher-quality care delivery, there is a persistent gap among mental health professionals in the adoption of AI. A needs assessment was conducted among mental health professionals to (1) understand the learning needs of the workforce and their attitudes toward AI and (2) inform the development of AI education curricula and knowledge translation products. A qualitative descriptive approach was taken to explore the needs of mental health professionals regarding their adoption of AI through semistructured interviews. To reach maximum variation sampling, mental health professionals (eg, psychiatrists, mental health nurses, educators, scientists, and social workers) in various settings across Ontario (eg, urban and rural, public and private sector, and clinical and research) were recruited. A total of 20 individuals were recruited. Participants included practitioners (9/20, 45% social workers and 1/20, 5% mental health nurses), educator scientists (5/20, 25% with dual roles as professors/lecturers and researchers), and practitioner scientists (3/20, 15% with dual roles as researchers and psychiatrists and 2/20, 10% with dual roles as researchers and mental health nurses). Four major themes emerged: (1) fostering practice change and building self-efficacy to integrate AI into patient care; (2) promoting system-level change to accelerate the adoption of AI in mental health; (3) addressing the importance of organizational readiness as a catalyst for AI adoption; and (4) ensuring that mental health professionals have the education, knowledge, and skills to harness AI in optimizing patient care. AI technologies are starting to emerge in mental health care. Although many digital tools, web-based services, and mobile apps are designed using AI algorithms, mental health professionals have generally been slower in the adoption of AI. As indicated by this study's findings, the implications are 3-fold. At the individual level, digital professionals must see the value in digitally compassionate tools that retain a humanistic approach to care. For mental health professionals, resistance toward AI adoption must be acknowledged through educational initiatives to raise awareness about the relevance, practicality, and benefits of AI. At the organizational level, digital professionals and leaders must collaborate on governance and funding structures to promote employee buy-in. At the societal level, digital and mental health professionals should collaborate in the creation of formal AI training programs specific to mental health to address knowledge gaps. This study promotes the design of relevant and sustainable education programs to support the adoption of AI within the mental health care sphere.

This study examines the impact of artificial intelligence (AI) adoption on economic growth and unemployment across G7 countries and India. As AI emerges as a transformative technology, there is a need to understand its effects on labour markets and develop appropriate policy frameworks. The research analyses historical patterns of automation and technological change to provide context for the current AI revolution. The study employs a mixed-methods approach, combining quantitative statistical analyses with qualitative assessments. Variables examined include GDP growth rates, R&D investment levels, AI adoption rates, and productivity gains. Descriptive statistics, correlation analyses, regression models, and cluster analyses were conducted to identify relationships between key variables. Results reveal a significant technology adoption gap between high-tech leaders and developing economies. A growth paradox was observed in developing tech economies, where rapid AI adoption did not necessarily translate to proportional economic gains. The research found complex relationships between technological advancement, unemployment rates, and investment levels across countries. Cluster analysis identified three distinct groups: Advanced Economies, Technology Leaders, and Developing Tech Economies. ANOVA and chi-square tests confirmed statistically significant differences between these clusters. Multiple regression analysis on unemployment rates provided insights into the factors influencing job displacement. Key findings include: 1) AI adoption shows potential to significantly boost GDP growth and productivity, though effects vary by country. 2) The relationship between technological progress and unemployment is nuanced, defying simplistic narratives. 3) Investment levels strongly correlate with technological advancement, but other factors also play important roles. 4) Leaders in AI adoption exhibit certain common characteristics, offering potential lessons for other ...

Artificial Intelligence (AI) and Big Data Analytics (BDA) are increasingly recognized as transformative forces that are reshaping industries, governance, and human society in profound ways. AI, with its foundations in machine learning, deep learning, natural language processing, and predictive modelling, requires vast amounts of data to train algorithms and generate reliable insights. Complementing this, BDA provides the scale, diversity, and velocity of data processing required to uncover hidden patterns, correlations, and trends within complex datasets. Together, AI and BDA form a synergistic paradigm in which Big Data serves as the fuel and AI operates as the engine, driving intelligent, data-driven innovation across multiple domains. Globally, AI-BDA applications are enabling predictive diagnostics, telemedicine platforms, genomic-driven personalized treatments, and advanced medical imaging, thereby enhancing both the accessibility and quality of care. In India, where the healthcare system must serve more than 1.4 billion citizens with limited resources, these technologies can play a pivotal role in addressing systemic challenges such as inadequate rural access, shortages of medical professionals, real-time disease surveillance, and hospital resource optimization. International best practices, including the NHS AI Lab in the United Kingdom, Mayo Clinic initiatives in the United States, and Ping a Good Doctor in China, provide models that India can adapt and scale to its unique demographic and infrastructural context. Despite their vast potential, the adoption of AI and BDA faces several barriers. Data privacy and cybersecurity remain critical concerns, particularly with the emergence of new regulations and the sensitive nature of health-related information. Additionally, algorithmic bias and the uneven availability of digital infrastructure could exacerbate existing inequities if not adequately addressed. Looking forward, advancements in Edge AI, Explainable AI (XAI), and quantum-enhanced analytics are expected to extend the scope and reliability of AI-BDA systems. This paper argues that the integration of AI and BDA is not merely a technological advancement but a paradigm shifts in how societies innovate, make decisions, and deliver essential services. Specifically, it highlights the need for a human-centric, ethical, and affordable approach to healthcare innovation in India in order to unlock the full transformative potential of these technologies. Keywords: Artificial Intelligence, Big Data Analytics, Healthcare Innovation, Predictive Diagnostics, Ethical AI, Edge AI.

Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to these challenges. AI enhances data management and analysis by automating data collection, processing, and reporting, thereby increasing accuracy and efficiency. Predictive maintenance and risk assessment tools powered by AI can identify potential compliance issues before they arise, allowing for proactive measures. Moreover, AI-driven compliance monitoring systems enable real-time tracking of regulatory adherence, reducing the risk of non-compliance and associated penalties. Automated auditing and inspection processes further streamline compliance checks, ensuring thorough and consistent evaluations. Case studies demonstrate successful AI implementations in regulatory compliance, such as automated reporting systems in offshore drilling and predictive maintenance in pipeline management, which have resulted in improved compliance rates and reduced operational risks. However, the adoption of AI is not without challenges. Issues related to data quality and integration, cybersecurity, and regulatory acceptance pose significant hurdles. Additionally, ethical and legal considerations surrounding AI deployment must be addressed to ensure responsible use. AI holds substantial potential to revolutionize regulatory compliance in the oil and gas industry by enhancing efficiency, accuracy, and proactive risk management. As AI technologies continue to evolve, their integration into compliance processes will likely become more sophisticated, offering greater benefits and addressing current limitations. The future of regulatory compliance in the oil and gas sector will be increasingly shaped by the advancements in AI, driving both operational excellence and adherence to stringent regulatory standards.

Purpose This study explores how organizational artificial intelligence (AI) adoption influences employee voice behavior through psychological mechanisms. Drawing on conservation of resources theory, we investigate job engagement as a mediator and leader-member exchange (LMX) quality as a moderator in this relationship. Design/methodology/approach We employed a 3-wave time-lagged design with 327 employees from South Korean organizations. Data were collected at six-week intervals: organizational AI adoption and LMX (T1), job engagement (T2) and voice behavior (T3). Moderated mediation analysis tested our hypothesized model. Findings Organizational AI adoption positively relates to voice behavior through enhanced job engagement. This indirect effect is strengthened by high-quality LMX relationships. Employees experiencing high LMX show stronger positive relationships between AI adoption and job engagement, subsequently exhibiting more voice behavior. The moderated mediation index confirms that LMX quality significantly influences how AI adoption affects voice behavior through engagement. Originality/value This study advances understanding of AI's human-side implications beyond productivity metrics. By revealing how technological and relational resources interact to influence discretionary behaviors, we contribute to literature on technology adoption, employee engagement and leadership. The findings demonstrate that AI adoption can enhance rather than diminish employee voice when supported by appropriate leadership relationships.

Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.

The integration of Artificial Intelligence (AI) in library management is redefining how libraries organize, deliver, and personalize information services. Leveraging technologies such as machine learning, natural language processing, and chatbots, AI enables automation of routine tasks, enhances information discovery, and facilitates user-centered services. This article explores the key applications of AI in areas such as cataloguing, personalized recommendations, virtual assistance, and digital preservation. It highlights the significant benefits of AI, including improved operational efficiency, personalized user experiences, and data-driven decision-making. At the same time, it addresses the challenges libraries face in AI adoption, including ethical concerns, data privacy, skill gaps among staff, and the need for inclusive, accessible technologies. The paper emphasizes that for AI to be effectively and responsibly integrated, libraries must invest in infrastructure, staff training, and ethical frameworks. As libraries transform into intelligent, adaptive knowledge hubs, AI presents both a powerful opportunity and a responsibility to serve diverse user communities with fairness, transparency, and innovation. Keywords: - Artificial Intelligence, Library management, Virtual Assistants, Chatbots, Natural language processing

Over the recent years, firms have showed an increased demand for leveraging the performance potential of Artificial Intelligence (AI) technologies. However, the general applicability of AI to many settings raises important managerial questions about whether aspects such as potential for in-house versus external AI development, the presence of complementary technological resources (e.g. access to cloud computing), or even the specific use of AI (e.g. product/service development, operational management) would guide firm-level decisions underlying AI adoption. While recent research has provided some insight on worker-level decisions about using AI, knowledge about firm-level AI adoption, as well as heterogeneities in adoption, remains scant. To address this empirical gap, this preliminary paper provides one of the first comprehensive assessments of firm-level determinants of AI adoption while accounting for the heterogeneous sources, applications, and complementary technological resources of AI in organizations. Specifically, this paper reports the first results of a novel firm-level AI adoption survey deployed to a representative sample of South Korean firms from 2017 and 2018. Econometric assessments identified that firm size and use of existing intangible assets are important for AI adoption. However, while these characteristics are significant for AI adoption regardless of whether the technology was produced in-house or obtained from a vendor, analyses suggest that there may be heterogeneities on the determinants of AI adoption depending on the operational application of AI. Furthermore, AI adoption also ensues in tandem with the adoption of other digital technologies (e.g. big data, cloud computing, and the Internet of Things) and with contemporaneous firm reorganization.

Artificial intelligence (AI) is transforming psychiatric training and education by enhancing diagnostic accuracy, improving therapeutic decision-making, and personalizing learning experiences for trainees. AI-driven simulations, virtual patients, and natural language processing (NLP)-based assessments allow for more effective skill development in psychiatric diagnosis and psychotherapy. Machine learning models provide evidence-based guidance, reinforcing clinical reasoning and treatment strategies. Ethical considerations, including patient confidentiality and bias mitigation, remain central to AI implementation in training. This session explores the latest advancements in AI-driven psychiatric education, discussing practical applications, challenges, and future directions for integrating AI into clinical training programs. Keywords AI, psychiatry, education, machine learning, clinical training

Artificial Intelligence (AI) is gradually transforming the corporate risk management and compliance landscape, as it allows making decisions in a more timely and correct way. This paper evaluates the new role being played by AI in promoting effective risk management and compliance processes in organizations through the manner in which the AI technologies with the ability to collect, analyze, process, and evaluate information, including machine learning, natural language processing, and predictive analytics, are being applied with the purpose of identifying, evaluating, and mitigating the various risks. Whether it is financial fraud detection, cybersecurity, operational interruptions, or compliance, the AI tools are increasing the speed of risk detection, increasing data analysis potential, and creating support for real-time monitoring and forecasting. The paper examines how AI aids in automating repetitive risk management and compliance tasks, thus enabling risk managers to concentrate on the high-level monitoring. As well as listing the advantages, the processes and all those concerned with the adoption of AI in the sphere of risk management and compliance are also spoken about, with the discussion touching upon data quality, model transparency, and governance. The analysis indicates that when integrated in a proper manner, AI can enhance risk management and compliance of an organization and its resilience in a rapidly complicated and more uncertain business-based environment.