Artificial Intelligence in Event Management: A Systematic Literature Review

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.

Artificial intelligence (AI) applications are widely employed nowadays in almost every industry impacting individuals and society. As many important decisions are now being automated by various AI applications, fairness is fast becoming a vital concern in AI. Moreover, the organizational applications of AI-enabled decision systems have exacerbated this problem by amplifying the pre-existing societal bias and creating new types of biases. Interestingly, the related literature and industry press suggest that AI systems are often biased towards gender. Specifically, AI hiring tools are often biased towards women. Therefore, it is an increasing concern to reconsider the organizational managerial practices for AI-enabled decision systems to bring fairness in decision making. Additionally, organizations should develop fair, ethical internal structures and corporate strategies and governance to manage the gender imbalance in AI recruitment process. Thus, by systematically reviewing and synthesizing the literature, this paper presents a comprehensive overview of the managerial practices taken in relation to gender bias in AI. Our findings indicate that managerial practices include: better fairness governance practices, continuous training on fairness and ethics for all stakeholders, collaborative organizational learning on fairness & demographic characteristics, interdisciplinary approach & understanding of AI ethical principles, Workplace diversity in managerial roles, designing strategies for incorporating algorithmic transparency and accountability & ensuring human in the loop. In this paper, we aim to contribute to the emerging IS literature on AI by presenting a consolidated picture and understanding of this phenomenon. Based on our findings, we indicate direction for future research in IS for the better development and use of AI systems.

The COVID-19 pandemic has significantly impacted global healthcare systems, revealing vulnerabilities in disease surveillance, treatment capabilities, and emergency preparedness. The rapid transmission, severity, and economic disruption caused by the pandemic emphasized the urgent need for technological interventions. Artificial Intelligence (AI) emerged as a game-changer in pandemic response, providing automated solutions for early diagnosis, patient monitoring, drug discovery, and social distancing enforcement. AI-driven solutions played a crucial role in identifying infections early using advanced imaging techniques and predictive analytics. Machine learning models trained on radiographic images improved diagnostic accuracy, reducing the dependency on human expertise in overwhelmed healthcare facilities. AI-based epidemiological modeling assisted policymakers in predicting the spread of infections, enabling timely containment measures. Additionally, AI powered surveillance systems enforced social distancing regulations in public spaces, minimizing human intervention. In the healthcare sector, AI revolutionized patient care and hospital management by optimizing treatment plans, automating administrative tasks, and enhancing robotic assisted surgeries. AI-driven chatbots provided virtual consultations, reducing the burden on frontline healthcare workers. AI powered wearable devices enabled real-time patient monitoring, ensuring early detection of health deterioration. The integration of AI in drug discovery accelerated the search for COVID-19 treatments and vaccine development. AI analyzed vast biomedical datasets to identify potential antiviral compounds and optimize clinical trials, significantly reducing drug development timelines. Despite these advancements, several challenges persist, including data privacy concerns, bias infrastructure in AI models, and limitations. Ethical considerations, such as transparency in AI decision-making and fairness in healthcare AI applications, remain crucial for widespread adoption. This study explores the multifaceted role of AI in healthcare, particularly in pandemic management, highlighting its benefits, challenges, and future directions. AI has proven to be an indispensable tool in mitigating pandemics, and continuous innovations in AI applications will play a crucial role in enhancing healthcare resilience against future outbreaks.

This study explores the role of artificial intelligence (AI) in transforming management education and strategic decision-making, focusing on AI-powered pedagogies and adaptive strategy implementation. The research investigates how AI can personalize learning experiences through intelligent tutoring systems, predictive analytics, and real-time feedback mechanisms, thereby enhancing engagement, retention, and critical thinking skills essential for modern management roles. Additionally, the study examines AI’s potential to support organizations in implementing adaptive strategies through real-time data analytics, allowing for proactive decision-making and agile responses to market dynamics. The study proposes a conceptual framework that integrates AI-driven learning with adaptive strategy tools, aiming to enhance both management learning outcomes and organizational agility. Based on a systematic literature review, the findings underscore the significance of AI in both educational and organizational contexts. However, challenges such as data privacy concerns, algorithmic bias, and the need for AI expertise remain critical barriers to broader adoption. This study contributes to the growing body of knowledge on AI applications in management, offering insights into its potential to revolutionize both learning and strategic processes. The findings highlight the need for further empirical research to validate the proposed framework and explore its practical implementation.

The COVID-19 pandemic has significantly impacted global healthcare systems, revealing vulnerabilities in disease surveillance, treatment capabilities, and emergency preparedness. The rapid transmission, severity, and economic disruption caused by the pandemic emphasized the urgent need for technological interventions. Artificial Intelligence (AI) emerged as a game-changer in pandemic response, providing automated solutions for early diagnosis, patient monitoring, drug discovery, and social distancing enforcement. AI-driven solutions played a crucial role in identifying infections early using advanced imaging techniques and predictive analytics. Machine learning models trained on radiographic images improved diagnostic accuracy, reducing the dependency on human expertise in overwhelmed healthcare facilities. AI-based epidemiological modeling assisted policymakers in predicting the spread of infections, enabling timely containment measures. Additionally, AI powered surveillance systems enforced social distancing regulations in public spaces, minimizing human intervention. In the healthcare sector, AI revolutionized patient care and hospital management by optimizing treatment plans, automating administrative tasks, and enhancing robotic assisted surgeries. AI-driven chatbots provided virtual consultations, reducing the burden on frontline healthcare workers. AI powered wearable devices enabled real-time patient monitoring, ensuring early detection of health deterioration. The integration of AI in drug discovery accelerated the search for COVID-19 treatments and vaccine development. AI analyzed vast biomedical datasets to identify potential antiviral compounds and optimize clinical trials, significantly reducing drug development timelines. Despite these advancements, several challenges persist, including data privacy concerns, bias infrastructure in AI models, and limitations. Ethical considerations, such as transparency in AI decision-making and fairness in healthcare AI applications, remain crucial for widespread adoption. This study explores the multifaceted role of AI in healthcare, particularly in pandemic management, highlighting its benefits, challenges, and future directions. AI has proven to be an indispensable tool in mitigating pandemics, and continuous innovations in AI applications will play a crucial role in enhancing healthcare resilience against future outbreaks.

Artificial Intelligence (AI) is transforming internal audit functions in financial institutions, offering capabilities such as predictive analytics, automated risk assessment, and real-time monitoring. In Islamic financial institutions (IFIs), integrating AI presents unique challenges and opportunities, particularly in aligning technological innovations with Shariah governance and ethical principles. Despite growing interest in AI applications, research on integrating AI into internal auditing for IFIs remains limited. This study proposes a conceptual framework illustrating the role of AI in enhancing internal audit effectiveness while ensuring compliance with Maqasid al-Shariah. The framework addresses key opportunities, including improved efficiency, fraud detection, and compliance monitoring, as well as conceptual challenges such as algorithmic bias, data privacy, and ethical accountability. The paper contributes to theory by extending audit and governance literature through the lens of digital transformation in Islamic finance. Practical implications are offered for regulators, Shariah boards, and internal auditors seeking to adopt AI responsibly. Future research directions include empirical validation of the proposed framework and cross-comparative studies with conventional financial institutions.

This study examined artificial intelligence (AI) applications for health information service delivery throughout Africa and its subregions. The study aimed to review existing research, identify key AI applications in healthcare, assess their impact in African healthcare, and examine strategies that promote effective, ethical, and equitable integration for sustainable healthcare improvements across the continent. African healthcare systems face obstacles, including restricted access to quality information, shortages of healthcare professionals, and infrastructural limitations. AI presents promising solutions to these challenges. The article comprises a systematic literature review of peer-reviewed research on AI-driven health information service delivery in Africa between 2014 and 2024, examining key AI applications, opportunities, challenges, and ethical considerations associated with AI adoption in healthcare, and their influence on health information service delivery. This review examined African healthcare systems’ preparedness for AI adoption, the importance of digital literacy, and the infrastructure that affects AI dissemination. The study also examined ethical considerations, including data privacy, equity, and the risk of widening health disparities. The findings reveal that AI technologies can enhance the accessibility, precision, and efficiency of health information services across Africa, particularly in underserved areas. The primary obstacles to AI adoption include insufficient digital infrastructure, low digital literacy, and socio-cultural hurdles. This review emphasises addressing ethical concerns to ensure that AI-driven health information systems benefit all populations without exacerbating inequality. The review offers insights for policymakers, healthcare providers, and researchers. Areas for future research and development were reviewed to ensure sustainable and equitable AI integration across Africa’s healthcare systems. The study recommends that stakeholders invest in digital infrastructure, promote digital literacy, and establish ethical guidelines for successful and equitable AI integration into health information service delivery throughout Africa.

Artificial Intelligence (AI) has revolutionized digital marketing by enhancing personalization, automating customer interactions, and optimizing marketing strategies. AI-powered tools, including chatbots, predictive analytics, recommendation systems, and automated content generation, have transformed how businesses engage with consumers. This study conducts a systematic literature review (SLR) to examine AI’s role in digital marketing, focusing on its applications, benefits, and challenges. Using data from Scopus, Web of Science, and Google Scholar, this review analyzes key AI-driven marketing strategies and their impact on customer engagement, conversion rates, and overall marketing efficiency. The findings indicate that AI enhances marketing effectiveness by enabling real-time personalization, improving customer targeting, and streamlining content creation. Businesses leverage AI to analyze vast amounts of consumer data, allowing for accurate predictions of consumer behavior and preferences. However, AI implementation in digital marketing presents challenges, including data privacy concerns, ethical issues, algorithmic bias, and high implementation costs. Moreover, consumer trust in AI-driven marketing varies, influencing acceptance and engagement levels. This study provides valuable insights for academics and practitioners on how AI is reshaping digital marketing landscapes. It highlights the need for balancing AI-driven automation with ethical considerations and consumer expectations. Future research should explore strategies for ethical AI adoption, enhancing consumer trust, and optimizing AI-driven decision-making. Addressing these challenges will enable businesses to fully harness AI’s potential for more effective and transparent marketing strategies.

Artificial Intelligence (AI) has become a pivotal force in transforming the retail industry, particularly in the online shopping environment. This study investigates the impact of various AI applications—such as personalized recommendations, chatbots, predictive analytics, and social media engagement—on consumer buying behaviors. Employing a quantitative research design, data was collected from 760 respondents through a structured online survey. The snowball sampling technique facilitated the recruitment of participants, focusing on diverse demographics and their interactions with AI technologies in online retail. The findings reveal that AI-driven personalization significantly enhances consumer purchase intentions and satisfaction. Multiple regression analysis shows that AI personalization (β = 0.35, p < 0.001) has the most substantial impact on purchase intention, followed by chatbot effectiveness (β = 0.25, p < 0.001), predictive analytics (β = 0.20, p < 0.001), and social media engagement (β = 0.15, p < 0.01). Similarly, AI personalization (β = 0.30, p < 0.001), predictive analytics (β = 0.25, p < 0.001), and chatbot effectiveness (β = 0.20, p < 0.001) significantly influence consumer satisfaction. The hierarchical regression analysis underscores the importance of ethical considerations, showing that ethical and transparent use of AI increases consumer trust and engagement. Model 1 explains 45% of the variance in consumer behavior (R2 = 0.45, F = 154.75, p < 0.001), while Model 2, incorporating ethical concerns, explains an additional 10% (R2 = 0.55, F = 98.25, p < 0.001). This study highlights the necessity for retailers to leverage AI technologies ethically and effectively to gain a competitive edge, improve customer satisfaction, and drive long-term success. Future research should explore the long-term impacts of AI on consumer behavior and the integration of emerging technologies such as augmented reality and the Internet of Things (IoT) in retail.

This study evaluates the impact of Artificial Intelligence (AI) applications in power systems on energy security and to determine relevant policy implications. We use a mixed methods approach to analyze the benefits and risks associated with AI implementation on the European power grid, focusing on four key dimensions of energy security: availability, affordability, accessibility, and acceptability. We investigated the benefits of AI using PyPSA, a Python-based model of the European electricity system. Three AI applications were parametrized: load reduction, load shifting, and wind wake steering. We compared scenarios in which these AI applications are widely deployed against a baseline scenario without these applications to determine if AI improves energy security. The study also analyzes risks associated with AI deployment in the power grid. We developed a risk taxonomy centered around six high-level categories: cybersecurity, jurisdictional or sovereignty issues, unexplained or unexpected actions by the model, unethical or illegal decision-making, reliance and trust in decision-making, and supplier dependency and vendor lock-in. Additionally, we conducted a back-casting exercise with subject-matter experts to determine positive and negative future outcomes of AI deployment and identify actions to create positive outcomes and avoid negative ones. The paper presents a set of policy implications for AI on the European synchronous grid. We find that AI applications can improve energy security in power systems. In the scenarios we tested, behind-the-meter applications have a greater impact on energy security than front-of-meter applications. The results suggest that AI applications targeting energy consumption may significantly improve energy security metrics. Keywords: Energy Security, AI, Power Grid, European Electricity System

The advent of artificial intelligence (AI) marks a pivotal shift in the landscape of corporate governance, catalyzing a reevaluation of traditional frameworks and necessitating a forward-looking approach to decision-making, risk management, and ethical considerations. This study explores the multifaceted impact of AI on corporate governance, offering a nuanced analysis of how AI technologies are transforming the operational, strategic, and ethical dimensions of organizations. The research underscores the potential of AI to enhance decision-making processes, optimize operational efficiencies, and foster innovation by providing advanced analytical capabilities and predictive insights. However, it concurrently highlights the emergence of unprecedented challenges, including data privacy concerns, algorithmic bias, and the need for robust regulatory frameworks to mitigate risks associated with AI deployment. The article advocates for a proactive stance in redefining corporate governance models to accommodate the disruptive nature of AI, emphasizing the integration of ethical considerations and transparency in AI applications. It calls for a collaborative effort among corporate leaders, policymakers, and stakeholders to develop governance structures that not only leverage AI’s potential but also safeguard against its inherent risks. The study’s recommendations include the establishment of ethical AI guidelines, the adoption of transparent AI practices, and the continuous monitoring of AI systems to ensure their alignment with corporate governance objectives and societal values. However, it is important to note that the approach and methods used in this study are based on a qualitativeliterature review and, therefore, the generalization of the findings across different sectors and corporate governance frameworks may be limited. Additionally, the rapidly evolving nature of AI technologies poses inherent challenges to keeping up with emerging trends and potential risks.

Technological progress, especially in the field of information, has led to reducing the gap between science, information and knowledge. This accelerating change has forced all educational institutions to comply with this new mission by using information technology in the teaching process, as artificial intelligence has become an integral part of the educational and learning process, and a means of Effective in the teaching process, in addition to being research tools in obtaining information and knowledge, the research problem focused on (what are the attitudes of the kindergarten teacher towards using artificial intelligence applications in the effective teaching process in the early education stage in the third Rusafa Education Directorate?), where the importance of The research is considered one of the researches that deals with an important and vital topic (artificial intelligence), as it gains its importance in that it represents the role that kindergarten teachers play in the early education stage in developing the skills and abilities and refining the information of the child through their use of artificial intelligence methods and applications, as well as The positive role of such research in stimulating kindergarten teachers’ motivation to benefit from artificial intelligence applications, which increases the efficiency and effectiveness of their teaching. The aim of the research is to reveal kindergarten teachers’ attitudes towards using artificial intelligence in the effective teaching process in the early education stage, as well as to reveal the most important The challenges facing kindergarten teachers using artificial intelligence in the effective teaching process, and are there any statistically significant differences between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables? The research reached several conclusions, perhaps the most important of which is the existence of differences. There is statistical significance between the average kindergarten teacher’s attitudes to using artificial intelligence in the effective teaching process in the early education stage according to the research variables. The research recommends holding many workshops and training courses for kindergarten teachers on how to use artificial intelligence in effective teaching, and including programs and rehabilitation plans for kindergarten teachers. About the use of artificial intelligence according to the plans of the Ministry of Education, which are consistent with its vision, and encouraging teachers to proceed and benefit from applications and websites for safe and available artificial intelligence applications in the teaching process

Artificial Intelligence (AI) is transforming the sports industry by enhancing athletic performance, improving predictive analytics, managing health, boosting fan engagement, and ensuring accurate officiating. This review provides a comprehensive examination of AI's applications in sports, drawing from a systematic literature search across databases such as PubMed, IEEE Xplore, and Google Scholar. The methodology involved selecting recent, relevant studies that contribute to understanding AI's impact in sports, with data extraction focusing on AI applications, specific sports contexts, observed benefits, challenges, and future opportunities. In enhancing athletic performance, AI analyzes data from sensors, cameras, and wearable devices to identify strengths and weaknesses, optimizing training programs. Examples include AI-driven platforms in football and basketball that track player positions and actions, providing actionable insights for coaches. Predictive analytics, another crucial AI application, involves forecasting game outcomes and evaluating player performance through historical and current data, aiding in strategic decision-making. AI also plays a vital role in health management and injury prevention by analyzing biomechanical and physiological data to predict potential injuries and monitor recovery, ensuring athletes' well-being and prolonging careers. Fan experience is significantly enhanced through personalized recommendations, virtual assistants, and augmented reality experiences, deepening engagement and satisfaction. Officiating benefits from AI technologies like VAR in football, which review and analyze controversial decisions to ensure accuracy and fairness. Despite the promising applications, challenges such as data quality, cost, and ethical concerns, including privacy and bias, remain. Future opportunities lie in developing real-time feedback systems and integrating AI with the Internet of Things (IoT) for comprehensive data collection, further revolutionizing performance analysis and injury prevention. This review highlights AI's transformative potential in sports, emphasizing the need for high-quality data, cost-effective solutions, and ethical considerations to maximize its benefits and address existing challenges.

This study explores the integration of Artificial Intelligence (AI) into the Reverse Logistics Process (RLP) to enhance decision-making, operational efficiency, and resource recovery. Through a systematic literature review, the applications of AI across critical RLP stages (planning, execution and control) are identified. Key findings highlight the transformative role of AI in optimizing network design, improving product collection and inspection, and supporting decision-making for the disposition of recovered items. AI technologies and functionalities such as machine learning, predictive analytics, and decision-support systems demonstrate significant potential for automating complex processes, reducing operational costs, and improving logistical precision. However, barriers to AI adoption in the RLP include high implementation costs, organisational resistance, lack of specialist personnel, and limited technological infrastructure. The review also identifies enablers such as advancements in the Internet of Things, blockchain, and big data analytics, which facilitate AI adoption and integration in RLP. The study concludes that AI is essential for establishing resilient and adaptive RL systems and offers substantial opportunities to address uncertainty while improving efficiency and decision-making. Future research should focus on overcoming adoption barriers, advancing predictive models, and integrating AI with other Industry 4.0 technologies to enhance RLP outcomes. This review fills a significant gap in the literature by offering a structured analytical framework that categorises AI applications based on decision types and RLP stages, providing an integrated perspective not previously addressed in the field.

Artificial intelligence (AI) is revolutionizing occupational health and safety (OHS) by addressing workplace hazards and enhancing employee well-being. This review explores the broader context of increasing automation and digitalization, focusing on the role of human-AI interaction in improving workplace health, safety, and productivity while considering associated challenges. A narrative review methodology was employed, involving a comprehensive literature search in PubMed, Embase, and Scopus for studies published within the last 25 years. After screening for relevance and eligibility, a total of 52 articles were included in the final analysis. These publications examined various AI applications in OHS, such as wearable technologies, predictive analytics, and ergonomic tools, with a focus on their contributions and limitations. Key findings demonstrate that AI enhances hazard detection, enables real-time monitoring, and improves training through immersive simulations, significantly contributing to safer and more efficient workplaces. However, challenges such as data privacy concerns, algorithmic biases, and reduced worker autonomy were identified as significant barriers to broader AI adoption in OHS. AI holds great promise in transforming OHS practices, but its integration requires ethical frameworks and human-centric collaboration models to ensure transparency, equity, and worker empowerment. Addressing these challenges will allow workplaces to harness the full potential of AI in creating safer, healthier, and more sustainable environments.