The Future of Management Learning: AI-Powered Pedagogies and Adaptive Strategy Implementation

This study explores the applications of artificial intelligence (AI) in event management, analyzing its role across different types of events and different stages of the event life cycle. It also uncovers key opportunities and challenges of AI application in event management and lays down future research directions for this domain. A systematic literature review methodology was employed to analyze peer-reviewed studies on AI in event management. The review categorizes AI applications across various event types and event life cycle stages, highlighting trends, technological advancements, and industry implications. The findings demonstrate that AI is increasingly integrated into event management, offering significant benefits across a wide range of event types, including sports, festivals, art, music, and both corporate and private events. AI adds substantial value throughout the entire event life cycle, from planning to postevent evaluation, by enhancing efficiency, personalization, and decision-making. Key AI applications such as predictive analytics, chatbots, automated marketing, and real-time monitoring have proven to optimize event management processes. However, challenges related to data privacy, ethical considerations, algorithmic biases, and accessibility remain critical concerns. The study highlights the need for a balanced approach that integrates AI-driven automation with human expertise to ensure efficient, sustainable, and responsible event management. This study contributes to the growing research on AI in event management by synthesizing the existing literature in the field and offering a structured analysis of its applications, opportunities, and challenges. It provides a foundation for future research and practical recommendations for industry professionals navigating AI-driven event management. This includes focusing on different types of events such as music and sports in terms of potential AI adoption. By doing so small community-based events that happen at the grassroots level can be compared and contrasted to larger events that have the capacity to utilize newer forms of AI for event management.

The integration of Artificial Intelligence (AI) in school education is transforming the educational landscape in India. AI-driven tools are reshaping traditional learning environments by offering personalised learning experiences, automating administrative tasks, and enhancing student engagement. This research paper explores the current state of AI in school education, analysing its impact on pedagogy, student outcomes, and the challenges that accompany its adoption. The study examines various AI applications, including adaptive learning systems, intelligent tutoring systems, and predictive analytics, which are increasingly becoming integral to classrooms across India. Despite the promising potential of AI, the paper also highlights significant barriers such as digital inequality, lack of infrastructure, and the need for teacher training. Furthermore, the ethical implications of AI in education, including data privacy concerns and algorithmic biases, are critically assessed. Through a review of literature and case studies, this paper offers insights into the future trajectory of AI in schools, emphasising the need for a balanced approach that maximises benefits while mitigating risks. The findings suggest that while AI holds the promise of revolutionising education in India, its successful implementation will require strategic investments in technology, policy frameworks, and capacity building to ensure that all students benefit equitably from these advancements.

Artificial intelligence (AI)-based emergency response systems have emerged as critical enablers of smart infrastructure safety, offering enhanced real-time decision-making, risk assessment, and disaster mitigation strategies across various domains. This systematic literature review, encompassing 424 eligible studies, investigates the integration of machine learning (ML), deep learning (DL), computer vision, IoT-enabled predictive analytics, and AI-powered robotics in optimizing emergency response mechanisms. The study comprehensively examines AI applications in disaster management, real-time incident detection, healthcare emergency response, industrial hazard prevention, cybersecurity frameworks, and intelligent traffic control, providing a detailed assessment of technological advancements and challenges in AI adoption. The findings reveal that AI has significantly improved predictive accuracy, automated hazard detection, and emergency resource optimization, leading to faster response times, minimized human error, and enhanced situational awareness in crisis management. AI-driven predictive analytics models have enabled early warning systems for earthquakes, floods, and wildfires, facilitating proactive disaster preparedness and risk mitigation. In real-time emergency response, AI-powered computer vision and sensor-based surveillance technologies have improved incident detection, reducing intervention delays and ensuring more efficient allocation of emergency resources. In the healthcare sector, AI-enhanced diagnostic tools, triage automation, and geospatial analytics for ambulance dispatch have streamlined medical crisis management, improving survival rates and reducing treatment delays. Additionally, AI-integrated industrial safety frameworks, robotic automation, and cybersecurity intelligence systems have strengthened workplace hazard prevention, cyber threat detection, and emergency communication resilience, ensuring safer and more secure operational environments. Despite these advancements, several challenges related to interoperability, regulatory constraints, cybersecurity vulnerabilities, algorithmic biases, and ethical concerns persist, hindering large-scale AI adoption in emergency response systems. This review provides a comprehensive synthesis of AI’s transformative role in modern emergency management, offering insights into technological developments, limitations, and policy considerations necessary to enhance AI-driven crisis response strategies and ensure more effective, scalable, and resilient emergency safety infrastructures worldwide.

Artificial Intelligence (AI) is a transformative field at the intersection of computer science, mathematics, and cognitive psychology.It involves the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, perception, and decision-making.AI technologies, including machine learning, natural language processing, and robotics, are revolutionizing industries ranging from healthcare to finance, and reshaping the way we live, work, and interact with technology."In the 21st century, Artificial Intelligence (AI) has emerged as a transformative force reshaping various aspects of society and industry.With advancements in computing power, data availability, and algorithmic techniques, AI has made significant strides in enabling machines to perform tasks traditionally requiring human intelligence.One of the key drivers of AI progress in the 21st century has been machine learning, a subfield of AI that focuses on algorithms capable of learning from data and making predictions or decisions.Deep learning, a subset of machine learning that uses artificial neural networks with many layers, has particularly fuelled breakthroughs in areas such as image and speech recognition, natural language processing, and autonomous vehicles.AI applications have become pervasive across industries, revolutionizing sectors such as healthcare, finance, transportation, manufacturing, and entertainment.In healthcare, AI is being used for disease diagnosis, drug discovery, personalized medicine, and medical image analysis.In finance, it's employed for fraud detection, algorithmic trading, and customer service.Autonomous vehicles and smart transportation systems leverage AI for navigation, route optimization, and traffic management.Moreover, AI-driven virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous, enhancing user experience and productivity.AIpowered recommendation systems personalize content delivery on platforms like Netflix, Spotify, and Amazon, improving user engagement and satisfaction.However, alongside its transformative potential, AI raises ethical, social, and economic concerns.Issues such as job displacement due to automation, algorithmic biases, data privacy, and the potential misuse of AI for surveillance or military purposes require careful consideration and regulation.Overall, the 21st century has witnessed AI evolve from a theoretical concept to a practical technology with profound implications for society, promising both opportunities and challenges as it continues to advance. Artificial Intelligence and Human Society:Artificial Intelligence (AI) is deeply intertwined with human society, impacting various aspects of our daily lives, work, and culture.Here's how AI influences human society:1. Workforce Automation: AI technologies automate routine and repetitive tasks across industries, leading to increased efficiency and productivity.However, concerns about job displacement and the need for reskilling and upskilling arise as AI replaces certain human roles.2. Healthcare: AI applications in healthcare facilitate disease diagnosis, personalized treatment plans, drug discovery, and medical imaging analysis.AI-driven predictive analytics also help in identifying health risks and optimizing patient outcomes.3. Education: AI enhances education through personalized learning platforms, intelligent tutoring systems, and adaptive assessments tailored to individual student needs.It also enables the development of educational content and resources based on learner preferences and performance.4. Communication and Interaction: AI-driven virtual assistants, chatbots, and language translation services improve communication and accessibility.Natural language processing enables human-like interactions with AI systems, enhancing user experience and accessibility for people with disabilities.

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 integration of artificial intelligence (AI) into online education has transformed the digital learning space, offering new ways to enhance learner satisfaction and engagement. This systematic literature review, covering a five-year span from 2020 to 2025, explores how AI technologies, such as chatbots, intelligent tutoring systems (ITS), sentiment analysis, gaze tracking and predictive analytics, support learner engagement across cognitive, emotional, behavioral, and social dimensions. Drawing from 30 peer-reviewed studies, the current review addresses three central research questions: (1) What aspects of AI positively influence learner satisfaction and engagement in online courses within higher education institutions; (2) What potential challenges from using these technologies may arise; and (3) What research approaches are most commonly used to assess AI’s impact in such learning contexts? The findings highlight that adaptive learning, real-time feedback, and emotion-aware systems contribute positively to personalized learning and motivation. However, concerns persist around data privacy, algorithmic bias, over-reliance on automation, and system usability. Experimental and quasi-experimental designs, as well as machine learning, mixed methods, and survey-based approaches are found to dominate in reviewed studies. Based on these insights, this work offers a foundation for future AI-enhanced learning management systems designed primarily to enhance learner engagement across cognitive, emotional, behavioral, and social domains.

The application of artificial intelligence (AI) in medicine has garnered significant attention in recent years, offering new possibilities for improving patient care across various domains. For herpes zoster, a viral infection caused by the reactivation of the varicella-zoster virus, AI technologies have shown remarkable potential in enhancing disease diagnosis, treatment, and management. This study aims to investigate the current research status in the use of AI for herpes zoster, offering a comprehensive synthesis of existing advancements. A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Three databases of Web of Science Core Collection, PubMed, and IEEE were searched to identify relevant studies on AI applications in herpes zoster research on November 17, 2023. Inclusion criteria were as follows: (1) research articles, (2) published in English, (3) involving actual AI applications, and (4) focusing on herpes zoster. Exclusion criteria comprised nonresearch articles, non-English papers, and studies only mentioning AI without application. Two independent clinicians screened the studies, with a third senior clinician resolving disagreements. In total, 26 articles were included. Data were extracted on AI task types; algorithms; data sources; data types; and clinical applications in diagnosis, treatment, and management. Trend analysis revealed an increasing annual interest in AI applications for herpes zoster. Hospital-derived data were the primary source (15/26, 57.7%), followed by public databases (6/26, 23.1%) and internet data (5/26, 19.2%). Medical images (9/26, 34.6%) and electronic medical records (7/26, 26.9%) were the most commonly used data types. Classification tasks (85.2%) dominated AI applications, with neural networks, particularly multilayer perceptron and convolutional neural networks being the most frequently used algorithms. AI applications were analyzed across three domains: (1) diagnosis, where mobile deep neural networks, convolutional neural network ensemble models, and mixed-scale attention-based models have improved diagnostic accuracy and efficiency; (2) treatment, where machine learning models, such as deep autoencoders combined with functional magnetic resonance imaging, electroencephalography, and clinical data, have enhanced treatment outcome predictions; and (3) management, where AI has facilitated case identification, epidemiological research, health care burden assessment, and risk factor exploration for postherpetic neuralgia and other complications. Overall, this study provides a comprehensive overview of AI applications in herpes zoster from clinical, data, and algorithmic perspectives, offering valuable insights for future research in this rapidly evolving field. AI has significantly advanced herpes zoster research by enhancing diagnostic accuracy, predicting treatment outcomes, and optimizing disease management. However, several limitations exist, including potential omissions from excluding databases like Embase and Scopus, language bias due to the inclusion of only English publications, and the risk of subjective bias in study selection. Broader studies and continuous updates are needed to fully capture the scope of AI applications in herpes zoster in the future.

Artificial Intelligence (AI) is increasingly transforming higher education across the globe by reshaping teaching, learning, research, and institutional management. In developing countries such as Tanzania, the integration of AI into Higher Education Institutions (HEIs) presents both significant opportunities and complex challenges. Despite the presence of infrastructural and resource limitations, AI has the potential to revolutionize the educational landscape by improving access to quality learning materials, supporting data-driven decision-making, and enhancing administrative efficiency. This study explores the multifaceted impacts of AI on Tanzanian HEIs, with a specific focus on its current applications, benefits, challenges, and policy implications. The research draws upon recent empirical studies and secondary data to analyze how AI technologies such as intelligent tutoring systems, predictive analytics, and automated assessment tools contribute to personalized learning experiences and improved academic outcomes. Moreover, the study identifies the major barriers to AI adoption, including inadequate technological infrastructure, limited institutional capacity, insufficient AI-related policies, and persistent ethical concerns surrounding data privacy and algorithmic bias. Findings reveal that although AI adoption in Tanzanian HEIs is on the rise, the implementation remains fragmented and uneven across institutions. To fully harness AI’s transformative potential, the study recommends the development of comprehensive national AI policies, investments in digital infrastructure, and targeted capacity-building programs for educators and administrators. Additionally, the research emphasizes the need for ethical frameworks that promote fairness, inclusivity, and transparency in AI utilization. Overall, this paper underscores the necessity of a strategic and policy-driven approach to ensure that AI contributes effectively and equitably to the advancement of higher education in Tanzania.

Globally, Artificial Intelligence (AI) is changing higher education, and African institutions are becoming more and more involved in this digital revolution. AI applications such as predictive analytics, automated grading, and intelligent tutoring systems offer promising opportunities to enhance research, teaching, learning, and administration—particularly in settings with limited resources. However, the incorporation of AI into African higher education also raises significant socio-technical and ethical issues. Academic dishonesty, algorithmic bias, data privacy, and unequal access to digital infrastructure are some of the issues that risk exacerbating existing educational inequalities. Furthermore, concerns regarding digital sovereignty, fairness, and cultural relevance are raised by the dependence on AI tools created in the Global North. This article highlights the dual potential for both empowerment and marginalization as it critically analyzes the advantages and hazards of implementing AI in African higher institutions. It calls for a responsible, inclusive, and culturally grounded approach to AI in African higher education through a contextual and ethical lens, using concepts like digital justice, the ethics of technology, and decolonial theory.

AI (Artificial Intelligence) has been a major factor in changing the financial landscape in recent years.  The term "Smart Finance" describes use of AI tools like RPA (robotic process automation), ML (machine learning), natural language processing (NLP) to improve financial decision-making, automate repetitive tasks, and offer more in-depth understanding of how businesses operate.  Financial institutions can improve customer service, manage risk better, streamline operations, and spur long-term development with this data-driven strategy. This chapter explores how AI is transforming finance industry, analyzing its applications, challenges, and opportunities for business growth. We also give a thorough look at future of AI in financial industry and talk about the approaches for integrating AI into financial systems. Combination of AI and finance in the digital era is changing conventional corporate environments. "Smart Finance" refers to integration of AI technologies—involving machine learning (ML), predictive analytics, and NLP—into financial operations to drive strategic decision-making, improve efficiency, and enhance business growth. This paper explores how AI-powered financial systems are transforming data into actionable insights that enable organizations to operate more intelligently and competitively. AI facilitates real-time financial data analysis, enabling companies to forecast trends, detect anomalies, manage risks, and optimize investments with higher accuracy. From automated bookkeeping and fraud detection to intelligent budgeting and customer behavior analysis, AI tools are becoming critical assets in the financial decision-making process. AI improves operational efficiency and frees up financial experts to concentrate on high-level strategic duties by automating repetitive operations and reducing human error. Additionally, platforms powered by AI are able to spot patterns in large datasets that would be very difficult to find by hand. This capability empowers businesses to personalize financial products, improve credit scoring models, and develop dynamic pricing strategies that respond to market shifts. SMEs, in particular, benefit from AI-driven financial insights that were traditionally accessible only to large corporations with extensive resources. But there are drawbacks to using AI in banking as well, such as algorithmic bias, data privacy issues, as well as requirement for qualified personnel to efficiently operate AI systems.  Notwithstanding these challenges, AI has significant long-term benefits for the financial industry, promoting a more robust, informed, and flexible corporate environment.  The objective of this essay is to present thorough examination of how AI is transforming financial operations and promoting long-term company success.  It illustrates that smart finance is not just a technical development but also a strategic necessity for progressive companies through case studies and new trends.

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) has rapidly emerged as a transformative force in modern medicine, revolutionising diagnostics, treatment personalisation, and clinical decision-making. This review synthesises current literature on AI's evolution, applications, challenges, and future directions in healthcare. From early rule-based systems to advanced deep learning algorithms, AI has consistently demonstrated capabilities that rival and enhance human expertise—particularly in imaging, predictive analytics, and drug discovery. The role of AI in global health is also expanding, offering scalable solutions to reduce disparities in low-resource settings. However, the integration of AI raises ethical and legal concerns, including data privacy, algorithmic bias, and unclear accountability frameworks. Drawing on the Technology Acceptance Model (TAM), Diffusion of Innovations Theory, and Principlism, this review highlights theoretical perspectives essential to understanding AI adoption and governance. The paper concludes with a call for longitudinal studies, ethical frameworks, and policy innovations to support AI's responsible and equitable deployment in the medical field.

This research aims to critically examine the ethical integration of artificial intelligence (AI) in education through the perspective of maqāṣid al-sharīʿah, emphasizing the alignment between technological innovation and Islamic moral principles. The methods used are a systematic literature review and thematic content analysis against peer-reviewed publications for the period 2015–2025, which discuss the application of AI in primary, secondary, and higher education. The study identified dominant ethical issues, such as data privacy, algorithmic bias, accountability, human agency, and moral development, which were then mapped to Islamic ethical goals, including ʿadl (justice), amānah (belief), karāmah al-insān (human dignity), and ḥifẓ al-ʿaql (protection of reason). The results of the analysis show that the adoption of AI in education often emphasizes efficiency, personalization, and predictive analytics, but has the potential to reduce learners' autonomy and ethical reasoning. The mapping of maqāṣid al-sharīʿah shows a strong normative conformity, so that Islamic principles can be a moral foundation as well as a practical guide for AI governance. The research contribution is theoretical by bridging the literature on AI ethics and Islamic educational philosophy, as well as practical by offering an integrative framework for AI policymakers, educators, and developers. The integration of maqāṣid al-sharīʿah in AI governance ensures justice, trust, inclusivity, and the development of the whole human being (insān kāmil).

Advances in artificial intelligence (AI) have transformed various aspects of human resource management (HRM), especially in decision making, operational efficiency, and data analysis. The application of AI in HRM offers opportunities to increase organizational effectiveness, but also raises challenges related to transparency, algorithmic bias, and ethics in workforce management. Therefore, this research was conducted to explore the impact of AI in HRM and how organizations can optimize its use. This research uses a literature review method, by examining various scientific sources related to the implementation of AI in HRM. The study was conducted on articles from academic journals, industry reports, and related publications that discuss the role of AI in the recruitment process, performance evaluation, employee training, and strategic decision making. The research results show that AI can improve HRM efficiency through automation of administrative tasks, predictive analytics, and personalization of the employee experience. However, the main challenges found include algorithmic bias, lack of transparency in AI systems, and legal and regulatory uncertainty. In conclusion, AI has great potential in supporting more effective and data-driven HRM, but its use must be accompanied by policies that ensure transparency, accountability and a balance between automation and the role of humans in decision making. Therefore, a careful approach is needed in adopting AI to ensure maximum benefits without compromising aspects of ethics and fairness.

This study investigates the scientific development of Artificial Intelligence (AI) and Machine Learning (ML) applications in Human Resource Management (HRM) through bibliometric analysis. To this end, 522 academic publications indexed in the Scopus database between 2020 and 2024 were analyzed using the R-based bibliometrix package and VOSviewer software. Descriptive analysis, scientific productivity metrics, and content analysis techniques were employed. The findings revealed three main patterns. First, research on AI and ML applications in HRM has grown significantly—particularly between 2022 and 2024—driven by post-pandemic digital transformation. Second, India, China, and the USA lead in research output, while the UK and France demonstrate strong citation impact, indicating a globally expanding research ecosystem. Third, the thematic focus of research is shifting from technical infrastructure toward more human-centered and ethical dimensions. Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. Compared to the previous studies, this research provides a more comprehensive bibliometric analysis of AI and ML applications in HRM. It is the first extensive study to map the intellectual evolution of the field from a multidisciplinary perspective. Furthermore, it charts research trends and collaboration networks, revealing a shift from technical implementations to strategic integration. In conclusion, this analysis offers new insights to the literature by illustrating the technological evolution in HRM and highlighting the growing significance of cutting-edge approaches such as AI and ML, reaffirming the field as a timely and impactful area of research.