A significant advancement in artificial intelligence is the development of large language models (LLMs). Despite opposition and explicit bans by some authorities, LLMs continue to play a transformative role, particularly in education, by improving language understanding and generation capabilities. This study explores LLMs’ types, history, and training processes, alongside their application in education, including digital and higher education settings. A novel theoretical framework is proposed to guide the integration of LLMs into education, addressing key challenges such as personalization, ethical concerns, and adaptability. Furthermore, the study presents practical case studies and solutions to barriers, such as data privacy and bias, offering insights into their role in enhancing the teaching–learning process. By providing a systematic analysis and proposing a structured framework, this study advances current knowledge and highlights the significant potential of LLMs in revolutionizing education.

This paper presents an in-depth literature review on the driving forces and barriers for achieving operational excellence through artificial intelligence (AI). Artificial intelligence is a technological concept spanning operational management, philosophy, humanities, statistics, mathematics, computer sciences, and social sciences. AI refers to machines mimicking human behavior in terms of cognitive functions. The evolution of new technological procedures and advancements in producing intelligence for machines creates a positive impact on decisions, operations, strategies, and management incorporated in the production process of goods and services. Businesses develop various methods and solutions to extract meaningful information, such as big data, automatic production capabilities, and systematization for business improvement. The progress in organizational competitiveness is apparent through improvements in firm’s decisions, resulting in increased operational efficiencies. Innovation with AI has enabled small businesses to reduce operating expenses and increase revenues. The focused literature review reveals the driving forces for achieving operational excellence through AI are improvement in computing abilities of machines, development of data-based AI, advancements in deep learning, cloud computing, data management, and integration of AI in operations. The barriers are mainly cultural constraints, fear of the unknown, lack of employee skills, and strategic planning for adopting AI. The current paper presents an analysis of articles focused on AI adoption in production and operations. We selected articles published between 2015 and 2020. Our study contributes to the literature reviews on operational excellence, artificial intelligence, driving forces for AI, and AI barriers in achieving operational excellence.

This chapter thoroughly investigates the revolutionary use of blockchain technology in the administration of medical data. Blockchain integration has become a disruptive force in the era of digital health records, with the potential to completely revolutionize how healthcare organizations manage, trade, and safeguard data. The main objective is to analyze blockchain's complex role in transforming healthcare data management. The foundations of blockchain in healthcare data management are among the important subjects addressed; this section introduces the technology's basic ideas, composition, and operation. Examining how blockchain improves data security and privacy in healthcare takes up much of this chapter. The analysis focuses on how blockchain breaks down the conventional boundaries of data silos to enable smooth data interchange across heterogeneous healthcare systems. Examining the difficulties and real-world applications of implementing blockchain technology in healthcare rounds off the chapter.

This chapter explores the ever-changing environment of wearable health technology and its critical role in transforming modern healthcare. It goes into the historical history of these devices, tracking their path from basic fitness trackers to sophisticated health-monitoring systems, focusing on proactive and preventative health management. The present scene emphasizes the range and breadth of accessible wearable technology in healthcare. It discusses sensor improvements, biometric monitoring, and wireless communications. These advancements have significantly improved the precision and effectiveness of monitoring of numerous health markers. This chapter also thoroughly reviews the use of wearable devices in remote patient monitoring, emphasizing their revolutionary influence on chronic illness management and senior care through real-world case studies. Furthermore, investigates the integration of wearable device data into healthcare systems for real-time monitoring, covering technological and infrastructure issues.

Artificial Intelligence (AI) has rapidly emerged as a revolutionary force across various sectors, with a profound influence permeating the domain of higher education. AI in higher education encompasses a wide range of applications designed to enhance teaching methodologies, streamline administrative processes, and personalize learning experiences. The transformative potential of AI lies in its ability to process vast amounts of data, identify patterns, and make intelligent decisions, which can significantly improve educational outcomes. AI-Driven Learning and Engagement in Higher Education provides a comprehensive exploration of these themes and offers insights into the theoretical foundations, practical applications, and ethical implications of AI in education. Each chapter delves into specific aspects of AI integration, from personalized learning and intelligent tutoring systems to administrative automation and ethical considerations. Covering topics such as applied artificial intelligence, online learning, and student success, this book is an excellent resource for educators, administrators, policymakers, researchers, academicians, and more.