This book delineates a comprehensive framework designed to ultimately assist organizations as they navigate the critical juncture—often termed the inflection point—in adopting generative Artificial Intelligence (AI). Addressing the exigent shift in organizational strategy mandated by Industry 4.0, and urging a transition from mere digital transformation to what is being progressively understood as "generative transformation,” it fills a conspicuous lacuna in both academic and practical discourses by presenting a timely synthesis of research, empirical studies, and case analyses. The authors utilize an interdisciplinary methodology, bridging the epistemological divide between the technological intricacies of generative AI and its effective implementation within institutional frameworks. Reorienting the focus of organizational leaders from a simplistic replacement paradigm to one of technological-human augmentation, it works toward a prescriptive blueprint for enabling organizations to adopt generative AI without compromising their most valuable asset: human intellectual and emotional capital. Fostering interdisciplinary dialog among academics, industry professionals, and policymakers, it fills a critical gap in current discourses and serves as a catalyst for future research and collaboration.

How might digital technology and notably smart technologies based on artificial intelligence (AI), learning analytics, robotics, and others transform education? This book explores such question. It focuses on how smart technologies currently change education in the classroom and the management of educational organisations and systems.

This book delineates a comprehensive framework designed to ultimately assist organizations as they navigate the critical juncture--often termed the inflection point--in adopting generative Artificial Intelligence (AI). Addressing the exigent shift in organizational strategy mandated by Industry 4.0, and urging a transition from mere digital transformation to what is being progressively understood as "generative transformation", it fills a conspicuous lacuna in both academic and practical discourses by presenting a timely synthesis of research, empirical studies, and case analyses. The authors utilise an interdisciplinary methodology, bridging the epistemological divide between the technological intricacies of generative AI and its effective implementation within institutional frameworks. Reorienting the focus of organizational leaders from a simplistic replacement paradigm to one of technological-human augmentation, it works towards a prescriptive blueprint for enabling organizations to adopt generative AI without compromising their most valuable asset: human intellectual and emotional capital. Fostering interdisciplinary dialogue among academics, industry professionals, and policymakers, it fills a critical gap in current discourses and serving as a catalyst for future research and collaboration.

Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]

Although there are numerous publications in the field of knowledge management (KM), there are still gaps in the literature regarding the aspects of KM that reflect new technology adoption and a deeper analysis discussing the interlinked process between KM and data analytics in business process improvement. It is essential for business leaders to understand the role and responsibilities of leaders for the adoption and consolidation of a KM system that is effective and profitable. Understanding, Implementing, and Evaluating Knowledge Management in Business Settings provides a comprehensive approach to KM concepts and practices in corporations and business organizations. Covering topics such as information overload, knowledge sharing adoption, and collective wisdom, this premier reference source is a comprehensive and essential resource for business executives, managers, IT specialists and consultants, libraries, students, entrepreneurs, researchers, and academicians.

Convergence of Cloud Computing, AI, and Agricultural Science explores the transformative potential of integrating cutting-edge technologies into the field of agriculture. With the rapid advancements in cloud computing, Artificial Intelligence (AI), and the Internet of Things (IoT), this research presents a comprehensive framework for monitoring agriculture farms remotely using a smart cloud-based system. The book delves into the application of AI-based machine learning models, such as the Support Vector Machine (SVM), to accurately classify and process the collected data. This advanced research reference book also explores how digital information can provide farmers with information about international markets, enabling them to make informed decisions regarding their crops. With its academic tone and in-depth exploration of cloud computing in smart agriculture, this book serves as an essential resource for researchers, academics, and professionals in the fields of agriculture, computer science, and environmental science. By examining the convergence of cloud computing, AI, and agricultural science, it provides a roadmap for harnessing technology to revolutionize farming practices and ensure sustainable agri-food systems in the digital era.

This tutorial book presents an augmented selection of the material presented at the Software Engineering Education and Training Track at the International Conference on Software Engineering, ICSE 2005, held in St. Louis, MO, USA in May 2005. The 12 tutorial lectures presented cover software engineering education, state of the art and practice: creativity and rigor, challenges for industries and academia, as well as future directions.