As classrooms move from chalkboards to digital platforms, there's a critical juncture where the potential of AI intersects with the future of academia. The problem is multifaceted — it involves the nature of pedagogy, the demand for personalized learning experiences, and the ethical considerations inherent in deploying AI technologies. Educators, researchers, and administrators face the challenge of navigating this transformation while ensuring inclusivity, fairness, and ethical practice. Integrating AI in higher education risks becoming a chaotic endeavor without a comprehensive understanding of the opportunities, challenges, and ethical dimensions. The lack of a strategic approach could lead to biases, privacy concerns, and a digital divide exacerbating educational inequalities. Enhancing Higher Education and Research With OpenAI Models explores the intersection of artificial intelligence and higher education, focusing on the social sciences. A collaborative team of academics and AI expert's analyses aims to illuminate the transformative potential of integrating AI technologies into traditional educational settings. The book unravels the rich tapestry of the history of higher education in the social sciences, tracing the evolution from conventional blackboards to the modern digital landscape. It meticulously examines the increasing integration of technology in classrooms. It sets the stage for the impact of AI-driven tools and data analytics on pedagogy, personalized learning experiences, and broader access to education.

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]

Higher education faces the ongoing challenge of engaging students in successful lifelong learning despite rapidly evolving environments. Traditional teaching methods need to be revised to prepare students for the complexities of modern society, as they currently leave a gap between theory and practice. To address this dilemma, Implementing Interactive Learning Strategies in Higher Education offers a comprehensive framework and the latest empirical research to help educators address students' diverse learning needs. The book focuses on active and interactive learning strategies, cognitive and metacognitive approaches, and social-emotional learning techniques. It provides practical insights for educators across disciplines, including adaptive learning strategies and innovative digital engagement methods.

The isolated nature of academic disciplines restricts the potential for collaborative problem-solving and innovative approaches to teaching and learning. This fragmentation limits the effectiveness of educational outcomes and fails to prepare students for the multidimensional demands of the modern world. To address this pressing issue, Transdisciplinary Approaches to Learning Outcomes in Higher Education provides a comprehensive framework for transdisciplinary research in higher education. This book offers a systematic approach to transdisciplinary research and equips researchers, educators, and policymakers with the tools needed to break down disciplinary barriers and foster interdisciplinary collaboration. Through a series of case studies, theoretical frameworks, and practical guidelines, the book demonstrates how transdisciplinary approaches can lead to more effective teaching practices, curriculum design, and student engagement. It highlights the importance of integrating digital transformation, creativity, communication skills, and information technology tools to enhance learning outcomes in higher education.

"The landscape for education has been rapidly changing in the last years: demographic changes affecting the makeup of families, multiple school options available to children, wealth disparities, the global economy demanding new skills from workers, and continued breakthroughs in technology are some of the factors impacting education. Given these changes, how can schools continue to prepare students for the future? In a world where information is readily available online, how can schools continue to be relevant? The emergence of Artificial Intelligence (AI) has exacerbated the need to have these conversations. Its impact on education and the multiple possibilities that it offers are putting pressure on educational leaders to reformulate the school curriculum and the channels to deliver it. The book "Artificial Intelligence in Education, Promises and Implications for Teaching and Learning" by the Center for Curriculum Redesign immerses the reader in a discussion on what to teach students in the era of AI and examines how AI is already demanding much needed updates to the school curriculum, including modernizing its content, focusing on core concepts, and embedding interdisciplinary themes and competencies with the end goal of making learning more enjoyable and useful in students' lives. The second part of the book dives into the history of AI in education, its techniques and applications -including the way AI can help teachers be more effective, and finishes on a reflection about the social aspects of AI. This book is a must-read for educators and policy-makers who want to prepare schools to face the uncertainties of the future and keep them relevant." --Amada Torres, VP, Studies, Insights, and Research, National Association of Independent School (NAIS) "The rapid advances in technology in recent decades have already brought about substantial changes in education, opening up new opportunities to teach and learn anywhere anytime and providing new tools and methods to improve learning outcomes and support innovative teaching and learning.Research into artificial intelligence and machine learning in education goes back to the late 1970s. Artificial intelligence methods were generally employed in two ways: to design and facilitate interactive learning environments that would support learning by doing, and to design and implement tutoring systems by adapting instructions with respect to the students' knowledge state.But this is just the beginning. As Artificial Intelligence in Education shows, AI is increasingly used in education and learning contexts. The collision of three areas - data, computation and education - is set to have far-reaching consequences, raising fundamental questions about the nature of education: what is taught and how it is taught. Artificial Intelligence in Education is an important, if at times disturbing, contribution to the debate on AI and provides a detailed analysis on how it may affect the way teachers and students engage in education. The book describes how artificial intelligence may impact on curriculum design, on the individualisation of learning, and on assessment, offering some tantalising glimpses into the future (the end of exams, your very own lifelong learning companion) while not falling victim to tech-hype. The enormous ethical, technical and pedagogical challenges ahead are spelt out, and there is a real risk that the rapid advances in artificial intelligence products and services will outstrip education systems' capacity to understand, manage and integrate them appropriately. As the book concludes: "We can either leave it to others (the computer scientists, AI engineers and big tech companies) to decide how artificial intelligence in education unfolds, or we can engage in productive dialogue."I commend this book to anyone concerned with the future of education in a digital world." --Marc Durando, Executive Director, European Schoolnet

The Handbook of Research on Innovative Frameworks and Inclusive Models for Online Learning is edited by Jared Keengwe, an experienced professor in Curriculum Design and Instruction. This comprehensive reference guide offers academic scholars a collection of diverse frameworks from empirical studies, literature reviews, and case studies related to inclusive models for online learning. Covering a wide range of topics, including pedagogical adaptations to online learning, innovative pedagogical theories, inclusive teaching and learning, and best practices in online course design, the handbook provides practical insights to achieve effective pedagogical outcomes grounded on sound theoretical frameworks. Whether you are an online educator, instructional designer, teacher educator, librarian, student, online learning researcher, or educational manager, this handbook can serve as a valuable resource to guide your research, design, and practice in online learning. The Handbook of Research on Innovative Frameworks and Inclusive Models for Online Learning is a must-read for academic scholars who want to stay updated on the latest research, theories, and models for effective online education.

In modern finance, academic scholars face a pressing dilemma - how to grapple with the sweeping influence of Information and Communication Technology (ICT). As ICT continues to transform financial practices, transactions, and data analysis, scholars are confronted with the daunting challenge of comprehending and navigating this rapidly changing terrain. Moreover, the disruptive potential of ICT introduces a range of intricate issues, including cybersecurity vulnerabilities, ethical quandaries, and privacy concerns. In the absence of a comprehensive guide, scholars find themselves adrift, searching for direction amidst the tumultuous waves of the digital revolution. Transforming the Financial Landscape With ICTs is a beacon of knowledge amid the turbulence of ICT-driven financial transformation. This meticulously curated book presents a vast array of research papers and case studies that delve deep into the multifaceted impact of ICT on the finance sector. It not only illuminates the path forward but also underscores the positive potential of embracing ICT as a catalyst for progress, championing efficiency, transparency, and inclusivity within finance.

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

The surge in enrollment for online courses is continually increasing. However, beneath the convenience lies a challenge that demands a resolution. Educators, administrators, and instructional designers must ensure that the human element is not lost in the virtual corridors of learning. Students with diverse backgrounds and learning needs require more than a simple virtual classroom. Research reveals a concerning trend: high attrition rates in online courses, often attributed to a lack of engagement and insufficient human interaction. To reverse this trend, deliberate measures must be taken to humanize the online learning environment. This book provides several solutions, offering an array of tools and strategies to promote engagement and infuse the human touch into online spaces. To confront this multi-layered challenge, it becomes paramount to undertake deliberate measures aimed at humanizing the online learning environment. Humanizing Online Teaching and Learning in Higher Education steps forward as a guide, offering an extensive array of tools and strategies meticulously crafted to foster student engagement and infuse the essential human touch into the digital educational landscape.

This book, "AI-Powered Productivity," aims to provide a guide to understanding, utilizing AI and generative tools in various professional settings. The primary purpose of this book is to offer readers a deep dive into the concepts, tools, and practices that define the current AI landscape. From foundational principles to advanced applications, this book is structured to cater to both beginners and professionals looking to enhance their knowledge and skills in AI. This book is divided into nine chapters, each focusing on a specific aspect of AI and its practical applications: Chapter 1 introduces the basic concepts of AI, its impact on various sectors, and key factors driving its rapid advancement, along with an overview of generative AI tools. Chapter 2 delves into large language models like ChatGPT, Google Gemini, Claude, Microsoft's Turing NLG, and Facebook's BlenderBot, exploring their integration with multimodal technologies and their effects on professional productivity. Chapter 3 offers a practical guide to mastering LLM prompting and customization, including tutorials on crafting effective prompts and advanced techniques, as well as real-world examples of AI applications. Chapter 4 examines how AI can enhance individual productivity, focusing on professional and personal benefits, ethical use, and future trends. Chapter 5 addresses data-driven decision- making, covering data analysis techniques, AI in trend identification, consumer behavior analysis, strategic planning, and product development. Chapter 6 discusses strategic and ethical considerations of AI, including AI feasibility, tool selection, multimodal workflows, and best practices for ethical AI development and deployment. Chapter 7 highlights the role of AI in transforming training and professional development, covering structured training programs, continuous learning initiatives, and fostering a culture of innovation and experimentation. Chapter 8 provides a guide to successfully implementing AI in organizations, discussing team composition, collaborative approaches, iterative development processes, and strategic alignment for AI initiatives. Finally, Chapter 9 looks ahead to the future of work, preparing readers for the AI revolution by addressing training and education, career paths, common fears, and future trends in the workforce. The primary audience for the book is professionals seeking to enhance productivity and organizations or businesses. For professionals, the book targets individuals from various industries, reflecting its aim to reach a broad audience across different professional fields. It is designed for employees at all levels, offering valuable insights to both newcomers to AI and seasoned professionals. Covering a range of topics from foundational concepts to advanced applications, the book is particularly relevant for those interested in improving efficiency, with a strong emphasis on practical applications and productivity tools to optimize work processes. For organizations and businesses, the book serves as a valuable resource for decision-makers and managers, especially with chapters on data-driven decision-making, strategic considerations, and AI implementation. HR and training professionals will find the focus on AI in training and development beneficial for talent management, while IT and technology teams will appreciate the information on AI tools and concepts.