This book provides a theoretical framework to allow educators, researchers, and policymakers to better understand computer‐generated writing and the policy and pedagogical implications of generative AI. Generative AI, such as ChatGPT and Gemini, has substantially disrupted educational spaces, forcing educators, policymakers, and other stakeholders to reconsider writing and how it should be used in education. Responding to this disruption, this book provides technically sound guidance on how various stakeholders should engage with generative AI. After providing a foundational and technical discussion of the technology, this book directly addresses the educational context. Informed by theories of learning and knowledge transfer and utilizing rhetorical theories of writing, this book assesses the impact of AI on student learning, student performance, and academic honesty and integrity. In doing so, the book outlines how generative AI can be both a help and a hindrance for students, enabling readers to craft informed and meaningful policies and successfully integrate AI in the composition classroom. This book will be of interest to scholars in the fields of Rhetoric and Composition, Technical Writing, Communication Studies, Linguistics, and TESOL, as well as to Education and Machine Learning policymakers, program directors, and researchers.

Generative AI in Writing Education provides technically sound guidance on how various stakeholders should engage with generative AI and outlines how generative AI can be both a help and hindrance for students, enabling readers to craft informed and meaningful policy and successfully integrate AI in the composition classroom.

Designed to cater to the needs of both novice and seasoned writing instructors, this book provides a range of practical and adaptable strategies for integrating generative artificial intelligence (AI) into English writing curricula. Generative AI in the English Composition Classroom proposes strategic methodologies to ensure that AI is utilized as a facilitator of learning and creativity, rather than as a shortcut to academic success. With a particular emphasis on sophisticated large language models such as Claude, ChatGPT, and Gemini, this book critically addresses potential challenges, including concerns related to academic integrity. It includes case studies and practical strategies to exemplify how AI can enhance the writing process while emphasizing the continuing importance of a solid foundation in writing structure, processes, and rhetorical strategies. These case studies and strategies are designed for immediate application, offering educators and students practical tools to effectively navigate AI-augmented writing environments. Finally, the book looks to the future, discussing the evolving skillsets required in the workforce and how educators can equip students for a future in which AI is an integral component. A forward-thinking and invaluable guide, this book will be of interest to educators involved in teaching English Composition and writing.

Using Generative AI Effectively in Higher Education explores how higher education providers can realise their role and responsibility in harnessing the power of generative artificial intelligence (GenAI) ethically and sustainably. This rich collection of established and evaluated practices from across global higher education offers a practical guide to leading an agile institutional response to emerging technologies, building critical digital literacy across an entire institution, and embedding the ethical and sustainable use of GenAI in teaching, learning, and assessment. Including reflections from stakeholders testifying to the value of the approaches outlined, the book examines how higher education can equip staff and students with the critical-digital literacy necessary to use GenAI in work, study, and social life responsibly and with integrity. It provides an evidence-based resource for any kind of higher education (HE) provider (modern, college-based, and research-focused) looking for inspiration and approaches which can build GenAI capability and includes chapters on the development of cross-institutional strategy, policies and processes, pedagogic practices, and critical-digital literacy. This resource will be invaluable to educational leaders, educational developers, learning developers, learning technologists, course administrators, quality assurance staff, and HE teachers wishing to embrace and adapt to a GenAI-enabled world.

Many educators in the realm of higher education face the critical challenge of fostering creativity in students using traditional teaching methods. In today's rapidly evolving world, these methods have become inadequate to nurture the innovative thinking demanded by modern society. Impacts of Generative AI on Creativity in Higher Education reveals a solution in the integration of generative AI into higher education. To revolutionize how we nurture and harness student creativity, the book explores the intersection of creativity, generative AI, and higher education with a fresh perspective and practical guidance for educators and institutions. It delves into the fundamental concepts of generative AI and its potential applications, providing educators with the tools to create more engaging and innovative learning environments.

Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Generative AI in Teaching and Learning delves into the revolutionary field of generative artificial intelligence and its impact on education. This comprehensive guide explores the multifaceted applications of generative AI in both formal and informal learning environments, shedding light on the ethical considerations and immense opportunities that arise from its implementation. From the early approaches of utilizing generative AI in teaching to its integration into various facets of learning, this book offers a profound analysis of its potential. Teachers, researchers, instructional designers, developers, data analysts, programmers, and learners alike will find valuable insights into harnessing the power of generative AI for educational purposes.