This book brings together the fields of artificial intelligence (often known as A.I.) and inclusive education in order to speculate on the future of teaching and learning in increasingly diverse social, cultural, emotional, and linguistic educational contexts. This book addresses a pressing need to understand how future educational practices can promote equity and equality, while at the same time adopting A.I. systems that are oriented towards automation, standardisation and efficiency. The contributions in this edited volume appeal to scholars and students with an interest in forming a critical understanding of the development of A.I. for education, as well as an interest in how the processes of inclusive education might be shaped by future technologies. Grounded in theoretical engagement, establishing key challenges for future practice, and outlining the latest research, this book offers a comprehensive overview of the complex issues arising from the convergence of A.I. technologies and the necessity of developing inclusive teaching and learning. To date, there has been little in the way of direct association between research and practice in these domains: A.I. has been a predominantly technical field of research and development, and while intelligent computer systems and 'smart' software are being increasingly applied in many areas of industry, economics, social life, and education itself, a specific engagement with the agenda of inclusion appears lacking. Although such technology offers exciting possibilities for education, including software that is designed to 'personalise' learning or adapt to learner behaviours, these developments are accompanied by growing concerns about the in-built biases involved in machine learning techniques driven by 'big data'.

This book delves into the various methods of constructing postdigital research, with a particular focus on the postdigital dynamic of inclusion and exclusion, as well as the interplay between method and emancipation. By answering three fundamental questions - the relationship between postdigital theory and research practice, the relationship between method and emancipation, and how to construct emancipatory postdigital research - the book serves as a comprehensive resource for those interested in conducting postdigital research. Constructing Postdigital Research: Method and Emancipation is complemented by Postdigital Research: Genealogies, Challenges, and Future Perspectives, also edited by Petar Jandrić, Alison MacKenzie, and Jeremy Knox, which explores these questions in theory.

This book explores genealogies and the challenges related to the concept of the postdigital, the ambiguous nature of postdigital knowledges, and the many faces of postdigital sensibilities. The book answers three key questions: What is postdigital knowledge? What does it mean to do postdigital research? What, if anything, is distinct from research conducted in other perspectives? As such, this book is a one-stop publication for those interested in the theory of postdigital research. Postdigital Research: Genealogies, Challenges, and Future Perspectives is complemented by Constructing Postdigital Research: Method and Emancipation, also edited by Petar Jandrić, Alison MacKenzie, and Jeremy Knox, which explores these questions in practice.

This book explores the relationships between artificial intelligence (AI) and education in China. It examines educational activity in the context of profound technological interventions, far-reaching national policy, and multifaceted cultural settings. By standing at the intersection of three foundational topics: AI and the recent proliferation of data-driven technologies; education, the most foundational of our social institutions in terms of actively shaping societies and individuals; and finally, China, which is a frequent subject for dramatic media reports about both technology and education, this book offers an insightful view of the contexts that underpin the use of AI in education, and promotes a more in-depth understanding of China. Scholars of educational technology and digital education will find this book an indispensable guide to the ways new technologies are imagined to transform the future, while being firmly grounded in the past.

Posthumanism and the Massive Open Online Course critiques the problematic reliance on humanism that pervades online education and the MOOC, and explores theoretical frameworks that look beyond these limitations. While MOOCs (massive open online courses) have attracted significant academic and media attention, critical analyses of their development have been rare. Following an overview of MOOCs and their corporate means of promotion, this book unravels the tendencies in research and theory that continue to adopt normative views of user access, participation, and educational space in order to offer alternatives to the dominant understandings of community and authenticity in education.

Data Justice and the Right to the City engages with theories of social justice and data-driven urbanism. It explores the intersecting concerns of data justice both the harms and civic possibilities of the datafied society and the right to the city a call to redress the uneven distribution of resources and rights in urban contexts.The book addresses these concerns through a variety of topics, including digital social services, as cities use data and automated systems to administer to citizens; education, as data-driven practices transform learning and higher education; labour, as platforms create new precarities and risks for workers; and activists and artists who seek to make creative and political interventions. They propose frameworks for understanding how data-driven technologies affect citizens' rights at the municipal scale and offer strategies for intervention by both scholars and citizens.

Posthumanism and the Massive Open Online Course critiques the problematic reliance on humanism that pervades online education and the MOOC, and explores theoretical frameworks that look beyond these limitations. While MOOCs (massive open online courses) have attracted significant academic and media attention, critical analyses of their development have been rare. Following an overview of MOOCs and their corporate means of promotion, this book unravels the tendencies in research and theory that continue to adopt normative views of user access, participation, and educational space in order to offer alternatives to the dominant understandings of community and authenticity in education.

This book brings together the fields of artificial intelligence (often known as A.I.) and inclusive education in order to speculate on the future of teaching and learning in increasingly diverse social, cultural, emotional, and linguistic educational contexts. This book addresses a pressing need to understand how future educational practices can promote equity and equality, while at the same time adopting A.I. systems that are oriented towards automation, standardisation and efficiency. The contributions in this edited volume appeal to scholars and students with an interest in forming a critical understanding of the development of A.I. for education, as well as an interest in how the processes of inclusive education might be shaped by future technologies. Grounded in theoretical engagement, establishing key challenges for future practice, and outlining the latest research, this book offers a comprehensive overview of the complex issues arising from the convergence of A.I. technologies and the necessity of developing inclusive teaching and learning. To date, there has been little in the way of direct association between research and practice in these domains: A.I. has been a predominantly technical field of research and development, and while intelligent computer systems and 'smart' software are being increasingly applied in many areas of industry, economics, social life, and education itself, a specific engagement with the agenda of inclusion appears lacking. Although such technology offers exciting possibilities for education, including software that is designed to 'personalise' learning or adapt to learner behaviours, these developments are accompanied by growing concerns about the in-built biases involved in machine learning techniques driven by 'big data'.

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