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Artificial Intelligence and Inclusive Education - Speculative Futures and Emerging Practices (Hardcover, 1st ed. 2019)
Loot Price: R4,319
Discovery Miles 43 190
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Artificial Intelligence and Inclusive Education - Speculative Futures and Emerging Practices (Hardcover, 1st ed. 2019)
Series: Perspectives on Rethinking and Reforming Education
Expected to ship within 12 - 19 working days
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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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