Smart Future Teaching Digital Media And Inclusion

This book is about the promotion and development of digital solutions for inclusive education, including a variety of hardware, software, digital learning materials, and digital learning content currently available on the market. All of these technological solutions serve as support materials and building blocks for inclusive learning environments but, at the same time, can involve hidden risks which may inadvertently create even greater gaps in inclusive education. This book provides strategies and methodologies that promote the development of opportunities for using digital technologies to support inclusive education. It provides an, understanding how to close the current digital gap while ensuring that the digital technologies selected do not support new risks of exclusion from the digital learning environment, strengthening and augmenting the already existing digital divide.

Digital technologies play a significant role in the popular imagination about the future of education, as they are a prominent aspect of modern education provision and practice across the globe. Due to the increased adoption of digital education materials during the COVID-19 pandemic, the pedagogical significance of digital technology has been amplified. Advancing Equity and Inclusion Through Educational Technology builds upon the available literature in equity and inclusion through educational technology while providing further research opportunities in this dynamic and growing field. It provides the opportunity for reflection on this crucial issue by increasing the understanding of the importance of inclusion and equity in the context of educational improvements and providing relevant academic work, empirical research findings, and an overview of this relevant field of study. Covering topics such as sustainable inclusion learning, virtual school press programs, and generic skills, this major reference work is a comprehensive and timely resource for educators and administrators of both K-12 and higher education, government officials, pre-service teachers, teacher educators, librarians, researchers, and academicians.

The interwoven futures of humanity and our planet are under threat. Urgent action, taken together, is needed to change course and reimagine our futures.

This cutting-edge overview explores big data and the related topic of computer code, examining the implications for education and schooling for today and the near future.

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’.