Handbook Of Artificial Intelligence In Education

Gathering insightful and stimulating contributions from leading global experts in Artificial Intelligence in Education (AIED), this comprehensive Handbook traces the development of AIED from its early foundations in the 1970s to the present day. The Handbook evaluates the use of AI techniques such as modelling in closed and open domains, machine learning, analytics, language understanding and production to create systems aimed at helping learners, teachers, and educational administrators. Chapters examine theories of affect, metacognition and pedagogy applied in AIED systems; foundational aspects of AIED architecture, design, authoring and evaluation; and collaborative learning, the use of games and psychomotor learning. It concludes with a critical discussion of the wider context of Artificial Intelligence in Education, examining its commercialisation, social and political role, and the ethics of its systems, as well as reviewing the possible challenges and opportunities for AIED in the next 20 years. Providing a broad yet detailed account of the current field of Artificial Intelligence in Education, researchers and advanced students of education technology, innovation policy, and university management will benefit from this thought-provoking Handbook. Chapters will also be useful to support undergraduate courses in AI, computer science, and education.

This book systematically reviews a broad range of cases in education that utilize cutting-edge AI technologies. Furthermore, it introduces readers to the latest findings on the scope of AI in education, so as to inspire researchers from non-technological fields (e.g. education, psychology and neuroscience) to solve education problems using the latest AI techniques. It also showcases a number of established AI systems and products that have been employed for education. Lastly, the book discusses how AI can offer an enabling technology for critical aspects of education, typically including the learner, content, strategy, tools and environment, and what breakthroughs and advances the future holds. The book provides an essential resource for researchers, students and industrial practitioners interested and engaged in the fields of AI and education. It also offers a convenient handbook for non-professional readers who need a primer on AI in education, and who want to gain a deeper understanding of emerging trends in this domain.

This book includes a collection of expanded papers from the 2019 Sino-German Symposium on AI-supported educational technologies, which was held in Wuhan, China, March, 2019. The contributors are distinguished researchers from computer science and learning science. The contributions are organized in four sections: (1) Overviews and systematic perspectives , (2) Example Systems, (3) Algorithms, and (4) Insights gained from empirical studies. For example, different data mining and machine learning methods to quantify different profiles of a learner in different learning situations (including interaction patterns, cognitive modes, knowledge skills, interests and emotions etc.) as well as connections to measurements in psychology and learning sciences are discussed in the chapters.

"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 quest for building an artificial brain developed in the fields of computer science and psychology. Artificial intelligence (AI), sometimes called machine intelligence, refers to intelligence demonstrated by machines, while the natural intelligence is the intelligence displayed by humans and animals. Typically, AI systems demonstrate at least some of the following human behaviors: planning, learning, reasoning, problem solving, knowledge representation, perception, speech recognition, decision-making, language translation, motion, manipulation, intelligence, and creativity. Artificial intelligence is an emerging technology which the educational sector can benefit from. In this book, we consider the applications of AI in key areas of education. Artificial intelligence in education (AIED) refers to the application of AI technologies in educational settings to facilitate teaching, learning, or decision making. AI will impact the education field in the areas of administration, instruction, and personalized, and individualized learning applications. In this book, AI is specifically applied in the following key educational sectors: education, natural sciences, social sciences, computer science, engineering, business, and medicine.

The global adoption of technology in education is transforming the way we teach and learn. Artificial Intelligence is one of the disruptive techniques to customize the experience of different learning groups, teachers, and tutors. This book offers knowledge in intelligent teaching/learning systems, and advances in e-learning and assessment systems. The book highlights the broad field of artificial intelligence applications in education, regarding any type of artificial intelligence that is correlated with education. It discusses learning methodologies, intelligent tutoring systems, intelligent student guidance and assessments, intelligent education chatbots, and artificial tutors and presents the practicality and applicability implications of AI in education. The book offers new and current research along with case studies showing the latest techniques and educational activities. The book will find interest with academicians which includes teachers, students of various disciplines, higher education policymakers who believe in transforming the education industry, and research scholars who are pursuing their Ph.D. or Post Doc. in the field of Education Technology, Education, and Learning, etc. and those working in the area of Education Technology and Artificial Intelligence such industry professionals in education management and e-learning companies.

The Handbook of Artificial Intelligence, Volume II focuses on the improvements in artificial intelligence (AI) and its increasing applications, including programming languages, intelligent CAI systems, and the employment of AI in medicine, science, and education. The book first elaborates on programming languages for AI research and applications-oriented AI research. Discussions cover scientific applications, teiresias, applications in chemistry, dependencies and assumptions, AI programming-language features, and LISP. The manuscript then examines applications-oriented AI research in medicine and education, including ICAI systems design, intelligent CAI systems, medical systems, and other applications of AI to education. The manuscript explores automatic programming, as well as the methods of program specification, basic approaches, and automatic programming systems. The book is a valuable source of data for computer science experts and researchers interested in conducting further research in artificial intelligence.

Focuses on the parameters of remote learning, machine learning, deep learning, and artificial intelligence under 21st-century learning and instructional contexts. Topics covered include data coding and social networking technology.