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The nature of technology has changed since Artificial Intelligence in Education (AIED) was conceptualized as a research community and Interactive Learning Environments were initially developed.
As explored in this open access book, higher education in STEM fields is influenced by many factors, including education research, government and school policies, financial considerations, technology limitations, and acceptance of innovations by faculty and students. In 2018, Drs. Ryoo and Winkelmann explored the opportunities, challenges, and future research initiatives of innovative learning environments (ILEs) in higher education STEM disciplines in their pioneering project: eXploring the Future of Innovative Learning Environments (X-FILEs). Workshop participants evaluated four main ILE categories: personalized and adaptive learning, multimodal learning formats, cross/extended reality (XR), and artificial intelligence (AI) and machine learning (ML). This open access book gathers the perspectives expressed during the X-FILEs workshop and its follow-up activities. It is designed to help inform education policy makers, researchers, developers, and practitioners about the adoption and implementation of ILEs in higher education.
"In a world where where online and offline overlap and coincide, this book presents how digital intelligence is a key competence for the future of education and looks at how AI and other digital tools are improving the world of education"--
This book addresses main issues concerned with the future learning, learning and academic analytics, virtual world and smart user interface, and mobile learning. This book gathers the newest research results of smart learning environments from the aspects of learning, pedagogies, and technologies in learning. It examines the advances in technology development and changes in the field of education that has been affecting and reshaping the learning environment. Then, it proposes that under the changed technological situations, smart learning systems, no matter what platforms (i.e., personal computers, smart phones, and tablets) they are running at, should be aware of the preferences and needs that their users (i.e., the learners and teachers) have, be capable of providing their users with the most appropriate services, helps to enhance the users' learning experiences, and to make the learning efficient.
These essays explore cognitively oriented empirical trials that use AI programming as a modeling methodology and that can provide valuable insight into a variety of learning problems. New perspectives and techniques are shaping the field of computer-aided instruction. These essays explore cognitively oriented empirical trials that use AI programming as a modeling methodology and that can provide valuable insight into a variety of learning problems. Drawing on work in cognitive theory, plan-based program recognition, qualitative reasoning, and cognitive models of learning and teaching, this exciting research covers a wide range of alternatives to tutoring dialogues. Contents Artificial Intelligence and Learning Environments, William J. Clancey, Elliot Soloway * Cognitive Modeling and Intelligence Tutoring, John R. Anderson, C. Franklin Boyle, Albert T. Corbett, Matthew W. Lewis * Understanding and Debugging Novice Programs, W. Lewis Johnson * Causal Model Progressions as a Foundation for Intelligent Learning Environments, Barbara Y. White and John R. Frederiksen
"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
At a time when ICTs are proliferating various facets of society and human interactivity, optimizing the use of these tools and technologies not only enhances learning but also transforms learning experiences all together, resulting in an increase of effectiveness and quality of education around the globe. As such, teachers are being challenged to implement a wide range of tools, such as mobile learning and augmented reality, to create smarter learning environments inside and outside of the classroom. Cases on Smart Learning Environments explores the potential of SLE tools for enhanced learning outcomes as experienced by educators, learners, and administrators from various learning institutions around the world. This publication presents cases on the real-world implementation of SLEs in 11 countries that span the continents of Asia, Africa, Europe, and North and South America. Featuring coverage on a broad range of topics such as learner engagement, teacher training, and intelligent agent technology, this book is ideally designed for academicians, instructors, instructional designers, librarians, educational stakeholders, and curriculum developers.
Constraint-based reasoning is an important area of automated reasoning in artificial intelligence, with many applications. These include configuration and design problems, planning and scheduling, temporal and spatial reasoning, defeasible and causal reasoning, machine vision and language understanding, qualitative and diagnostic reasoning, and expert systems. Constraint-Based Reasoning presents current work in the field at several levels: theory, algorithms, languages, applications, and hardware. Constraint-based reasoning has connections to a wide variety of fields, including formal logic, graph theory, relational databases, combinatorial algorithms, operations research, neural networks, truth maintenance, and logic programming. The ideal of describing a problem domain in natural, declarative terms and then letting general deductive mechanisms synthesize individual solutions has to some extent been realized, and even embodied, in programming languages. Contents Introduction, E. C. Freuder, A. K. Mackworth * The Logic of Constraint Satisfaction, A. K. Mackworth * Partial Constraint Satisfaction, E. C. Freuder, R. J. Wallace * Constraint Reasoning Based on Interval Arithmetic: The Tolerance Propagation Approach, E. Hyvonen * Constraint Satisfaction Using Constraint Logic Programming, P. Van Hentenryck, H. Simonis, M. Dincbas * Minimizing Conflicts: A Heuristic Repair Method for Constraint Satisfaction and Scheduling Problems, S. Minton, M. D. Johnston, A. B. Philips, and P. Laird * Arc Consistency: Parallelism and Domain Dependence, P. R. Cooper, M. J. Swain * Structure Identification in Relational Data, R. Dechter, J. Pearl * Learning to Improve Constraint-Based Scheduling, M. Zweben, E. Davis, B. Daun, E. Drascher, M. Deale, M. Eskey * Reasoning about Qualitative Temporal Information, P. van Beek * A Geometric Constraint Engine, G. A. Kramer * A Theory of Conflict Resolution in Planning, Q. Yang A Bradford Book.
It has quickly become apparent in the past year that online learning is not only an asset, but it is critical to the continued education of youth during times of crisis. However, districts and schools across the nation are in need of guidance and practical, research-backed approaches to distance and hybrid learning. The current COVID-19 crisis has demonstrated that effective learning in K-12 is possible, but many districts struggled and continue to struggle in achieving that reality. There is also the growing consensus that even if things “return to normal,” distance and blended learning strategies should continue to be employed in many ways across the K-12 environment. Designing Effective Distance and Blended Learning Environments in K-12 provides key insights into the ways that school districts and educators from across the world have effectively designed and implemented distance and blended learning approaches to enable and enhance student learning. The diverse collection of authors from various demographics and roles in school systems will benefit readers across a wide spectrum of school community stakeholders. There will also be an emphasis on how research and theory is put into practice, along with an honest discussion of what strategies and actions were successful as well as those that were less so. This book is essential for professionals and researchers working in the field of K-12 education, particularly superintendents, curriculum developers, professional learning designers, school principals, instructional technology specialists, and teachers, as well as administrators, researchers, academicians, and students interested in the effective practices being used in blended learning approaches.
Building Intelligent Interactive Tutors discusses educational systems that assess a student's knowledge and are adaptive to a student's learning needs. The impact of computers has not been generally felt in education due to lack of hardware, teacher training, and sophisticated software. and because current instructional software is neither truly responsive to student needs nor flexible enough to emulate teaching. Dr. Woolf taps into 20 years of research on intelligent tutors to bring designers and developers a broad range of issues and methods that produce the best intelligent learning environments possible, whether for classroom or life-long learning. The book describes multidisciplinary approaches to using computers for teaching, reports on research, development, and real-world experiences, and discusses intelligent tutors, web-based learning systems, adaptive learning systems, intelligent agents and intelligent multimedia. It is recommended for professionals, graduate students, and others in computer science and educational technology who are developing online tutoring systems to support e-learning, and who want to build intelligence into the system. - Combines both theory and practice to offer most in-depth and up-to-date treatment of intelligent tutoring systems available - Presents powerful drivers of virtual teaching systems, including cognitive science, artificial intelligence, and the Internet - Features algorithmic material that enables programmers and researchers to design building components and intelligent systems