The general theme of this book is to present the applications of artificial intelligence (AI) in test development. In particular, this book includes research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing. By utilizing artificial intelligence, the efficiency of item development, test form construction, test delivery, and scoring could be dramatically increased. Chapters on automated item generation offer different perspectives related to generating a large number of items with controlled psychometric properties including the latest development of using machine learning methods. Automated scoring is illustrated for different types of assessments such as speaking and writing from both methodological aspects and practical considerations. Further, automated test assembly is elaborated for the conventional linear tests from both classical test theory and item response theory perspectives. Item pool design and assembly for the linear-on-the-fly tests elaborates more complications in practice when test security is a big concern. Finally, several chapters focus on computerized adaptive testing (CAT) at either item or module levels. CAT is further illustrated as an effective approach to increasing test-takers’ engagement in testing. In summary, the book includes both theoretical, methodological, and applied research and practices that serve as the foundation for future development. These chapters provide illustrations of efforts to automate the process of test development. While some of these automation processes have become common practices such as automated test assembly, automated scoring, and computerized adaptive testing, some others such as automated item generation calls for more research and exploration. When new AI methods are emerging and evolving, it is expected that researchers can expand and improve the methods for automating different steps in test development to enhance the automation features and practitioners can adopt quality automation procedures to improve assessment practices.

Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. Summarizes AI advances for use in mental health practice Includes advances in AI based decision-making and consultation Describes AI applications for assessment and treatment Details AI advances in robots for clinical settings Provides empirical data on clinical efficacy Explores practical issues of use in clinical settings

While many facets of our lives are rapidly becoming more digital, educational institutions are now faced with the task of finding new and innovative ways to incorporate technology into the classroom. Examining the latest trends in digital tools provides a more effective learning environment for future generations. The Handbook of Research on Digital Content, Mobile Learning, and Technology Integration Models in Teacher Education is a pivotal scholarly reference source that outlines the most efficient ways for educators to employ technology-enhanced lesson plans in their classroom. Featuring pertinent topics that include blended learning environments, student engagement, artificial intelligence, and learner-centered pedagogy, this is an ideal resource for educators, aspiring teachers, and researchers that are interested in discovering recent trends and techniques related to digital learning environments and technology-enhanced classrooms.

This book is the first to explore the big question of how assessment can be refreshed and redesigned in an evolving digital landscape. There are many exciting possibilities for assessments that contribute dynamically to learning. However, the interface between assessment and technology is limited. Often, assessment designers do not take advantage of digital opportunities. Equally, digital innovators sometimes draw from models of higher education assessment that are no longer best practice. This gap in thinking presents an opportunity to consider how technology might best contribute to mainstream assessment practice. Internationally recognised experts provide a deep and unique consideration of assessment’s contribution to the technology-mediated higher education sector. The treatment of assessment is contemporary and spans notions of ‘assessment for learning’, measurement and the roles of peer and self within assessment. Likewise the view of educational technology is broad and includes gaming, learning analytics and new media. The intersection of these two worlds provides opportunities, dilemmas and exemplars. This book serves as a reference for best practice and also guides future thinking about new ways of conceptualising, designing and implementing assessment.

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data

This book collects papers on education quality assessment based on AI technology and introduces the latest research direction and progress of AI technology in the field of education and teaching, including classroom teaching quality assessment, online education quality assessment, teaching reflection quality assessment, etc. This book promotes the application of artificial intelligence technology in the field of education and teaching, effectively improving the quality of education and teaching. Researchers in artificial intelligence technology, teachers, students, and others benefit from this book.

Artificial Intelligence: Models, Algorithms and Applications presents focused information about applications of artificial intelligence (AI) in different areas to solve complex problems. The book presents 8 chapters that demonstrate AI based systems for vessel tracking, mental health assessment, radiology, instrumentation, business intelligence, education and criminology. The book concludes with a chapter on mathematical models of neural networks. The book serves as an introductory book about AI applications at undergraduate and graduate levels and as a reference for industry professionals working with AI based systems.