Advances in Artificial Intelligence in Education – serie

This book addresses the implications of artificial intelligence for teaching, learning and academic integrity in higher education. It explores policies about the use of Generative Artificial Intelligence (GenAI), describes how to teach writing in the era of GenAI, and how instructors can design courses and assessments that prevent plagiarism while building the necessary skills for critical thinking and writing. Together, the chapters include research results, case studies, teaching methodologies, course design ideas, analysis of power and gatekeeping, and best practices related to GAI from a diverse range of researchers from English and French Canada, the United States, England, Ukraine and Croatia. The authors approach the advent and rapid spread of GenAI in higher education by examining its use from different perspectives with a particular focus on its impact on academic integrity. Taking a communication studies approach, consideration is given to the role GenAI might play disrupting power structures in universities to improve access for students who are non-traditional or English Language Learners. The book also explores how reimagining teaching methodologies can help to mitigate academic integrity violations due to misuse of GenAI and to teach students to use GenAI with integrity as a research and brainstorming tool. Students need to learn how to assess the reliability of GenAI’s output as the develop the skills for research and writing. Methods of teaching writing and research skills using GenAI are explored in an effort to ensure that critical thinking skills are developed successfully. Most instructors who use writing-intensive assessments believe that essential critical thinking skills are developed via the writing process; often, ideas become concrete as one writes about them. Teaching with GenAI can provide opportunities for instructors to guide their students into a deeper analysis and critique of their research.

This book addresses the implications of artificial intelligence for teaching, learning and academic integrity in higher education. It explores policies about the use of Generative Artificial Intelligence (GenAI), describes how to teach writing in the era of GenAI, and how instructors can design courses and assessments that prevent plagiarism while building the necessary skills for critical thinking and writing. Together, the chapters include research results, case studies, teaching methodologies, course design ideas, analysis of power and gatekeeping, and best practices related to GAI from a diverse range of researchers from English and French Canada, the United States, England, Ukraine and Croatia. The authors approach the advent and rapid spread of GenAI in higher education by examining its use from different perspectives with a particular focus on its impact on academic integrity. Taking a communication studies approach, consideration is given to the role GenAI might play disrupting power structures in universities to improve access for students who are non-traditional or English Language Learners. The book also explores how reimagining teaching methodologies can help to mitigate academic integrity violations due to misuse of GenAI and to teach students to use GenAI with integrity as a research and brainstorming tool. Students need to learn how to assess the reliability of GenAI’s output as the develop the skills for research and writing. Methods of teaching writing and research skills using GenAI are explored in an effort to ensure that critical thinking skills are developed successfully. Most instructors who use writing-intensive assessments believe that essential critical thinking skills are developed via the writing process; often, ideas become concrete as one writes about them. Teaching with GenAI can provide opportunities for instructors to guide their students into a deeper analysis and critique of their research.

This book provides a comprehensive exploration of the integration of artificial intelligence (AI) into academic research, specifically tailored for higher education institutions and postgraduate research (PGR) students. It addresses the unique challenges and opportunities that these institutions and students encounter when incorporating AI into research. The work emphasises practical case studies, step-by-step guides on AI tools and techniques, ethical considerations in AI usage, and features contributions from experts across various disciplines. Following the introduction, the book delves into the specifics of how AI can enhance academic research such as literature review, data analysis and interpretation, and assistance in academic writing across different disciplines. The wide range of topics introduced in this book is supported by practical examples and guidance. This book also explores the landscape of current AI applications in research, the methodologies for effectively leveraging AI technologies, and the critical ethical dimensions of AI work. The importance of interdisciplinary collaboration in expanding the use of AI in research is covered in this book by drawing on expert insights to provide a rich, multifaceted understanding of the potential of AI in academia. The combination of topics in this book can empower PGR students to navigate the complexities of AI in their research. The book is a much-needed compilation prepared by leading scholars in the field of digital technology to help PGRs, as well as decision-makers, determine the best ways to integrate and use AI tools in research.

This book provides a comprehensive exploration of the integration of artificial intelligence (AI) into academic research, specifically tailored for higher education institutions and postgraduate research (PGR) students. It addresses the unique challenges and opportunities that these institutions and students encounter when incorporating AI into research. The work emphasises practical case studies, step-by-step guides on AI tools and techniques, ethical considerations in AI usage, and features contributions from experts across various disciplines. Following the introduction, the book delves into the specifics of how AI can enhance academic research such as literature review, data analysis and interpretation, and assistance in academic writing across different disciplines. The wide range of topics introduced in this book is supported by practical examples and guidance. This book also explores the landscape of current AI applications in research, the methodologies for effectively leveraging AI technologies, and the critical ethical dimensions of AI work. The importance of interdisciplinary collaboration in expanding the use of AI in research is covered in this book by drawing on expert insights to provide a rich, multifaceted understanding of the potential of AI in academia. The combination of topics in this book can empower PGR students to navigate the complexities of AI in their research. The book is a much-needed compilation prepared by leading scholars in the field of digital technology to help PGRs, as well as decision-makers, determine the best ways to integrate and use AI tools in research.

This book examines the ongoing emergence of generative artificial intelligence (GenAI)  in education. It focuses both on the potential and effects of GenAI and on its limitations, risks and challenges. Particular emphasis is placed on the impact that GenAI has on pedagogy and knowledge assessment across a range of educational fields and disciplines, as well as on teacher preparation, development, productivity and workload.While artificial intelligence has gradually penetrated the field of education over the last 30 years, the emergence of GenAI which we are now witnessing is changing the game. From a perspective that transcends national specificities, the analyses address all levels of education as well as the development of education policies. In terms of epistemology and methodology, the work draws on a variety of research frameworks (theoretical, conceptual, empirical) and methodologies, including qualitative, quantitative and mixed-methods research.This work offers a series of suggestions and recommendations, as well as avenues for future research in a range of areas, including AI/GenAI literacy, teacher preparation and development, curriculum updating, pedagogy, knowledge assessment, teacher effectiveness, learner development, education policy and ethics. It also warns of the risks posed by GenAI, particularly in terms of deskilling, breaches of data privacy and research integrity, bias, social justice, ecological sustainability and loss of meaning of the teaching profession.

This volume illuminates the transformative potential of Artificial Intelligence (AI) in shaping the future of learning, particularly in terms of advancing quality and inclusion across educational settings. It explores the diverse ways AI technologies can be harnessed to enhance pedagogical practices, enrich learning experiences, improve educational outcomes, widen access to quality knowledge sources, and empower minds for all. It thereby aligns with the broader objectives of promoting education quality and inclusion for all as outlined in the United Nations' SDG4 - Education 2030 agenda. This book combines multidisciplinary perspectives, offering a comprehensive view on AI in Education (AIED) practices geared towards quality and inclusion. Through a curated collection of evidence-based research, the volume offers pragmatic insights into the intricate AIED nexus.

This book explores artificial intelligence (AI) applications in early childhood and primary education, focusing on practical uses, pedagogical implications, and the future of AI-driven learning environments. It examines how AI can transform educational practices, including personalized learning, ethical considerations, and the development of AI literacy. Through various contributions, it showcases innovative AI integration in language development, teacher education, creative learning, and student assessment, offering educators comprehensive insights into utilizing AI tools to enhance learning experiences.The chapters draw from real-world case studies and research projects, highlighting both the potential and challenges of AI in education. The book emphasizes the importance of preparing educators and students to engage with AI responsibly and effectively, addressing issues such as equity, ethics, and the evolving role of teachers in an AI-driven landscape. With diverse perspectives and practical examples, it serves as a guide for policymakers, educators, and researchers to navigate the growing impact of AI in shaping the future of education.