Bias, Privacy, and the Ethics of AI in Education

In an era defined by rapid technological advancement, the integration of artificial intelligence (AI) in education is profoundly reshaping curriculum development, teacher training, assessment, and other core aspects of the field. As AI continues to transform teaching, learning, and administration, it also introduces critical challenges related to data privacy, algorithmic bias, equity, autonomy, and surveillance. There is a pressing need for research that not only examines these concerns but also offers practical, sustainable approaches for the responsible long-term integration of AI in education. Bias, Privacy, and the Ethics of AI in Education examines the ethical dimensions of integrating AI into educational contexts, with a focus on developing practical frameworks for responsible, transparent, and inclusive innovation. Drawing on theoretical perspectives, empirical research, case studies, and policy insights, this book offers a balanced and actionable approach to guiding ethical AI adoption in education. Covering topics such as student privacy, educational machine learning systems, and federated intelligence, this book is an excellent academic resource for graduate and doctoral students, educators, instructional designers, AI engineers, policymakers, and more.