Generative Artificial Intelligence (GenAI) is rapidly transforming higher education by reshaping how knowledge is created, shared, and assessed. Its ability to generate content, analyze learner responses, and provide adaptive feedback is creating new opportunities to enhance learning outcomes and educational quality. At the same time, the growing use of GenAI raises important questions about academic integrity, transparency, fairness, and the role of human judgment in assessment. As institutions seek to support authentic, equitable, and competency-based learning, they must carefully balance innovation with ethical and pedagogical considerations. Understanding how GenAI can be integrated responsibly into assessment practices has therefore become a critical priority for higher education worldwide. AI-Driven Assessment Design in Higher Education explores how GenAI is transforming assessment practices in higher education and redefining the ways learning is evaluated, supported, and enhanced. Through assessment models, frameworks, and empirical studies, this book investigates the effectiveness of AI-assisted approaches alongside human-led assessment practices. Covering topics such as adaptive assessment, automated feedback, and teacher education, this book is an excellent academic resource for graduate and doctoral students, educational technology professionals, instructional designers, academic quality assurance practitioners, higher education administrators, institutional leaders, academic integrity officers, policymakers, and more.
