Generative AI is rapidly changing how higher education assesses learning: models now draft feedback on student writing, assign scores, and even explain their decisions. But does a system's resemblance to human judgment make it trustworthy? This book argues that it does not, treating assessment in the age of AI not as an accuracy problem to be solved by larger models, but as a human-centered socio-technical practice in which feedback must be designed, scoring must be governed, and claims must be validated. Across three connected parts, the book follows a single arc: designing AI feedback that students actually learn from; governing LLM-based scoring around privacy, security, fairness, and human oversight; and validating scores through defensible study design, reliability and bias evidence, and reproducible reporting. Written for researchers, instructors, and academic developers in educational technology and assessment, it offers not a tool to adopt but a way of thinking, one that keeps ...