When generative AI can produce polished academic work in seconds, what does student work actually tell us about learning? Rather than focusing on AI tools, detection systems, or platform comparisons, this book examines how assignment design can make learning visible under changing technological conditions.

Drawing on research in learning, assessment, and faculty development, Daniel R. Sanford argues that the central challenge posed by AI is not simply academic integrity, but the growing difficulty of using finished products as evidence of learning. When production can be automated, assignments must be designed to surface judgment, decision-making, revision, interpretation, and critical thinking.

Organized around durable cognitive practices such as reflection, analysis, argumentation, collaboration, ethics, and metacognition, this book offers practical strategies for redesigning assignments across disciplines and teaching modalities. It will help faculty make student thinking visible, cultivate authentic voice, and reconnect student work to the learning it is meant to represent.