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This session explores how faculty can use AI to develop feedback practices that are sustainable at scale by taking a human-in-the-loop approach that saves time, allows for deeper feedback, and preserves faculty expertise, judgment, and responsibility. We will build an AI feedback assistant by defining its role, purpose, tone, instructions, source materials, boundaries, and expected outputs. We will also address the professional decisions that remain essential: determining when AI-supported feedback is appropriate, deciding which aspects of feedback faculty should address themselves, reviewing and revising AI-generated feedback, evaluating its quality, and refining the assistant over time.