Faculty are experimenting with generative AI across teaching, assessment, and program work, but recurring tasks often need more structure than a one-off prompt can provide. This session introduces a platform-neutral design pattern that is especially well suited to recurring faculty workflow tasks. Participants will examine how defined inputs, designed behavior, expected outputs, boundaries, and verification steps can make AI-supported work more reliable across uses and easier to manage. Using concrete faculty workflow examples, the session shows how structured design can support consistency, clarify expectations, preserve human judgment in day-to-day academic work, and potentially reduce some of the cognitive load involved in many common faculty responsibilities.

Learning Objectives

Identify a recurring faculty workflow where a reusable AI support tool would be helpful.

Outline a structured, platform-neutral AI support tool by specifying its task, inputs, outputs, boundaries, and verification steps.

Define criteria for quality output and how that output will be verified before use.

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