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Common workflows: draft → review → refine
In this lesson
Common workflows: draft → review → refine
Apply the draft → review → refine workflow to delegate mechanical subtasks to an AI while reserving critical judgment and synthesis for human review, consistent with Mollick and Mollick's AI-as-TOOL pedagogical frame…
You'll be able to
- Apply the draft → review → refine workflow to delegate mechanical subtasks to an AI while reserving critical judgment and synthesis for human review, consistent with Mollick and Mollick's AI-as-TOOL pedagogical frame [^1].
- Evaluate AI-generated first drafts against domain-specific quality criteria, identifying where the output requires correction, evidence strengthening, or structural revision before the artifact is suitable for professional use [^1][^6].
- Create iterative refinement cycles in which you use AI critique (Mollick's AI-as-COACH role) to surface weaknesses in a draft, then apply your own judgment to accept, reject, or adapt suggested improvements [^1].
- Classify which stages of a multi-step workflow benefit from AI automation (draft generation, scenario enumeration, initial classification) versus which demand human oversight (final approval, clinical safety review, alignment with regulatory standards), drawing on real-world clinician-in-the-loop and policy-development patterns [^2][^4][^5].
- Explain why rewriting and debugging are more feasible when the AI produces a manipulable first draft than when starting from scratch, citing Papert's observation that easy revision transforms reluctance into iterative improvement [^6][^8].