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Generate\u003Cbr>Open Learning Lane | Students produce an initial AI output. In tutorials, use the provided base prompt. In assessments, students first create a human pre-attempt (notes, outline, or rough draft), then build it into their prompt. | What prompt or scaffold will you provide? What context should students include in their prompt?(Leave blank if students design their own) | How will students submit evidence of this step?(e.g., submit the rawAI output + the prompt used) |\n| 2 | Evaluate\u003Cbr>Open Learning Lane | Students compare AI output against authoritative anchors—rubric criteria, professional standards, regulatory frameworks, research papers, or disciplinary guidelines. | What anchors will you provide? (e.g., NIST CSF, Privacy Act, clinical guidelines, unit readings) Note: students must identify their own anchorsin Stage 2. | How will students document their evaluation?(e.g., annotated AI output, evaluation checklist, comparison notes) |\n| 3 | Refine\u003Cbr>Open Learning Lane | Students revise the AI output based on their evaluation. Every significant change is logged as Accept, Modify, or Reject with a brief evidencebased justification (~50–75 words per decision) . | Will you provide a decision log template or use the SAGE default? Minimum: 3 columns—AI Output | Decision | Justification | The decision log IS the submission for this step. Specify minimum number of logged decisions.(Recommended: at least 6–8 for a substantial task) |\n| 4 | AI Critic / Audit\u003Cbr>Open Learning Lane | Students prompt the AI to act as a domain expert reviewer (matched to the anchor used in Step 2) and critique the decisions recorded in the Step 3 log. Students then adjudicate | What professional role should the AI adopt? (e.g., security auditor, clinical reviewer, compliance officer) This should match the anchor used in Step 2. | How will students document their adjudication? (e.g., extended decision log, separate adjudication table) |\n\n\n|  |  | each flag—accepting or rejecting the AI critique with reasons. |  |  |\n| --- | --- | --- | --- | --- |\n| 5 | Reflect\u003Cbr>Open Learning Lane | Students write a structured reflection reviewing their decision log and identifying:\u003Cbr>(1) what the AI got wrong and why,(2) where their disciplinary knowledge was essential,(3) what anchoring judgements they will apply in future professional contexts. | Will you provide reflection questions or use open reflection? Recommended:\u003Cbr>tie at least one question directly to the professional or disciplinary context of the unit. | Specify word count and format. (Recommended:\u003Cbr>200–400 words, structured around the two layers—intellectual contribution and transferable anchoring habit) |\n| 6 | Defend\u003Cbr>Assurance Lane | Students demonstrate under supervised conditions that they can explain, justify, and reproduce the reasoning documented in their submitted work. Format varies by discipline—seethe Implementation Appendix for examples. | Which Defend format suits your cohort and context?(e.g., oral viva, live demonstration, timed exercise, structured Q&A) Consult the Assurance Guidance Tool at sage[framework.com/defend-tool](framework.com/defend-tool) | Specify: format, duration, weighting, and whether it gates the rest of the assessment.(Recommended: 10–15 minutes per student; sufficient weighting to incentivise preparation) |\n\nDownload the student-facing guide, decision log template, and Defend format recommender at [sage-framework.com](sage-framework.com) Elkhodr, M. & Gide, E. (2026) . SAGE Framework. CC BY 4.0.","cbCailYjfiGNFowu","https://ap.wps.com/l/cbCailYjfiGNFowu","pdf",62420,"English","# Unit Planning Template\n## Unit details\n## Step 1 - Generate: Open Learning Lane\n## Step 2 - Evaluate: Open Learning Lane\n## Step 3 - Refine: Open Learning Lane\n## Step 4 - AI Critic / Audit: Open Learning Lane\n## Step 5 - Reflect: Open Learning Lane\n## Step 6 - Defend: Assurance Lane","[{\"question\":\"What is the purpose of the SAGE Unit Planning Template?\",\"answer\":\"It provides a structured workflow for designing an AI-supported unit, including assessment evidence from generation through evaluation, refinement, reflection, and a supervised defend stage.\"},{\"question\":\"How should students handle the decision log in Step 3?\",\"answer\":\"Students must record every significant change as Accept, Modify, or Reject, and include brief evidence-based justifications, with a suggested minimum of 6–8 logged decisions for substantial tasks.\"},{\"question\":\"What does Step 4 require from students and the AI critic?\",\"answer\":\"Students prompt the AI to act as a domain expert reviewer matched to the anchors used in Step 2, then they adjudicate the critique and document their adjudication decisions.\"},{\"question\":\"What options exist for the Step 6 “Defend” stage?\",\"answer\":\"Defend formats can include oral viva, live demonstration, timed exercise, or structured Q\\u0026A, with specification of format, duration, weighting, and whether it gates the rest of the assessment.\"}]","SAGE Unit Planning Template - AI-assisted Open Learning Lanes | PDF",1789147187]