AI-Integrated Curriculum Design
This course trains educators to use AI as a studio collaborator—not a shortcut. You will design prompts, evaluate outputs, and translate drafts into classroom-ready learning sequences that remain human-centered, culturally responsible, and instructionally precise.
ADTL Fit: AICI 301 extends ADTL into AI-mediated workflow—keeping Cognitive Design, Cultural Connection, and Ethical Design judgment central at every step.
Establish the working relationship: what AI can draft, what humans must decide, and how ADTL governs the process.
- Define AI’s role as collaborator (drafting, variation, summarization) rather than authority.
- Identify where human judgment is non-negotiable (ethics, culture, accuracy, assessment validity).
- Workflow Map: draft your “AI-in-the-loop” sequence aligned to ADTL stages.
- Red Flag List: identify failure modes (hallucination, bias, oversimplification, tone mismatch).
- Personal AI Collaboration Charter + ADTL guardrails.
ADTL Connection: Positions the educator as the designer of meaning, not a consumer of outputs.
Demonstrate mastery by diagnosing AI drafts through ADTL lenses and proposing human-led improvements.
- Given an AI-generated mini-lesson, identify 6 issues across: clarity, accuracy, culture, assessment, and tone.
- Rewrite one section using ADTL rationale (what the revision designs in the learner).
- Critique notes + revised draft excerpt + decision justification.
Learn prompt structures that generate coherent lessons, differentiated tasks, and clean instructional artifacts.
- Use constraint-based prompting (role, audience, standards, tone, output format, checks).
- Design prompts that embed ADTL: cognitive target, cultural connection, and aesthetic constraints.
- Prompt Ladder: baseline prompt → constrained prompt → studio-grade prompt.
- Format Studio: create prompts for (a) lesson plan, (b) worksheet, (c) rubric, (d) slide outline.
- Prompt Library v1 (10 prompts) with “why it works” notes.
ADTL Connection: Prompting becomes a design practice—inputs create instructional architecture.
Exhibit mastery by producing a usable artifact in one run, then refining it with an evidence-based prompt revision.
- Run your prompt and evaluate output with a 12-point quality checklist.
- Revise the prompt (not just the output) to fix weaknesses, then re-run.
- Before/after prompt + before/after artifact + short reflection on what changed.
Build verification habits that protect learners: sources, claims checks, and error-resistant workflows.
- Identify claim types (dates, definitions, statistics, causal claims) and verify appropriately.
- Create a lightweight “verification pass” that fits teacher time constraints.
- Claim Markup: highlight all factual claims in a draft and categorize them.
- Verification Sprint: validate 10 claims using trusted sources and document corrections.
- Verification Checklist + corrected draft with annotated changes.
ADTL Connection: Cognitive Design requires truth integrity—clarity built on errors collapses.
Demonstrate mastery by locating, correcting, and documenting errors and weak claims in an AI-generated unit draft.
- Correct at least 8 issues across accuracy, clarity, and assessment alignment.
- Provide a correction ledger: original claim → fix → source type → impact on learning.
Convert AI drafts into ADTL-structured lessons with explicit learning targets, sequencing, and reflection cycles.
- Align lessons to ADTL stages (Orientation → Exploration → Synthesis → Application → Reflection → Mastery).
- Design transitions: how each stage prepares the next.
- Stage Map: label and revise a lesson so every segment has a stage purpose.
- Reflection Engineering: create prompts that elicit design rationale, not preference.
- ADTL-Mapped Lesson Prototype + stage rationale notes.
ADTL Connection: The learning cycle becomes your backbone—AI is only the drafting tool.
Exhibit mastery by proving your learning cycle is coherent and that each stage produces evidence for the next.
- Create an evidence chain: what learners produce in each stage and how it is used next.
- Revise one weak transition where the stage purpose is unclear.
Build a repeatable process for checking bias, representation, and harm—before learners ever see the artifact.
- Identify bias risks: stereotypes, omission, flattening culture, colonial framing, deficit language.
- Apply an ethics pass: who is centered, who is missing, what assumptions are embedded.
- Representation Audit: evaluate a draft for voice, framing, and cultural accuracy.
- Rewrite Studio: revise language for dignity, agency, and contextual truth.
- Ethics & Cultural Integrity Checklist + revised exemplar passage.
ADTL Connection: Cultural Connection is designed intentionally—AI must be corrected when it flattens truth.
Demonstrate mastery by documenting bias risks and showing how your revisions protect learners and honor culture.
- Identify at least 5 risks (stereotype, erasure, framing, voice, power). Provide evidence.
- Revise with a rationale: what the revision changes in learner understanding and empathy.
- Before/after excerpt set + revision ledger tied to your checklist items.
Assemble a complete, teachable unit: lesson sequence, materials, assessments, and implementation notes.
- Package a unit with teacher-ready clarity: objectives, materials, pacing, differentiation, checks.
- Build assessment evidence that matches your learning targets (not busywork).
- Unit Skeleton Build: 5–7 lesson arc aligned to ADTL stages.
- Materials Studio: handouts, prompts, rubrics, and “student-facing clarity” checks.
- AI-Integrated Unit Prototype (teach-ready) + supporting materials.
ADTL Connection: The unit becomes a designed experience—coherent, ethical, and instructional.
Exhibit course mastery through a final critique defense of your unit and a review of your AI decision-making audit log.
- Present your unit: design intent → ADTL mapping → materials → assessment evidence.
- Submit an audit log: prompts used, edits made, verification notes, ethics checks, and revisions.
- Is the unit teach-ready and coherent?
- Are outputs accurate and culturally responsible?
- Does the audit log demonstrate human judgment and revision integrity?
Outcome: Final unit package + audit log + short reflection: “Where AI helped, where I overruled it, and why.”