AImagineering: full table of contents and chapter specifications

• Distinguish generation fluency from design judgment Analyze
• Identify at least five categories of human judgment skipped in a standard AI-assisted design session Apply
• Articulate the course argument in one sentence in their own words Understand

In medias res. No theory. Students open a brief, open their AI suite, and have 30 minutes. The output arrives. The chapter begins: "The output is good. That is not the point."

1. The thirty-minute experiment — what was produced and what was assumed
2. The questions that weren't asked — a taxonomy of the human judgment the session skipped
3. The Tier 1 map — what AI does at superhuman level; what this means for the engineer
4. The cognitive forklift reframed for design — applied to the design studio
5. The course argument stated directly — this course teaches the five things the forklift cannot do

1. Run the thirty-minute experiment with your own brief. Write a 500-word audit of what the session assumed that it never asked. Apply HHD required
2. Compare your AI-generated output to one you would produce after two weeks of user research. What is different? What is missing? Analyze
3. In one sentence, state the course argument in your own words. Not the textbook's words. Understand

• Map the grain of at least two AI tools against a specific design brief Apply
• Identify at least one design decision that changes because of grain awareness Analyze
• Place AI tools within the Krebs Cycle of Creativity and explain where each is strongest and weakest Understand

The carpenter and the wood grain. Crawford's Shop Class as Soulcraft. The tool that has a grain is not the enemy of craft — ignoring the grain is.

1. The grain metaphor applied to AI tools — what each does easily, what it fights
2. The Krebs Cycle of Creativity (Oxman) — Science → Engineering → Design → Art. AI is strong at Engineering. Weakest at Art — converting behavior into new perceptions. Prerequisite note: Krebs Cycle introduced here — not assumed.
3. Mapping the grain of three tools: a language model, an image generator, a code assistant
4. The grain as a design resource — working with affordances rather than against them
5. Platform awareness as a design competency — choosing tools based on what the brief needs, not habit

1. Document the grain of your primary AI tool for your capstone domain — what it does naturally, what it resists, and one specific design decision that changes because of this knowledge. Apply
2. Place your primary tool on the Krebs Cycle. Where is it strongest? Where does human judgment have to compensate? Analyze
3. Given your capstone brief, which tool is best suited for which stage of the design process? Defend the assignment. Evaluate

• Conduct an empathy investigation (minimum 2 human contacts) using observation, interview, and artifact analysis Apply
• Document at least one finding that would not appear in any AI-generated persona for the user group Apply
• Explain why plausible user simulations are insufficient for design that serves specific people in specific contexts Analyze

An AI-generated user persona next to a field note from an actual conversation. They are not the same document.

1. The difference between user simulation and user contact — what each produces
2. Why AI personas are plausible and wrong — the training data problem for edge cases
3. The empathy investigation protocol — observation, interview, artifact analysis
4. What to look for that simulation misses — the unexpected, the contradictory, the embodied
5. Tier 3 named directly — AI simulates interpersonal intelligence; humans live it

1. Conduct one genuine empathy investigation (minimum 2 human contacts). Document one finding that would not appear in any AI-generated persona for your user group. Apply
2. Compare your AI-generated persona to your field findings. Where does the persona succeed? Where does it fail? What type of failure is it? Analyze
3. What is the design consequence of relying on the AI persona alone? Name a specific decision that would have been wrong. Evaluate

• Generate at least three alternative problem definitions from a single brief Apply
• Evaluate each reframe against explicit criteria — user impact, feasibility, alignment with empathy findings Evaluate
• Select and defend a reframe in a 3-minute presentation, fielding peer objections Create

The classic brief: "Design a faster horse." The reframe: "Help people move between places efficiently." The AI response to each brief is radically different. The choice of brief is a human judgment.

1. The rationalist vs. co-evolutionary models of design — solving the given problem vs. reframing it
2. Primary generators — the conceptual anchors that shape problem formulation
3. The reframe protocol — five questions that produce alternative problem definitions
4. Criteria for choosing among reframes — who decides, on what basis
5. AI as a reframe stress-tester — using AI to find the failure modes of each reframe before committing

1. Produce three reframes of your capstone brief using the reframe protocol. Apply
2. Evaluate each reframe against explicit criteria. Which survives the stress-test? Evaluate
3. Select and defend your reframe in a 3-minute presentation to peers. Field two objections. Revise or hold your position with stated reasons. Create

• Direct an AI ideation session that produces minimum 30 genuine concepts (not variations on one concept) Apply
• Conduct a documented curation session reducing 30+ concepts to 3 with explicit criteria Evaluate
• Write a defense of each selected concept that explains what made it worth developing over the alternatives Create

The Dreamer/Realist/Critic framework (Disney Imagineering). This is the Dreamer's week. The Dreamer's job is not to evaluate — it is to generate without premature constraint. AI is the best Dreamer tool ever built. This week is about learning to use it without becoming dependent on it.

1. The Dreamer/Realist/Critic framework — what each mode does and when each is needed
2. AI as Dreamer — why this is the right role and the only week it owns
3. The ideation session protocol — directing AI generation toward genuine divergence, not repetition
4. The curation problem — why choosing among 50 plausible options is harder than generating them
5. Curatorial criteria — what makes a concept worth developing beyond its plausibility

1. Run an AI ideation session producing minimum 30 concepts for your capstone brief. Apply
2. Conduct a documented curation session. Select 3 concepts. Write a 300-word defense of each selection. Evaluate
3. Name one judgment call in the curation process that required your values, your domain knowledge, or your accountability — that an AI tool could not have made on your behalf. Create HHD required

• Produce a low-fidelity prototype of a primary concept using AI acceleration Apply
• Document minimum 5 identification decisions — choices the AI could not make without human input Analyze
• Articulate the relationship between prototype fidelity and the specific question it is designed to answer Evaluate

The Realist's job is to take the Dreamer's concepts and make them real. AI accelerates the build. The human decides what to build and what to test.

1. The Realist's cognitive job — translate concept into testable artifact
2. AI as build accelerator — code, design, content, structure
3. The identification decisions — every prototype encodes assumptions about what matters; AI cannot supply these
4. The specification document — translating human judgment into a build brief for AI tools
5. Knowing when to stop building — the prototype answers a specific question; it is not finished product

1. Build a low-fidelity prototype of your primary concept using AI acceleration. Apply
2. Submit a specification document identifying every decision the AI could not make — minimum 5 explicit identification decisions. Analyze
3. What specific question does this prototype answer? What question does it deliberately not answer yet? Evaluate

• Conduct prototype testing with minimum 5 users and document results Apply
• Evaluate results against all three legitimacy types — not just pragmatic Evaluate
• Identify at least one finding the data cannot resolve and specify what additional judgment or evidence would be required Analyze

The test results are in. They show the prototype works. The question the data cannot answer: should it?

1. The three legitimacy types — pragmatic (does it work), moral (should it exist), cognitive (can it be trusted)
2. Why AI achieves pragmatic legitimacy and struggles with the others
3. The interpretive leap — what the data shows and what it implies are different things
4. Reading test results as a designer — what counts as evidence, what counts as noise
5. The failure modes of interpretation — over-trusting data, under-trusting human judgment, confusing metric with meaning

1. Conduct prototype testing with minimum 5 users. Document results. Apply
2. Submit an interpretive judgment document that addresses all three legitimacy types. Evaluate
3. Name one finding the data cannot resolve. What would you need — in the way of evidence, expertise, or judgment — to address it? Analyze

• Produce a draft Commit document that specifies a course of action, its evidential basis, acknowledged uncertainty, and stated accountability Create
• Critique a peer's Commit document using the four structural criteria, identifying where specificity, accountability, or honesty about uncertainty is missing Evaluate
• Revise and finalize a Commit document that survives peer scrutiny Create
• Articulate the difference between commitment under uncertainty and certainty, and explain why AI cannot make the Commit on the designer's behalf Analyze

Design Thinking ends at Test. This chapter is the stage it omits. AI iterates forever. Humans commit. The Commit is not certainty — it is the decision to act despite uncertainty, with your name attached to the consequences.

1. Why Design Thinking omits Commit — it was designed as an ideation methodology, not a deployment methodology. The omission was invisible when prototyping was hard. AI has made it catastrophic.
2. What Commit actually requires — a specific course of action (not "explore further"); evidence from the test phase; acknowledged uncertainty; stated accountability for what happens if it fails.
3. The bad Commit — before peer critique — the food waste worked example shown first as a vague, over-qualified, accountability-free draft. This is the model for what peer critique is designed to find and fix.
4. The peer critique protocol — four diagnostic questions: Specificity, Evidential basis, Honesty about uncertainty, Accountability.
5. The good Commit — after peer critique — the same food waste example revised: specific, evidentially grounded, honest about uncertainty, with a named accountable designer.
6. Phronesis as the meta-capacity — the peer critique round is not just a quality gate. Students who watch their Commit fail under peer scrutiny before it fails in the real world are developing phronesis. Students who only write the document once are practicing confidence, not judgment.

• Specificity: Could someone act on this recommendation without a follow-up conversation?
• Evidential basis: Is every claim traceable to the test phase, or is some of it confidence dressed as evidence?
• Honesty about uncertainty: Does this document say what the designer doesn't know, or only what they know?
• Accountability: Is there a name attached to specific consequences — or is the document engineered to distribute blame?

1. Produce a draft Commit document for your capstone design using the required five-element structure. Create
2. Apply the four peer critique diagnostic questions to a partner's Commit document. Deliver written feedback naming every place where specificity, evidential basis, honesty about uncertainty, or accountability is missing. Evaluate
3. Revise and finalize your Commit document. Name one judgment call in the Commit that required your values, your domain knowledge, or your accountability — that an AI tool could not have made on your behalf. Create HHD required

• Identify at least two moments in their capstone process where a mode switch was required and describe what triggered the recognition Analyze
• Explain why the metacognitive switch has no AI equivalent Analyze
• Articulate what each mode costs when it runs too long — and what the switch produced in their specific case Evaluate

The most dangerous moment in any design process is when the Dreamer won't stop dreaming, or the Critic won't let the Dreamer start. The capacity to recognize which mode is needed — and to switch — is what the conductor does that no instrument can do for itself.

1. The metacognitive switch defined — knowing which mode the moment requires, not just executing a mode well
2. Signs that each mode has gone on too long — the Dreamer who can't curate, the Realist who won't stop building, the Critic who prevents the Commit
3. AI as a mode amplifier — it will Dream forever, Realize indefinitely, Critique without resolution. The human is the circuit breaker.
4. The human as director of the process — the role that has no AI equivalent, and why Tier 4 is the directing intelligence, not just a capacity among others
5. Developing the switch — retrospective analysis as the primary development mechanism; the switch cannot be taught prospectively, only recognized retrospectively and then anticipated

1. Write a 500-word process reflection documenting at least two moments when you had to switch modes — what triggered the recognition, what the switch cost, and what it produced. Analyze
2. Name one moment when you failed to switch and describe the consequence. Analyze
3. What is the mode you default to? What does that default cost you in a design process? Evaluate

• Apply the full pipeline to a new brief in condensed form (one week), with each stage explicitly narrated Create
• Identify, unprompted, where in the condensed process the human judgment layers were required and what they produced Analyze
• Articulate the series argument — what is irreducibly human in AI-augmented design, and why — in their own terms, not the book's Evaluate
• Produce a pipeline narrative that could be delivered to a client, employer, or review committee Create

This chapter does not summarize the book. It tests it. A new brief. No chapter numbers to consult. No worked example to follow. The question is not "did you learn the pipeline?" It is "can you use it?"

1. The pipeline as a single argument — not a sequence of methods but one integrated claim: the human judgment layers are not supplements to AI-assisted design; they are the design.
2. The condensed pipeline brief — a new design problem introduced in class, deliberately chosen to prevent direct transfer of the capstone worked examples. Domain unfamiliar enough that domain expertise cannot substitute for process. Complex enough to require genuine reframing. Consequential enough to require a real Commit.
3. What has changed for this student — specifically — a structured reflection protocol that requires concrete behavioral change, not generic growth statements. "I now ask three reframe questions before touching a tool" is evidence. "I have a deeper appreciation for human judgment" is not.
4. The professional application — students name the specific role they are moving into and describe one decision in that role where the pipeline applies directly.
5. The series argument and what comes next — where this course sits in the Irreducibly Human series; what the next course takes on; why the Commit in this course becomes a moral architecture in the next.

1. Apply the full pipeline to the condensed brief introduced in class. Produce a pipeline narrative document that covers every stage and names every human judgment call. Create
2. In your pipeline narrative, identify the three moments where you were most tempted to let the AI decide. What did you do instead? What did it produce? Analyze
3. Name one judgment call anywhere in your full course capstone that required your values, your domain knowledge, or your accountability — that an AI could not have made on your behalf. This is your graduation statement from AImagineering. Create HHD — final
4. Describe the role you are moving into. Name one decision in that role where this pipeline applies directly. What does the pipeline require you to do that you would not have done before this course? Evaluate — Professional Application

Students present their capstone process as a pipeline narrative — not the output, but every human judgment call, every metacognitive switch, and the Commit document they stand behind. Assessment criteria: specificity of judgment call narration, evidence of metacognitive switching, quality of Commit document, credibility of the Human Half Declaration.