AIQ
AIQ
Part of the AI + Creativity Mashup lesson guide. Teaching a different grade? 🌈 Explorer (5–7) · 🔧 Builder (8–10) · 💻 Hacker (11–14)
Open with the lesson's actual framing question, and let it sit rather than rushing to answer it: "When AI helps create art, who is the artist? What counts as original? Let's explore the creative, legal, and ethical questions of human-AI collaboration."
This age group can handle — and often wants — a genuine unresolved question rather than a tidy takeaway delivered up front. Follow with: "This isn't a lesson where I'm going to give you a clean answer at the end, because there isn't one yet. Courts, artists, and publishers are actively disagreeing about this right now. What I want you to leave with is a framework for thinking about it clearly, not a verdict." Naming that up front sets honest expectations and tends to increase engagement with older students, who can be skeptical of oversimplified "AI is good/bad" framing.
Walk through the three scenes as building blocks toward the lesson's real content: prompt engineering as a skill, transformational creativity, and the extended mind thesis — all introduced in the quiz, so the activity should build the vocabulary and reasoning that makes that quiz make sense rather than feel like trivia.
Scene 1 — AI as Creative Partner. Use the four examples to establish a spectrum of human involvement: AI + Human Art (human guides generation — "your ideas + AI's execution = something new"), AI + Musicians (AI generates backing tracks; the human performs live vocals/instruments on top), Pure Human Art (a watercolor — zero algorithmic involvement, useful as the control case), and AI + Writers (AI as brainstorming partner; the human writes final prose). Point out that none of these examples is "no human contribution" — the interesting question the lesson is building toward is not whether a human was involved, but how to characterize what kind of contribution a prompt, a curation choice, or a final edit actually is.
Scene 2 — Creative Projects. Cover the technical grounding: AI photo/image tools handle background removal, outpainting (extending an image beyond its original borders), and style transfer; AI animation tools generate in-between frames and synthetic voice performance from keyframes and scripts; photography remains presented as fully human because framing, timing, and emotional read of a scene are judgment calls made in the moment, not outputs of a trained model; AI game-development tools let one person generate art, level geometry, and music that used to need a full studio team. Use this scene to introduce prompt engineering explicitly: getting good results from any of these tools is itself a skill built through iteration — refining word choice, structure, and reference material in a prompt — which is the technical basis for the first quiz question.
Scene 3 — Human + AI Rules. Present the four rules, then push past them into the harder question the quiz is really testing: "YOU Direct, AI Executes, YOU Judge, Credit Honestly" is a practical ethic, but it doesn't settle who legally or morally "owns" the result, or whether the human's directorial contribution (a prompt, a curation choice) is creatively equivalent to the AI's generative contribution (the actual pixels or notes produced). That's the opening for introducing two philosophical framings directly:
Margaret Boden's creativity typology distinguishes exploratory creativity (finding new things within a known space of possibilities) from transformational creativity (changing the rules of the space itself, making previously impossible things possible). Most AI generation today is exploratory — it recombines and extends patterns from training data — but the lesson asks students to consider whether some AI systems, or human-AI combinations, cross into transformational territory by producing genuinely new possibility spaces rather than new points within an old one.
The extended mind thesis (from philosophy of mind, associated with Andy Clark and David Chalmers) argues that tools a person relies on heavily and fluently — a notebook, a calculator, arguably now an AI generation tool — can function as an extension of their own cognitive process rather than an external assistant. Applied here: if an artist's AI tool is integrated tightly enough into how they think and create, the tool arguably becomes part of their creative process rather than a separate contributor competing for credit.
Close with the summary, treated as a working position rather than a settled conclusion: "Human-AI creative collaboration works best when humans maintain creative direction. Prompt engineering is itself a creative skill. AI handles generation and variation. Humans provide intent, curation, and quality judgment. Attribution and copyright remain unresolved."
One more thread worth pulling before moving to discussion: ask students to notice that "attribution and copyright remain unresolved" is doing real work in that summary — it's an honest admission built into the lesson's own content, not something added by this guide. Point out that this is a case where a school lesson is teaching students to sit with genuine uncertainty rather than being handed a tidy rule, which is itself a skill worth naming explicitly for a group old enough to appreciate it.
These are genuinely contested questions in art, law, and philosophy right now — treat this less like a Socratic seminar with a hidden correct answer and more like an actual live debate among informed adults, because that's a fair description of where the discourse currently stands.
Close with: "The technology here will keep changing fast, probably faster than the law or the art world's norms can keep up with. What won't change is that someone has to decide what's worth making and whether it's any good — hold onto that as the part of the process that's actually yours, no matter how the tools evolve."
Extension activity (25–30 minutes, portfolio-relevant): Have students find and analyze one real, current copyright or authorship dispute involving AI-generated creative work (a lawsuit, a platform policy change, an artist's public statement) and write a one-page position memo: what's the disputed claim, what does current law or policy say, and what would they argue if it were up to them. For students building a portfolio, this can double as a piece of written work demonstrating critical engagement with AI ethics — genuinely useful for the kind of reflective writing college applications and portfolio reviews look for.
Structure the memo with three short sections to keep it focused rather than a rambling opinion piece: first, a factual summary of the dispute in their own words (what happened, who's involved, what's actually being contested); second, a brief statement of the current legal or policy position, sourced from something they actually read rather than assumed; and third, their own reasoned position, explicitly using at least one concept from today's lesson — Boden's creativity types, the extended mind thesis, or the "direct/execute/judge" framework — to justify it. Requiring that explicit connection is what turns this from a generic current-events assignment into a demonstration that the lesson's conceptual tools actually transfer to a real case they found themselves.
If a student is genuinely interested in a creative or technical career, this is also a natural moment to mention that the questions raised today — who owns AI-assisted work, how it should be disclosed, what counts as fair training data — are live professional questions in game studios, design agencies, publishing houses, and record labels right now, not settled classroom trivia. Being able to reason about them clearly, as this lesson asks them to, is a genuinely useful skill heading into any creative field they might pursue after graduation.