DRAFT — This page is a work in progress.
Curiosity Practices for Creatives
How to keep learning without outsourcing your agency.
Frameworks often tell you what to know. We focus on how to keep learning—practices that help you work with AI while staying responsible for the outcomes.
These practices are grounded in Hypandra's five expressions of curiosity and our AI principles.
See how these practices map to AI literacy frameworks: AI Literacy Crosswalk →
Tools + Artifacts
These practices work because they leave handles—small artifacts other people can inspect, contest, and improve.
Curiosity Build Card
Our 'model-card-style' record for creative AI work
Handoff Map
What we delegate / keep / forbid
Critique Note
Structured feedback that explains why something is broken
Provenance Note
IP, consent, attribution, uncertainty labeling
Decision Record
What we chose + tradeoffs
Repair Log
What changed, why, and what it addressed
1) Question Surfacing
Noticing gaps, generating better questions, staying engaged without outsourcing wonder to machines.
This is humility in action: we don't start with "what can the model do?" We start with "what are we trying to understand, and what would count as progress?" The admission that we don't already know is where real learning begins.
Stretch your questioning skills in Are You Curiouser? and reflect with Hypandra in Explore.
2) Handoff Spotting
Mapping what to delegate and what to keep; naming risks aloud; choosing handoff depth deliberately.
When a task moves from a person to a machine, the job might get done — but responsibility, learning, and control change shape. Handoff Spotting makes those shifts explicit.
We ask:
- What stays human because it's judgment, care, accountability, or taste?
- What can be delegated because it's reversible, low-stakes, or auditable?
- What should never be delegated because it quietly changes the world?
Two questions we refuse to skip
Curiosity about who wins / who pays
When this works, who benefits? When it fails, who eats the cost—time, money, reputational harm, surveillance, exclusion, degraded craft, or shifted responsibility?
Curiosity about what gets normalized
What behavior becomes standard once this tool is common? What does it quietly make acceptable—opacity, dependency, copy-paste culture, or "no one's responsible"?
Artifact you should leave behind: Handoff Map
- Delegate (drafts, options, reversible transforms)
- Hold (final judgment, accountability, claims with stakes)
- Guardrails (hard no's; escalation triggers)
3) Exploratory Prototyping
Building to learn; testing with real users; iterating based on evidence rather than assumption.
Prototyping is where "AI literacy" stops being vocabulary and becomes judgment. You learn what the system does, where it fails, and what it changes in people's workflow.
Artifact you should leave behind: Test Note
- Hypothesis (what you think will improve)
- Test (who used it, for what)
- Evidence (what happened)
- Surprise (what broke your assumptions)
- Next repair (what you'll change)
Try it (smallest real test):
Before you build the next feature, write one measurable outcome you can test in a week. If you can't name the measure, you're not prototyping—you're vibing.
4) Critical Evaluation
Evaluating outputs and systems for limits, bias, safety, and context — not just "this is bad," but why might it be this way, what does it do to people, and how could we demand/build better?
This is demanded curiosity in practice. We don't let ourselves settle for "it works" or "it's broken." We keep asking who wins, who pays, and what gets normalized—even when it's uncomfortable.
This is a real, functional form—submissions go to our team. The toggles below show how critique can take different forms depending on what you need to communicate.
5) Collaboration
Explaining choices, giving and receiving feedback, documenting decisions so others can contest and build on them.
Collaboration is how curiosity becomes shared infrastructure instead of private cleverness. It's also how repair happens: people can only improve what they can see and challenge.