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ModelQuest
Train it, audit it, decide whether to ship it — an AI / algorithmic-ethics decision & role-play lab for ages 15–18. Tune a toy model, watch an accuracy-vs-fairness trade-off move, audit a data pipeline for bias, and deliberate a deployment decision from stakeholder roles — building critical, not just functional, AI literacy.
▶ Play it nowBuilt for sittings of about 25 minutes on the screen. That is our design budget, not a measurement.
#B85C8A Characters who teach
Meet the cast
ModelQuest's characters are realistic people, not mascots, and each one shows one question to ask about AI. Dara shows how biased data makes a biased model, Faye shows the trade-off between accuracy and fairness, Gil memorizes the training data and fails on new cases, Prue finds a detail that quietly stands in for race or gender, Lux explains a model's decision, and Nils collects only the data that's needed. Wynn, Odell, and Sena cover models that change their own future data, keeping a human in charge, and the people a model affects. Amara is the mentor who guides each review.
Dara
Training data / bias — curates the data the model learns from; shows bias-in → bias-out
Faye
Accuracy vs fairness — moves the decision threshold and shows accuracy up, fairness down
Gil
Overfitting / generalization — memorizes the training set, fails on new data
Prue
Proxy variables — spots a feature that secretly leaks a protected attribute
Lux
Transparency / explainability — opens the black box and explains a decision
Nils
Privacy / data minimization — collects the least data needed
Wynn
Feedback loop / deployment harm — shows a deployed model reshaping its own future inputs
Odell
Human-in-the-loop oversight — keeps a human check on the automated decision
Sena
Consent / affected stakeholders — represents the affected community's voice (Sena + Faye = deployment case)
Amara
Mentor — frames the audit, keeps it anti-evangelist, guides reflect-on-decision (the AAR guide)
What's inside
Learning goal
Train it, audit it, decide whether to ship it — an AI / algorithmic-ethics decision & role-play lab for ages 15–18. Tune a toy model, watch an accuracy-vs-fairness trade-off move, audit a data pipeline for bias, and deliberate a deployment decision from stakeholder roles — building critical, not just functional, AI literacy.
Question kits
16 curriculum-aligned question kits, 400 questions in all, mapped to recognized standards.
On-device AI mentor
FoundationModels-powered hints, feedback, and adaptive difficulty — all running locally.
Mentored by Amara — on-device AI, no data leaves the device.
How ModelQuest handles your kid's data
- ✅ All progress, settings, and AI-generated content stays on the device
- ✅ No analytics, no tracking, no third-party SDKs
- ✅ Free — no ads, no in-app purchases
- ✅ No personal information collected from children — in line with COPPA (updated by the 2026 FTC amendments)
- ✅ No chat with strangers — play-together rooms use a short code and preset emotes, never typed messages
ModelQuest runs on ForgeKit — the open-source Swift Package Manager framework that powers every Spark & Anvil app. ForgeKit ensures consistent accessibility, COPPA compliance, and design language across the portfolio, so your kid's progress and preferences feel coherent across every app they touch.
Coming to the App Store
ModelQuest is in active development. Email us to hear when it ships — no marketing, no spam, just a one-shot launch announcement.
Email me at launch
