STEM
NeuralQuest
Players train image classifiers, build recommendation systems, discover how bias creeps into data, and explore AI ethics through hands-on experiments. Learn how AI actually works -- not by coding, but by doing. The first gamified AI literacy app for kids on any platform.
Meet your mentor
Every Spark & Anvil app has a friendly mentor character that demonstrates, praises, and patiently scaffolds learning. On-device AI personalizes the mentor's responses to your kid's progress — never connecting to a server, never collecting data.
#29B6F6 NeuralQuest teaches you how artificial intelligence actually works -- by doing it yourself! Train image classifiers, build recommendation systems, discover bias in data, and explore AI ethics. You do not need to know how to code. You will learn by running real experiments in a virtual AI research la
Distributed-narrative cast
Meet the cast
NeuralQuest's distributed-narrative cast embodies the 5 decision points in the AI/ML pipeline — labeling, training, bias-check, generalization, ethics — as 5 lab-partner characters Sift introduces across the 16 question kits. The cast foregrounds AI bias-vigilance (Skew appears in every kit from 5 onward) and neurodivergent-affirming human cognition (no 'your brain is like a model' framing).
Tag
Labeling — the cheerful labeler who treats every label as a human choice and meaning-making act ('every label is a choice — and you're the one making it')
Drill
Training loops — the focused practitioner who treats iteration as rhythm, not race; explicit teacher of when-to-stop ('once, again, again — different this time? Then again')
Skew
Bias + data fairness — the bias-vigilance anchor who always asks 'whose data is in here, whose is missing, who decided'; appears in every kit from kit 5 onward
Veer
Generalization vs overfit — the wandering scout who treats generalization as travel ('trained here, tested here — now go somewhere new, does it still know the way?')
Weigh
Ethics + decisions — the reflective elder who carries the ethics gate at the AI-in-society capstone ('can we build it? Yes. Should we? That's a different question')
What's inside
Training Lab
Collect and label training data, then feed it to a simple AI model. Watch the model learn! Will it get better with more data? What happens if your training data
Classification Lab
Train an image classifier to sort pictures into categories. Take photos or use the built-in datasets. Test your model on new images it has never seen. Can it te
Recommendation Lab
Build a simple recommendation system that suggests things based on patterns. See how apps like Netflix or Spotify figure out what you might like. Then test if t
Ethics Panel
Discuss real AI ethics questions. Should AI make decisions about people? What if an AI is wrong? What about privacy? Think through these big questions with your
Mentored by Sift — on-device AI, no data leaves the device.
How NeuralQuest handles your kid's data
- ✅ All progress, settings, and AI-generated content stays on the device
- ✅ No analytics, no tracking, no third-party SDKs
- ✅ No ads, no in-app purchases — you pay once
- ✅ COPPA compliant under the 2026 FTC amendments
- ✅ Parental controls + session limits + content filters built in
NeuralQuest 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
NeuralQuest 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