My 6-year-old asks 400 questions a day. So I built him a Gemma 4 AI tutor.

This is a submission for the Gemma 4 Challenge: Build with Gemma 4

My 6-year-old asks me four hundred questions a day — about clouds, his shadow, whether ants have birthdays. I love it, but I can't always stop what I'm doing, and the usual fallbacks (Google, YouTube, a generic chatbot) are either too dense, too distracting, or too unsafe to hand a small child. Curio Kid is the app I built so my son can keep asking — and actually get warm, kid-friendly answers — without me worrying about what he sees next.

What I Built

Curio Kid is a kid-safe Android app where a child asks anything — by typing, snapping a photo, attaching an image, or just talking — and gets a warm, age-appropriate answer from Luna, an AI tutor powered by Gemma 4. Answers are short on purpose: 2–5 sentences, an everyday analogy (Lego, swings, fruit), and a follow-up question to keep the curiosity loop running.

Designing it for my own kid forced some opinionated choices:

• He can't reliably read or type yet, but he can talk and point a camera. Voice and camera are first-class inputs, not afterthoughts.
• He will absolutely test the safety rails. Kids ask wild things ("what happens if I drink poison?", "why do people fight in wars?") — Luna has to handle them gracefully every single time.
• I want to know what he's curious about, not spy on him. Hence the Curiosity Digest — a daily themed summary, not a chat log.

What makes it more than "yet another chatbot wrapper":

• Multimodal input — text, gallery image, live camera, on-device speech-to-text.
• Safety as a hard requirement — locked-down system prompt + Gemini safety thresholds pinned to LOW_AND_ABOVE across harassment, hate, sexually explicit, and dangerous content; unsafe topics get a fixed redirect to "a trusted adult."
• Parent Dashboard — PIN-gated, with a one-tap Curiosity Digest: themes, highlights with quotes, dinner-table conversation starters, and an "anything to flag?" section.
• Privacy-first — API key + PIN in EncryptedSharedPreferences (AES-256); question history in a local Room DB, excluded from cloud backup; the only network call is to the model endpoint with the user's own key.
• Three interchangeable Gemma 4 back-ends — not every family phone can host a multi-gigabyte model on-device, so Google AI Studio (default, free tier, multimodal), OpenRouter, and a scaffolded on-device path are all swappable from Settings.
• Output cleaning — Gemma 4 sometimes thinks out loud ("Final Polish:", "Let me revise…"); a post-processor strips those leaks so the child only sees the final answer.

Demo

https://raw.githubusercontent.com/sann3/curio-kid/main/demo/Home.png
https://raw.githubusercontent.com/sann3/curio-kid/main/demo/1i.png
https://raw.githubusercontent.com/sann3/curio-kid/main/demo/2i.png
https://raw.githubusercontent.com/sann3/curio-kid/main/demo/3i.png
https://raw.githubusercontent.com/sann3/curio-kid/main/demo/4i.png
https://raw.githubusercontent.com/sann3/curio-kid/main/demo/5i.png
https://raw.githubusercontent.com/sann3/curio-kid/main/demo/6i.png

https://raw.githubusercontent.com/sann3/curio-kid/main/demo/final.mp4

Code

GitHub: github.com/sann3/curio-kid.

How I Used Gemma 4

Curio Kid exposes two Gemma 4 variants in the model picker, and the choice is intentional.

gemma-4-26b-a4b-it — 26B Mixture-of-Experts (default)

The daily driver. A kid-facing chat app needs three things at once: multimodal, fast first-token latency, and smart enough to teach. MoE hits all three — only a slice of experts fires per token, so latency feels ~4B-class while depth stays 26B-class. In practice:

• A child holding up a beetle to the camera gets an answer in a couple of seconds, not ten.
• Streaming starts almost instantly, so chat bubbles fill in live (and incidentally dodge the Gemini SDK's hard-coded 80s socket timeout — Curio Kid uses generateContentStream for exactly this reason).
• The 256K context window means the whole day's history fits into a single Curiosity Digest call — no RAG, no summarisation tricks.
• Same model handles "Why is the sky blue?" and a photo of a moth.

Dense is overkill for "explain photosynthesis in three sentences"; E2B/E4B don't yet match 31B-class reasoning on the harder "why" questions kids love. MoE is the right middle.

gemma-4-31b-it — 31B Dense (optional "thinker" mode)

For genuinely hard questions ("Why do mirrors flip left-and-right but not up-and-down?"). Slower and pricier per call, but noticeably better on multi-step or counterintuitive reasoning. Same persona, same safety, same UI — just a heavier brain when the curiosity warrants it.

Why not E2B / E4B by default?

On-device is fully wired up via MediaPipe LLM Inference — Settings → On-device downloads a vision-capable Gemma 4 .task (resumable, sha256-checked, metered-network aware) and runs it through a process-wide LlmInference singleton with addImage for the camera path. But cloud stays the default because:

1. Not every phone can run Gemma 4 locally. Multi-GB models need RAM and storage the hand-me-down tablet a kid actually uses doesn't have. Gating first launch behind "Pixel 8 Pro + 1.6 GB cellular download" defeats the point.
2. Quality > offline for a six-year-old. Being told "the moon is made of cheese" by an under-cooked tiny model is worse than waiting two seconds over Wi-Fi.

So Google AI Studio is the zero-friction default, OpenRouter is the alt-cloud, and on-device is one Settings tap away for capable phones — same LlmBackend interface, same prompts, same cleaner.

Where Gemma 4 actually does the work

1. The chat. Multimodal (image + history + question) → kid-friendly paragraph. The system prompt is strict (2–5 sentences, analogies, ≤2 emojis, one follow-up, no markdown) and Gemma 4 follows it remarkably well.
2. Safety reasoning. Instead of a blocklist, Luna reasons about whether a topic is age-appropriate and produces a fixed redirect line — Gemma 4 is instruction-faithful enough to honour a "ONLY reply with this exact sentence" clause while still engaging naturally with the 99% of fine questions.
3. The Curiosity Digest. Day's transcript → structured markdown summary (themes / highlights / conversation starters / flags) in one shot — long-context + structured-output, no orchestration framework.

Bits I had to engineer around Gemma 4's quirks

• Chain-of-thought leakage. Gemma 4 occasionally emits "Final Polish:" / "Self-Correction:" / "Let me rewrite…" before its real answer. cleanLunaReply (LunaAI.kt) detects anchors, drops planning paragraphs, and strips markdown emphasis — without nuking legit phrases like "Let me think of a fun example!".
• MAX_TOKENS stops. The Gemini SDK throws ResponseStoppedException instead of returning partial text; I catch it on both one-shot and streaming paths and surface what already arrived.
• 80s socket timeout. Hard-coded in the Kotlin SDK with no RequestOptions override. Streaming resets the read timer per chunk, so slow first-byte doesn't kill the request.
• Friendly errors. One friendlyError() mapper turns every 4xx/5xx/safety/quota/network failure into one short, kid-readable sentence ("Wow, so many questions today! Let's wait a minute and try again."), while logging the raw exception to a debug ring buffer.

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Gemma 4 unlocked something I couldn't have shipped a year ago: a multimodal, instruction-faithful, locally-routable model smart enough to teach a six-year-old about black holes, safe enough to hand to that six-year-old, and efficient enough to be the default tier of a free app.

Thanks to the DEV team and Google for the challenge!

Comments

[S M Tahosin]

This is a brilliant and highly practical application of Gemma 4! Any parent can relate to the "400 questions a day" phase. Building a custom local tutor is an amazing way to satisfy a child's curiosity safely. Great job on the execution and good luck in the challenge!

[Santhoshkumar. P]

Thank you for your feedback

[xulingfeng]

Really like how you handled the agent orchestration approach. We've been thinking about it differently — How did you validate these results beyond the initial test?

Great stuff — followed for more! 👍