ADHD Learning Companion

A structured, cyclical AI learning companion: explanation → feedback → interest-based personalization → technical question → answer evaluation → next-stage expansion, repeating. Originally a Streamlit prototype, rebuilt as a Next.js app deployable on Vercel.

Live: https://adhd-learning-companion.vercel.app

Why this is a bounded conversational agent, not a chatbot

The model never drives the conversation. A fixed client-side state machine (app/page.tsx) decides which of five stages is active — question → feedback → interest → questionPhase → answer → (back to feedback) — and only calls the model with a stage-specific system prompt for that one turn. The LLM produces content within a stage; it never chooses the next stage.

Stack

• Next.js 14 (App Router, TypeScript)
• Tailwind CSS
• OpenRouter chat completions API (google/gemini-2.5-flash-lite), called server-side only from app/api/chat/route.ts — the API key never reaches the browser.

Local development

npm install
cp .env.local.example .env.local   # fill in OPENROUTER_API_KEY
npm run dev

Open http://localhost:3000.

Deployment (Vercel)

1. Push this repo to GitHub.
2. Import it at https://vercel.com/new.
3. Add environment variable OPENROUTER_API_KEY in Project Settings → Environment Variables.
4. Deploy.

Security note

OPENROUTER_API_KEY is read only inside app/api/chat/route.ts (a server-side route handler) and is never sent to the client. .env.local is gitignored.

Disclaimer

Educational tool only. Not a clinical diagnostic system, psychological assessment tool, or medical intervention platform.