English AI Coach is a starter project for a mobile AI-based English learning app. It helps users write short English sentences, find mistakes, and get clear corrections in English.

The project is intentionally lightweight: React Native, Expo, TypeScript, and React Navigation. It avoids unnecessary Expo-only libraries and is prepared for local AI with Ollama, speech input, text-to-speech, and push notifications.

Features MVP

HomeScreen with progress card and mock daily reminder
Level selection for A1, A2, B1, and B2
ChatScreen with user and AI messages
AI replies can be read aloud on iPhone
Ollama integration with offline fallback examples
TypeScript-typed navigation
Structure for future services, hooks, and utilities
Web/PWA path prepared through Expo Web

Beispiel

User: I go yesterday to work

AI Coach:
Good try. Correct: "I went to work yesterday."
Use the past tense with "yesterday".

Tech Stack

React Native
Expo
TypeScript
React Navigation
Ollama API for local AI
Text-to-speech with expo-speech
Speech input currently through iPhone keyboard dictation
Planned: in-app speech-to-text
Planned: push notifications for daily reminders

Projektstruktur

english-ai-coach/
├── App.tsx
├── app.json
├── package.json
├── src/
│   ├── components/
│   │   ├── ChatBubble.tsx
│   │   ├── PrimaryButton.tsx
│   │   └── ProgressCard.tsx
│   ├── hooks/
│   │   └── useDailyReminder.ts
│   ├── navigation/
│   │   ├── AppNavigator.tsx
│   │   └── types.ts
│   ├── screens/
│   │   ├── ChatScreen.tsx
│   │   ├── HomeScreen.tsx
│   │   └── LevelScreen.tsx
│   ├── services/
│   │   └── mockAiCoach.ts
│   ├── theme/
│   │   └── colors.ts
│   └── utils/
│       └── levels.ts
└── README.md

Installation

Requirements:

Node.js
npm
Expo through npx expo

Install dependencies:

npm install

Create optional local AI configuration:

cp .env.example .env

Set your Ollama model in .env:

EXPO_PUBLIC_OLLAMA_BASE_URL=http://localhost:11434
EXPO_PUBLIC_OLLAMA_MODEL=llama3.2

When testing on a real phone with Expo Go, localhost means the phone itself. Use your laptop IP instead, for example:

EXPO_PUBLIC_OLLAMA_BASE_URL=http://192.168.10.102:11434
EXPO_PUBLIC_OLLAMA_MODEL=qwen2.5:7b

Start the app:

npm run start

Speech Input And Playback

Playback works directly in Expo Go. AI replies are read aloud through the iPhone speaker. The chat also has buttons for Read last answer and Stop.

Speech input currently works through the iPhone keyboard:

Tap the chat input field
Press the microphone icon on the iPhone keyboard
Speak English
Review the text and send it

Real in-app speech-to-text with a custom microphone button is planned for later. That requires either a native iOS module after expo prebuild or a transcription API such as Whisper.

iOS Preview starten:

npm run ios

Web Preview starten:

npm run web

Note: local iOS simulator builds require macOS with Xcode. With Expo Go, the app can be tested on a real device.

Ollama Integration

The chat uses src/services/ollamaClient.ts for real local AI replies. If Ollama is not reachable, src/services/mockAiCoach.ts automatically falls back to example replies. Model configuration lives in src/config/ai.ts and reads values from .env.

Local flow:

React Native Chat UI
-> AI Service
-> local Ollama API
-> model such as llama, qwen, or mistral
-> English-only correction and explanation

For a PWA or local web version, the app can later run in the browser and connect to a local Ollama server on the network.

PWA Idee

Die Web-Version kann spaeter als PWA erweitert werden:

Auf Home-Bildschirm installieren
Lokale Ollama-Verbindung konfigurieren
Daily Reminder ueber Web Push pruefen
Desktop- und Tablet-Layout optimieren

Roadmap

Chat UI erweitern
Mock KI durch echte KI ersetzen
Ollama API Client implementieren
Level-spezifische Prompts erstellen
Fehleranalyse und Vokabeltraining ergaenzen
Fortschritt lokal speichern
Speech-to-Text hinzufuegen
Text-to-Speech fuer Aussprachetraining hinzufuegen
Push Notifications fuer taegliche Uebungen einbauen
PWA-Ausbau pruefen
App Store Release vorbereiten

Ziel

Ziel ist ein skalierbares Starterprojekt fuer eine KI-basierte Englischlern-App, die zuerst als mobile Expo-App funktioniert und spaeter lokal mit Ollama oder ueber eine externe KI-API erweitert werden kann.