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Creating an AI-Based Language Learning Roleplay Bot with Expo & Vercel AI SDK
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Have you ever wished you could practice a new language through immersive roleplay, like chatting with a Parisian barista in French or a Japanese tour guide in Tokyo? With the Vercel AI SDK and Expo, we can bring that dream to life — by creating an AI-powered Language Learning Roleplay Bot.
In this guide, we will walk through how to:
- Build a conversational UI using Expo (React Native)
- Create an AI chat endpoint using Vercel AI SDK and Express
- Enable immersive, character-driven language conversation
- Conversations
Project Overview
We will be building an app that lets users pick a roleplay scenario — for example, ordering food at a restaurant or booking a flight. The AI then takes on a role (e.g., waiter, receptionist, teacher) and converses with the user in their target language, offering corrections and encouragement naturally.
The project is divided into two parts:
- Frontend (Expo) → Handles user input, chat UI, and communication with backend
- Backend (Node + Vercel AI SDK) → Uses OpenAI models (via Vercel AI SDK) to generate responses
Tech Stack
| Layer | Technology |
|---|---|
| Frontend | Expo (React Native), Tailwind CSS classes, TypeScript |
| Backend | Express.js, Vercel AI SDK (ai package), zod for schema validation |
| AI Model | OpenAI GPT-4o-mini (through Vercel AI SDK) |
| Hosting | Vercel for API / Expo Go or EAS for mobile app |
Setting up the Backend (AI Controller)
Let us start with our Express controller that handles chat requests. This endpoint uses the Vercel AI SDK to call OpenAI models in a structured way.
How It Works
- generateObject from ai SDK calls the OpenAI model in a structured manner.
- z.object() defines the schema for our expected output — in this case, just a single message.
- The prompt includes:
- The roleplay scenario (title)
- The character role (basePrompt)
- The target language
- The conversation history
This ensures responses stay consistent, concise, and immersive — ideal for language learning.
Building the Frontend with Expo
Now, let us build the mobile interface where users can chat with the AI.
How It Works
- Each user message is appended to the local messages state.
- The app sends the chat history and roleplay info to the backend endpoint.
- The backend uses generateObject() from the Vercel AI SDK to get a structured response.
- The response is displayed in the chat interface.
AI Challenges & How I Tackled Them
Building an AI-driven roleplay experience is about shaping its behavior consistently across dynamic scenarios. During development, I encountered several interesting AI-related challenges that required careful prompt design and structured validation.
1. Staying in Character and Context
The challenge:
Early on, the AI often drifted out of character — for example, the “Parisian barista” would suddenly start explaining grammar rules in English or forget the roleplay context after several messages.
Why it happened:
Language models like GPT depend heavily on prompt conditioning and the recency of conversation context. When the input messages grew longer, the AI would prioritize recent dialogue over the initial scenario setup.
My solution:
I strengthened the base prompt to explicitly remind the AI of its role, tone, and constraints at every call. I also included a clearly structured “ROLEPLAY CONTEXT” and “CONVERSATION GUIDELINES” section, so even mid-session, the model re-anchored itself to the scenario.
This simple improvement made a huge difference — the AI now stays consistently “in character,” maintaining immersion throughout the chat.
2. Unexpected or Unstructured Outputs
The challenge:
This caused parsing issues on the frontend and broke message rendering.
Why it happened:
By default, model responses are flexible and creative — which is great for conversation, but risky when the app expects structured JSON.
My solution:
I used the generateObject() method from the Vercel AI SDK combined with Zod validation.
The SDK ensures that the AI must respond in a JSON-compatible format matching the schema. If the output deviates, it’s automatically retried or flagged — ensuring stability and predictable results every time.
Future Enhancements
- Add speech recognition and text-to-speech for full voice interaction
- Store conversation history locally or in Firebase
- Support multi-language translation mode
- Use streamText() from Vercel AI SDK for real-time streaming replies
Conclusion
With Expo and Vercel AI SDK, you can quickly create powerful, structured, and context-aware chatbots. This roleplay-based approach to language learning feels alive — your AI teacher becomes a travel guide, a barista, or even a friend abroad.
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