Building a WhatsApp Quizbot with Twilio

While the AI world is buzzing with multimodal agents and futuristic workflows, sometimes the most valuable tools are the simplest ones, especially when they solve a real, personal need.

I built this WhatsApp quizbot to help myself prepare for the CCSE exam, a requirement for obtaining Spanish citizenship. I needed something that was quick, practical, and fit into my daily routine, not the official mobile app with random questions or YouTube Videos. Using Twilio Studio and a lightweight Python backend on Anvil, I created a conversational assistant that sends quiz questions directly on WhatsApp and gives instant feedback.

Now, others preparing for the same exam can benefit too, and the same structure can be adapted to many other learning scenarios.

🧠 1 The Problem

Many people preparing for official exams (language tests, citizenship exams, or school entrance assessments) don’t have the time or motivation to study through traditional methods. Most tools are either mobile app based or lack personalization, and none meet people where they already are: on WhatsApp.

What if we could deliver a fast, friendly, and feedback-driven quiz experience directly inside WhatsApp?

🤖 2 The Solution(User experience)

Here's how the assistant work in practice:

1. The user starts the conversation with the CTA: “Hola, me gustaría practicar para el examen CCSE.”
2. The assistant responds by sending a WhatsApp quiz flow with 25 official questions, just like the real exam.
3. After each answer, the bot replies with instant feedback and simple NLP-based memory reinforcement techniques to help users retain the correct answer.
4. Once the user completes all 25 questions, the assistant provides a personalized performance summary and automatically moves on to the next batch of 25 questions
5. Over time, the user goes through all 300 official questions, in order ensuring full coverage and increasing retention through repetition.
   

🎛️ 3 System Architecture

The assistant is built with a simple but powerful architecture that combines Twilio’s WhatsApp API with a lightweight Python backend.

📞 Twilio Studio Flow

Manages the WhatsApp conversation flow, handles incoming messages, and routes user inputs to the backend via HTTP requests.

🐍 Anvil (Python Backend)

The backend acts as the brain of the operation, managing state and logic:

• Creates the WhatsApp Flow, presenting quizzes in batches of 25 questions via the Twilio API
• Fetches and sends the correct question batch based on the user's current position within the master list
• Validates answers instantly and applies logic for real-time feedback and NLP-based memorization prompts
• Returns Twilio-compatible messaging responses

🗄️ Anvil Embedded Data Tables

Stores:

• User progress (current question index)
• Score tracking
• Timestamps for session activity

🔁 Data Flow Overview:

WhatsApp → Twilio Studio → Anvil HTTP Endpoint → Logic + DataTables → Response → WhatsApp

💡 Why This Matters: Simplicity and Accessibility

While my recent work often involves designing complex AI-first systems and conversational interfaces powered by LLMs, this personal project was a refreshing reminder of a core product principle: solve the user's problem where they already are.

For me, that place was WhatsApp, and the solution didn't require a lot of AI at all, just smart design, a lightweight backend, and a focus on the user journey.

This project embodies what I've spent the last decade doing: merging software, human-centered design, and capability to drive real-world impact. Whether it's delivering accessible smart-courses via WhatsApp for IndiGo Academy or helping myself pass an exam, the goal is always to make technology accessible and intuitive.

🗣️ Let's Connect

This conversational architecture is highly adaptable. It could easily be repurposed for employee onboarding, product training, or daily knowledge reinforcements in a corporate setting.

• Are you studying for a similar exam? Give the bot a try CCSE Exam Preparation
• Are you a developer or PM interested in the code or the specific Twilio/Anvil setup? Send me a DM on LinkedIn