How to Build Simple Communication Boards with “AI Based Projects” Using Google’s Teachable Machine

Communication is a fundamental human right, yet many children and adults with speech or developmental challenges struggle to express their thoughts effectively. Traditional communication boards have long helped in bridging this gap, but now AI based projects like Google’s Teachable Machine are taking this innovation to a new level. With AI, families and educators can design custom, interactive, and adaptive communication boards tailored to individual needs. 🔹

In this article, you’ll learn step-by-step how to use Google’s Teachable Machine to create personalized communication aids, explore the role of AI in assistive technologies, and understand how to make these tools engaging for learners at home or school.

Understanding Communication Boards and Their Role in Accessibility

Communication boards are tools—digital or physical—that display symbols, pictures, or words to help non-verbal individuals convey their needs, feelings, or responses. They are widely used in speech therapy, autism education, and special needs learning.

Key Benefits of Communication Boards

• Encourage independent communication
• Support visual learning and comprehension
• Reduce frustration and improve social interaction
• Help caregivers understand non-verbal cues more effectively

Traditional boards, however, lack adaptability. That’s where AI based projects make a difference—offering personalization, real-time feedback, and dynamic content generation.

What Is Google’s Teachable Machine?

Google’s Teachable Machine is a free, web-based tool that allows anyone to train simple machine learning models without coding. It uses AI to recognize images, sounds, or poses—making it perfect for assistive tools like communication boards.

Features of Google’s Teachable Machine:

• No programming required
• Three model types: Image, Audio, and Pose
• Easy export options to integrate with websites or apps
• User-friendly interface for educators and beginners

According to Google Research, thousands of educators worldwide have used Teachable Machine to prototype accessibility tools, AI games, and inclusive learning experiences.

Why Use AI Based Projects for Communication Boards?

AI adds adaptability, personalization, and responsiveness that static boards lack. Using AI based projects ensures the board can recognize gestures, objects, or sounds, making it interactive and more intuitive for children.

Benefits of AI-Driven Communication Boards:

• Recognize multiple modes of input (voice, gestures, or images)
• Adapt responses based on user patterns
• Provide real-time visual or auditory feedback
• Encourage consistent communication practice

These features make AI-enhanced boards ideal for children with autism spectrum disorder (ASD), speech delays, or motor challenges.

Step-by-Step Guide: Building a Simple AI-Powered Communication Board

Let’s go through the process of creating a simple communication board using Google’s Teachable Machine. You can complete this project with just a webcam and basic images.

Step 1: Define the Communication Goal

Decide what your board will do:

• Identify facial expressions (happy, sad, tired)
• Recognize gestures (wave, point, thumbs up)
• Detect objects (toys, food items, daily use items)

Step 2: Collect and Upload Training Data

• Open Teachable Machine
• Select Image Project
• Create classes (e.g., “Yes,” “No,” “Hungry,” “Play”)
• Upload or record multiple examples for each category (around 20–30 per class)

Step 3: Train the Model

• Click Train Model and let Teachable Machine analyze your examples.
• Test with new images to check recognition accuracy.
• Adjust lighting or angle if needed for better precision.

Step 4: Export and Integrate

Once the model performs well:

• Export it as a TensorFlow.js or Python file.
• Integrate it into your web app or device-based interface.
• Link it with buttons, images, or text-to-speech outputs.

Integrating Text-to-Speech (TTS) for Real-Time Feedback

Pairing your AI based project with text-to-speech (TTS) software allows the system to respond verbally when a gesture or object is recognized. This bridges the communication gap even further.

Recommended Tools for TTS Integration:

Tool	Platform	Features
Google Cloud Text-to-Speech	Web	Natural voice output in 220+ voices
Amazon Polly	Web/API	Multilingual, lifelike speech synthesis
ResponsiveVoice	Browser	Quick setup, supports HTML integration

By combining Teachable Machine and TTS, you can create a board where a child shows an object (like a cup), and the AI responds with a voice saying, “I want water.”

Making Communication Boards Visually Engaging

Visual design plays a critical role in maintaining engagement. Use clear images, bold colors, and minimal text for accessibility.

Design Tips:

• Use contrasting backgrounds for clarity.
• Add emoji-based feedback to make it fun (e.g., smiling face when correct).
• Include animation or sound cues for positive reinforcement.

A study by Frontiers in Psychology found that children engage 50% more with visually enriched AI interfaces compared to static boards.

Example Project: Gesture-Based Emotion Communicator

This simple AI based project recognizes three gestures:

• Thumbs Up: Indicates “I’m happy.”
• Hand on Heart: Indicates “I need help.”
• Covering Face: Indicates “I’m upset.”

When linked with TTS, each gesture triggers a corresponding audio message, allowing children to express themselves quickly.

Customization Options:

• Add more gesture classes.
• Integrate haptic feedback (vibration or color change).
• Track frequency of emotions for therapy use.

Advantages of No-Code AI Projects for Parents and Teachers

Creating communication boards using no-code platforms like Teachable Machine helps parents and teachers become AI creators without needing programming knowledge.

Key Advantages:

• Cost-effective and customizable
• Encourages hands-on STEM learning
• Enables real-world applications for AI
• Promotes accessibility and inclusion

This approach aligns with UNESCO’s AI in Education framework, which emphasizes ethical, inclusive, and accessible AI technologies for learning.

Challenges and How to Overcome Them

While AI-based communication boards are powerful, some challenges include:

• Lighting inconsistencies affecting camera recognition
• Limited training data for diverse gestures
• Internet dependency for model hosting

Solutions:

• Use consistent background and lighting.
• Collect diverse data samples for training.
• Use offline export options available in Teachable Machine.

Future of AI-Based Communication Tools

As AI becomes more intuitive, tools like Google’s Teachable Machine will evolve into real-time adaptive communication systems. Future boards could:

• Recognize complex emotions
• Translate gestures into multiple languages
• Sync with wearable devices for health and mood tracking

These innovations will make communication tools not only assistive but empowering.

Conclusion

Creating communication boards using AI based projects like Google’s Teachable Machine is an accessible and impactful way to empower non-verbal learners. With simple setups, visual engagement, and real-time feedback, families and educators can build personalized tools that transform communication challenges into opportunities for expression and connection.

AI is not just about machines learning—it’s about people connecting better than ever before. 😊

FAQs

1. What is an AI-based communication board?

An AI-based communication board uses artificial intelligence to recognize gestures, sounds, or images and translate them into meaningful messages, helping non-verbal users communicate.

2. Is Google’s Teachable Machine suitable for beginners?

Yes, it’s perfect for beginners. The platform allows you to train AI models using images or sounds without any coding knowledge.

3. Can I use a smartphone to build and test my project?

Absolutely! You can use your smartphone camera for image recognition and even run the model on a mobile browser.

4. How accurate is Google’s Teachable Machine for gesture or object detection?

Accuracy depends on the quality and quantity of training data. With enough examples and consistent lighting, accuracy can reach over 90%.

5. What are some extensions of AI-based communication boards?

You can integrate your board with smart speakers, wearable devices, or web dashboards for remote tracking and advanced interaction.