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A newer version of the Gradio SDK is available: 6.12.0
title: Worked Example Weaver
emoji: 🧵
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.42.0
app_file: app.py
pinned: false
license: cc0-1.0
🧵 Worked Example Weaver
Personalized Learning Through Cognitive Load Theory
An AI-powered web application that generates personalized worked examples tailored to individual learners' interests, goals, and contexts. Built on research-backed principles from Cognitive Load Theory.
What is this?
This tool demonstrates two key learning science principles:
The Worked Example Effect
"Novice learners who study worked examples perform better than learners who solve problems independently." — NSW Centre for Education Statistics and Evaluation (2017)
Why? Unguided problem-solving overloads working memory. Worked examples reduce cognitive load, freeing capacity for learning.
The Personalization Effect
Familiar contexts (your hobbies, interests, goals) are easier to process, further reducing cognitive load and improving learning outcomes.
Features
- 🎯 Three Learning Domains: Programming (Python), Health Sciences (Statistics), Agronomy
- 📚 16 Total Concepts: 5-6 concepts per domain with varying difficulty levels
- 🤖 AI-Powered Personalization: Uses OpenAI GPT-5.1 with structured outputs
- 🎨 Clean Web Interface: Built with Gradio for easy interaction
How to Use
Fill in your profile
- Name, domain, interests, hobbies, goals, skill level
Select a concept
- Choose from domain-specific concepts based on your learning domain
Generate your example
- Click the generate button and wait 30-60 seconds
Study the worked example
- Examine the complete solution step-by-step
- See how it connects to your personal goals
Supported Domains
🐍 Programming (Python)
- For loops
- List comprehensions
- Dictionary methods
- Functions with parameters
- String formatting
Example contexts: Recipe websites, photography portfolios, gaming tools
🏥 Health Sciences (Statistics)
- Mean and standard deviation
- Correlation analysis
- Linear regression
- Independent t-tests
- Confidence intervals
- Effect size (Cohen's d)
Example contexts: Sports performance, nutrition tracking, patient outcomes
🌾 Agronomy (Agricultural Science)
- Yield prediction
- NPK optimization
- Growing degree days
- Water use efficiency
- Cost-benefit analysis
Example contexts: Family farms, specific crops, sustainable agriculture
Technical Stack
- Gradio: Modern web interface for ML applications
- PydanticAI: Type-safe AI agent framework
- OpenAI GPT-5.1: Language model for generation
- Pydantic: Data validation using Python type hints
Research Foundations
This tool implements evidence-based learning principles:
Cooper, G., & Sweller, J. (1987). Effects of schema acquisition and rule automation on mathematical problem-solving transfer. Journal of Educational Psychology, 79(4), 347-362.
Cordova, D. I., & Lepper, M. R. (1996). Intrinsic motivation and the process of learning: Beneficial effects of contextualization, personalization, and choice. Journal of Educational Psychology, 88(4), 715.
NSW Centre for Education Statistics and Evaluation (2017). Cognitive load theory: Research that teachers really need to understand. Link
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257-285.
Extend This Tool
Ideas for enhancement:
- Add more domains (economics, chemistry, history, literature)
- Include images and diagrams in examples
- Create sequences of scaffolded examples
- Track learner progress over time
- Export examples to PDF or flashcards
- Add multilingual support
- Integrate with learning management systems
About
Created by the Virtual Academy at Bern University of Applied Sciences as part of the workshop "Building Personalized Worked Example Generators with AI."
Instructor: Dr. Andrew Ellis, Cognitive Psychologist
Workshop Materials: ki-lehre-advanced
License
CC0 1.0 Universal - Public Domain
Local Development
# Clone and install
git clone <repo>
cd worked-example-weaver-app
pip install -r requirements.txt
# Set OpenAI API key
export OPENAI_API_KEY="sk-your-key-here"
# Run locally
python app.py