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| """ | |
| Worked Example Weaver - Gradio App | |
| Personalized worked examples based on Cognitive Load Theory | |
| Built by the Virtual Academy at Bern University of Applied Sciences | |
| """ | |
| import gradio as gr | |
| from openai import OpenAI | |
| from pydantic import BaseModel, Field | |
| from typing import Literal | |
| import os | |
| from dotenv import load_dotenv | |
| # Load environment variables | |
| load_dotenv() | |
| # Initialize OpenAI client | |
| client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) | |
| # Data Models | |
| class LearnerProfile(BaseModel): | |
| """Collect learner information for personalization""" | |
| name: str = Field(description="Learner's first name") | |
| domain: Literal["programming", "health_sciences", "agronomy"] = Field(description="Learning domain") | |
| specific_interest: str = Field(description="Specific interest within domain") | |
| hobby_or_passion: str = Field(description="A hobby or passion they have") | |
| goal: str = Field(description="What they want to achieve") | |
| background_level: Literal["beginner", "intermediate", "advanced"] = Field(description="Current level") | |
| class PersonalizedWorkedExample(BaseModel): | |
| """A complete worked example tailored to the learner""" | |
| title: str = Field(description="Engaging title that incorporates learner's interest") | |
| problem_statement: str = Field(description="Problem framed in learner's context (3-4 sentences)") | |
| given_data: str = Field(description="Data presented in familiar context") | |
| step_by_step_solution: list[str] = Field(description="Clear steps with explanations") | |
| final_answer: str = Field(description="Final answer with interpretation") | |
| connection_to_goal: str = Field(description="How this relates to their goal (2-3 sentences)") | |
| practice_suggestion: str = Field(description="Similar problem they could try next") | |
| # Concept Library | |
| CONCEPTS = { | |
| "programming": [ | |
| {"name": "For Loops", "abstract": "Iterate through a sequence of items", "difficulty": "beginner", "typical_use": "Processing lists, repeating actions"}, | |
| {"name": "List Comprehensions", "abstract": "Create new lists using concise syntax", "difficulty": "intermediate", "typical_use": "Transform data, filter lists elegantly"}, | |
| {"name": "Dictionary Methods", "abstract": "Access and manipulate key-value pairs", "difficulty": "beginner", "typical_use": "Store related data, perform fast lookups"}, | |
| {"name": "Functions with Parameters", "abstract": "Create reusable code blocks that accept inputs", "difficulty": "beginner", "typical_use": "Organize code, avoid repetition"}, | |
| {"name": "String Formatting", "abstract": "Create formatted text output using f-strings", "difficulty": "beginner", "typical_use": "Display data, create messages dynamically"}, | |
| ], | |
| "health_sciences": [ | |
| {"name": "Mean and Standard Deviation", "abstract": "Describe central tendency and variability in data", "difficulty": "beginner", "typical_use": "Summarize health measurements"}, | |
| {"name": "Correlation Analysis", "abstract": "Measure strength and direction of relationship between two variables", "difficulty": "intermediate", "typical_use": "Find relationships in health data"}, | |
| {"name": "Linear Regression", "abstract": "Predict one variable from another using a straight-line relationship", "difficulty": "intermediate", "typical_use": "Predict outcomes, understand relationships"}, | |
| {"name": "Independent T-Test", "abstract": "Compare means between two independent groups", "difficulty": "intermediate", "typical_use": "Test intervention effectiveness"}, | |
| {"name": "Confidence Intervals", "abstract": "Estimate population parameters with uncertainty", "difficulty": "intermediate", "typical_use": "Interpret research findings"}, | |
| {"name": "Effect Size (Cohen's d)", "abstract": "Measure practical significance of differences", "difficulty": "intermediate", "typical_use": "Interpret research impact"}, | |
| ], | |
| "agronomy": [ | |
| {"name": "Yield Prediction", "abstract": "Estimate crop output based on inputs using regression", "difficulty": "intermediate", "typical_use": "Plan harvest, allocate resources"}, | |
| {"name": "NPK Optimization", "abstract": "Calculate optimal fertilizer ratios for maximum benefit", "difficulty": "intermediate", "typical_use": "Maximize yield while minimizing cost"}, | |
| {"name": "Growing Degree Days", "abstract": "Calculate heat accumulation for crop development", "difficulty": "beginner", "typical_use": "Predict crop stages, plan field operations"}, | |
| {"name": "Water Use Efficiency", "abstract": "Calculate crop yield per unit of water used", "difficulty": "beginner", "typical_use": "Optimize irrigation, compare varieties"}, | |
| {"name": "Cost-Benefit Analysis", "abstract": "Compare costs and returns of agricultural interventions", "difficulty": "intermediate", "typical_use": "Make informed decisions about inputs"}, | |
| ] | |
| } | |
| # System prompt for the AI | |
| SYSTEM_PROMPT = """You are an expert educator who creates highly personalized | |
| worked examples that connect abstract concepts to learners' lived experiences. | |
| CRITICAL INSTRUCTIONS: | |
| 1. Weave the learner's interests, hobbies, and goals naturally into the example | |
| 2. Use their name throughout to increase personal connection | |
| 3. Make data and scenarios feel authentic to their context | |
| 4. Keep explanations clear but connect to what they care about | |
| 5. Match complexity to their level (beginner/intermediate/advanced) | |
| 6. Make the connection to their goal explicit and motivating | |
| 7. Use concrete numbers and realistic data | |
| 8. For programming examples, include actual runnable code with comments | |
| 9. For quantitative examples, show all calculations step by step | |
| STRUCTURE YOUR EXAMPLES: | |
| - Start with an engaging title that mentions their interest | |
| - Frame the problem in their context | |
| - Present data that feels real to their situation | |
| - Walk through steps clearly with explanations | |
| - Connect the final answer to their goal | |
| - Suggest a related practice problem | |
| Remember: This is a WORKED EXAMPLE - a complete solution for the learner | |
| to study, not a problem for them to solve. | |
| """ | |
| def generate_example( | |
| name: str, | |
| domain: str, | |
| interest: str, | |
| hobby: str, | |
| goal: str, | |
| level: str, | |
| concept_name: str | |
| ) -> str: | |
| """Generate a personalized worked example""" | |
| # Validate inputs | |
| if not all([name, domain, interest, hobby, goal, level, concept_name]): | |
| return "⚠️ Please fill in all fields." | |
| # Create profile | |
| domain_key = domain.lower().replace(" ", "_").replace("(", "").replace(")", "").split()[0] | |
| if "programming" in domain.lower(): | |
| domain_key = "programming" | |
| elif "health" in domain.lower(): | |
| domain_key = "health_sciences" | |
| elif "agronomy" in domain.lower(): | |
| domain_key = "agronomy" | |
| # Find concept | |
| concepts = CONCEPTS.get(domain_key, []) | |
| concept = None | |
| for c in concepts: | |
| if concept_name.startswith(c["name"]): | |
| concept = c | |
| break | |
| if not concept: | |
| return f"⚠️ Concept not found: {concept_name}" | |
| profile = LearnerProfile( | |
| name=name, | |
| domain=domain_key, | |
| specific_interest=interest, | |
| hobby_or_passion=hobby, | |
| goal=goal, | |
| background_level=level | |
| ) | |
| # Generate example | |
| try: | |
| user_prompt = f""" | |
| Create a worked example for: | |
| LEARNER PROFILE: | |
| - Name: {profile.name} | |
| - Domain: {profile.domain} | |
| - Specific interest: {profile.specific_interest} | |
| - Hobby/passion: {profile.hobby_or_passion} | |
| - Goal: {profile.goal} | |
| - Level: {profile.background_level} | |
| CONCEPT TO TEACH: | |
| - Name: {concept['name']} | |
| - Abstract description: {concept['abstract']} | |
| - Difficulty: {concept['difficulty']} | |
| - Typical use: {concept['typical_use']} | |
| Create a worked example that teaches this concept using {profile.name}'s | |
| specific context. Make the problem realistic and the data believable. | |
| For programming: Include complete, runnable code with explanatory comments. | |
| For quantitative problems: Show every calculation step explicitly. | |
| """ | |
| # Call OpenAI with structured outputs | |
| response = client.responses.parse( | |
| model="gpt-5.1", | |
| input=[ | |
| {"role": "system", "content": SYSTEM_PROMPT}, | |
| {"role": "user", "content": user_prompt} | |
| ], | |
| text_format=PersonalizedWorkedExample | |
| ) | |
| example = response.output_parsed | |
| if not example: | |
| return "❌ Failed to generate example. Please try again." | |
| # Format output | |
| output = f"""# {example.title} | |
| ## 📋 The Problem | |
| {example.problem_statement} | |
| ### Given Data | |
| {example.given_data} | |
| --- | |
| ## 💡 Step-by-Step Solution | |
| """ | |
| for i, step in enumerate(example.step_by_step_solution, 1): | |
| output += f"**Step {i}:**\n\n{step}\n\n" | |
| output += f"""--- | |
| ## ✅ Final Answer | |
| {example.final_answer} | |
| --- | |
| ### 🎯 Why This Matters for You | |
| {example.connection_to_goal} | |
| --- | |
| ### 🚀 Try This Next | |
| {example.practice_suggestion} | |
| """ | |
| return output | |
| except Exception as e: | |
| return f"❌ Error generating example: {str(e)}\n\nPlease check your OpenAI API key." | |
| def update_concepts(domain: str): | |
| """Update concept dropdown based on selected domain""" | |
| domain_key = domain.lower().replace(" ", "_").replace("(", "").replace(")", "").split()[0] | |
| if "programming" in domain.lower(): | |
| domain_key = "programming" | |
| elif "health" in domain.lower(): | |
| domain_key = "health_sciences" | |
| elif "agronomy" in domain.lower(): | |
| domain_key = "agronomy" | |
| concepts = CONCEPTS.get(domain_key, []) | |
| choices = [f"{c['name']} ({c['difficulty']})" for c in concepts] | |
| return gr.Dropdown(choices=choices, value=None, label="Choose a concept") | |
| # Gradio Interface | |
| with gr.Blocks(title="Worked Example Weaver", theme=gr.themes.Soft()) as demo: | |
| gr.Markdown(""" | |
| # 🧵 Worked Example Weaver | |
| **Learn concepts through examples tailored to YOUR interests!** | |
| This tool demonstrates principles from **Cognitive Load Theory**: | |
| - **Worked Example Effect**: Studying complete solutions is more effective than struggling through problems | |
| - **Personalization Effect**: Familiar contexts reduce cognitive load and improve learning | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("## 👤 Step 1: Tell Us About Yourself") | |
| name = gr.Textbox(label="Your first name", placeholder="e.g., Maria", value="Alex") | |
| domain = gr.Dropdown( | |
| choices=["Programming (Python)", "Health Sciences (Statistics)", "Agronomy (Agricultural Science)"], | |
| label="Choose your learning domain", | |
| value="Programming (Python)" | |
| ) | |
| interest = gr.Textbox(label="Your specific interest in this domain", placeholder="e.g., web development, sports nutrition, coffee farming", value="data analysis") | |
| hobby = gr.Textbox(label="A hobby or passion you have", placeholder="e.g., photography, cycling, cooking", value="photography") | |
| goal = gr.Textbox(label="What you want to achieve", placeholder="e.g., build a portfolio site, improve performance, increase yield", value="automate my photo workflow") | |
| level = gr.Dropdown(choices=["beginner", "intermediate", "advanced"], label="Your current level", value="beginner") | |
| gr.Markdown("## 📚 Step 2: Choose a Concept to Learn") | |
| concept = gr.Dropdown( | |
| label="Choose a concept", | |
| choices=["For Loops (beginner)", "List Comprehensions (intermediate)", "Dictionary Methods (beginner)", "Functions with Parameters (beginner)", "String Formatting (beginner)"], | |
| value="List Comprehensions (intermediate)" | |
| ) | |
| domain.change(update_concepts, inputs=[domain], outputs=[concept]) | |
| generate_btn = gr.Button("✨ Generate My Personalized Example", variant="primary", size="lg") | |
| with gr.Column(): | |
| output = gr.Markdown(label="Your Personalized Worked Example") | |
| generate_btn.click( | |
| fn=generate_example, | |
| inputs=[name, domain, interest, hobby, goal, level, concept], | |
| outputs=[output] | |
| ) | |
| gr.Markdown(""" | |
| --- | |
| ## 📖 About This Tool | |
| ### Research Foundations | |
| **The Worked Example Effect** (Sweller, 1988) | |
| > "Novice learners who study worked examples perform better on subsequent tests than learners who solve problems themselves." | |
| **The Personalization Effect** (Cordova & Lepper, 1996) | |
| > "Familiar contexts require less cognitive effort to process, reducing extraneous cognitive load." | |
| ### Built With | |
| - [OpenAI GPT-5.1](https://platform.openai.com/docs/guides/latest-model) - Latest language model with structured outputs | |
| - [Gradio](https://gradio.app) - Web interface | |
| - [Pydantic](https://pydantic.dev) - Data validation | |
| --- | |
| **Created by the [Virtual Academy](https://virtuelleakademie.ch/) at Bern University of Applied Sciences** | |
| """) | |
| if __name__ == "__main__": | |
| demo.launch() | |