default_max_attempts_per_step: 3
classifier_model: "MODEL_1"
feedback_model: "MODEL_1"

tokens_for_ai_rubric: |
  You are an enthusiastic evolution scientist teaching genetic algorithms! 🧬

  Use the evolution metaphor throughout - "breeding," "survival of the fittest," "mutations."
  Be encouraging and celebrate when students grasp concepts.

  The user's programming language is stored in metadata.programming_language (if set).
  Always provide feedback in their chosen language.

  When evaluating code:
  - Check if it implements the core concept (not perfect syntax)
  - Look for understanding of: fitness, selection, crossover, mutation
  - Praise creative approaches
  - Guide gently if they're struggling

sections:
  - section_id: "introduction"
    title: "Welcome to Genetic Algorithms"
    steps:
      - step_id: "welcome"
        title: "Welcome"
        content_blocks:
          - "# 🧬 Welcome to Genetic Algorithms: Evolution in Code! 🧬"
          - ""
          - "Ever wondered how nature solves complex optimization problems?"
          - ""
          - "**Nature's secret**: Evolution! 🌱➡️🌳"
          - ""
          - "- **Reproduce** the best solutions"
          - "- **Combine** traits from parents (crossover)"
          - "- **Mutate** randomly for diversity"
          - "- **Repeat** for many generations"
          - ""
          - "Today, you'll build a genetic algorithm that evolves solutions to problems that would take billions of years to solve by brute force!"
          - ""
          - "Let's start by choosing your programming language..."

      - step_id: "choose_language"
        title: "Choose Programming Language"
        question: "What programming language would you like to use? (Python, JavaScript, Java, C++, Ruby, Go, Rust, or any language you prefer)"
        tokens_for_ai: |
          Extract the programming language from their response.
          Accept ANY language they mention: Python, JavaScript, Java, C++, C#, Ruby, Go, Rust, PHP, Swift, Kotlin, R, etc.

          Categorize as 'language_chosen' if they name a specific language.
          Categorize as 'unsure' if they seem uncertain or ask for a recommendation.
          Categorize as 'off_topic' if completely unrelated.
        buckets: [language_chosen, unsure, off_topic, set_language]
        transitions:
          language_chosen:
            metadata_add:
              programming_language: "the-users-response"
            ai_feedback:
              tokens_for_ai: |
                Great choice! Celebrate their language selection.
                Mention one reason why their language is good for genetic algorithms.
                (e.g., Python has great list operations, JavaScript has functional programming, etc.)
            next_section_and_step: "concepts:evolution_metaphor"
          unsure:
            content_blocks:
              - "No worries! 😊"
              - ""
              - "**I recommend Python** for beginners - it's clear and readable."
              - "**JavaScript** is great if you're web-focused."
              - "**C++** or **Rust** if you want performance."
              - ""
              - "Pick whichever you're most comfortable with - genetic algorithms work in ANY language!"
            counts_as_attempt: false
            next_section_and_step: "introduction:choose_language"
          off_topic:
            content_blocks:
              - "Let's focus on choosing a programming language first! 🎯"
              - ""
              - "Popular choices: Python, JavaScript, Java, C++, Ruby, Go, Rust"
              - ""
              - "Which language would you like to use?"
            counts_as_attempt: false
            next_section_and_step: "introduction:choose_language"
          set_language:
            content_blocks:
              - "Language preference updated! 🌍"
            metadata_add:
              language: "the-users-response"
            counts_as_attempt: false
            next_section_and_step: "introduction:choose_language"

  - section_id: "concepts"
    title: "Understanding Genetic Algorithms"
    steps:
      - step_id: "evolution_metaphor"
        title: "The Evolution Metaphor"
        content_blocks:
          - "# 🦎 How Evolution Solves Complex Problems 🦎"
          - ""
          - "Imagine you want to find the **perfect solution** to a problem."
          - ""
          - "**Brute Force**: Try every possibility ❌"
          - "- Problem: 10 variables, 100 values each = 100^10 = 100 trillion trillion possibilities!"
          - "- Would take longer than the age of the universe 🌌"
          - ""
          - "**Genetic Algorithm**: Let solutions evolve ✅"
          - "- Start with random guesses (generation 1)"
          - "- Keep the best ones"
          - "- Breed them together (crossover)"
          - "- Add random mutations"
          - "- Repeat for 100 generations"
          - "- Find excellent solutions in seconds! ⚡"
          - ""
          - "This is how nature designed complex organisms over millions of years."
          - "We'll do it in code in minutes! 🧬"

      - step_id: "ga_components"
        title: "Genetic Algorithm Components"
        content_blocks:
          - "# 🧬 The 5 Core Components of Genetic Algorithms"
          - ""
          - "## 1️⃣ **Population** (Pool of Candidates)"
          - "- A collection of potential solutions"
          - "- Each solution is called a **chromosome**"
          - "- Example: Random strings trying to match \"GENETIC\""
          - ""
          - "## 2️⃣ **Fitness Function** (Survival Test)"
          - "- Measures how good each solution is"
          - "- Better fitness = more likely to survive"
          - "- Example: Count matching letters in the string"
          - ""
          - "## 3️⃣ **Selection** (Choose the Best)"
          - "- Pick the fittest individuals to reproduce"
          - "- Methods: Tournament, Roulette Wheel, Elite Selection"
          - "- Survival of the fittest! 💪"
          - ""
          - "## 4️⃣ **Crossover** (Breeding)"
          - "- Combine two parent solutions"
          - "- Create offspring with mixed traits"
          - "- Example: \"GEN\" + \"TIC\" = \"GENIC\""
          - ""
          - "## 5️⃣ **Mutation** (Random Changes)"
          - "- Randomly modify some offspring"
          - "- Prevents getting stuck in local optima"
          - "- Adds diversity to the gene pool 🌈"

      - step_id: "understand_components"
        title: "Check Understanding"
        question: "In your own words, why do we need BOTH crossover AND mutation in genetic algorithms? (Hint: Think about what each one does for the solution space)"
        tokens_for_ai: |
          Categorize their understanding:

          'deep_understanding' if they mention BOTH:
          - Crossover combines good traits from parents (exploitation)
          - Mutation explores new possibilities and prevents premature convergence (exploration)

          'partial_understanding' if they mention ONE of:
          - Crossover combines solutions
          - Mutation adds randomness/diversity

          'creative_thinking' if wrong but shows good reasoning about evolution/optimization

          'needs_help' if confused or very brief

          'set_language' if changing language preference

          'off_topic' otherwise
        feedback_tokens_for_ai: |
          Provide feedback in their chosen language from metadata.programming_language.

          If deep_understanding: Celebrate! Explain this is the exploration-exploitation tradeoff.
          If partial_understanding: Acknowledge what they got right, add the missing piece.
          If creative_thinking: Appreciate their reasoning, gently guide to the core concept.
          If needs_help: Use an analogy - crossover is like breeding dogs (mix best traits), mutation is like genetic mutations (new random traits).
        buckets: [deep_understanding, partial_understanding, creative_thinking, needs_help, set_language, off_topic]
        transitions:
          deep_understanding:
            ai_feedback:
              tokens_for_ai: "Celebrate their understanding! Mention the exploration-exploitation tradeoff is key to many optimization algorithms."
            next_section_and_step: "problem:define_problem"
          partial_understanding:
            ai_feedback:
              tokens_for_ai: "Acknowledge what they got right. Explain the missing piece (exploration vs exploitation). Be encouraging!"
            next_section_and_step: "problem:define_problem"
          creative_thinking:
            ai_feedback:
              tokens_for_ai: "Appreciate their creative thinking! Guide them to the core: crossover=exploit good solutions, mutation=explore new ones."
            next_section_and_step: "problem:define_problem"
          needs_help:
            content_blocks:
              - "Let me clarify! 🎯"
              - ""
              - "**Crossover** = Combine the BEST traits from parents"
              - "- Focuses on what's already working"
              - "- Exploitation of good solutions"
              - ""
              - "**Mutation** = Random changes"
              - "- Explores NEW possibilities"
              - "- Prevents getting stuck"
              - ""
              - "**Together** = Perfect balance of using what works + trying new things! 🧬"
            next_section_and_step: "problem:define_problem"
          set_language:
            content_blocks:
              - "Language preference updated! 🌍"
            metadata_add:
              language: "the-users-response"
            counts_as_attempt: false
            next_section_and_step: "concepts:understand_components"
          off_topic:
            content_blocks:
              - "Let's stay focused on genetic algorithms! 🧬"
              - ""
              - "Think about why we need BOTH crossover (combining solutions) AND mutation (random changes)."
            counts_as_attempt: false
            next_section_and_step: "concepts:understand_components"

  - section_id: "problem"
    title: "Define the Problem"
    steps:
      - step_id: "define_problem"
        title: "Our Evolution Challenge"
        content_blocks:
          - "# 🎯 The String Evolution Challenge"
          - ""
          - "**Goal**: Evolve random characters into the string \"GENETIC\""
          - ""
          - "**Starting Point**:"
          - "- Population of 100 random 7-letter strings"
          - "- Example: \"XQMZPRL\", \"KDJFHGA\", \"BVNCXZM\""
          - "- Fitness = 0 (no matching letters)"
          - ""
          - "**After 100 Generations**:"
          - "- Best solution: \"GENETIC\""
          - "- Fitness = 7 (perfect match!)"
          - "- We'll watch evolution happen! 🧬➡️✨"
          - ""
          - "**Why This Problem?**"
          - "- Easy to understand fitness (count matching letters)"
          - "- Brute force: 26^7 = 8 billion possibilities"
          - "- GA solves it in ~100 generations with population of 100 = 10,000 evaluations"
          - "- **800,000x faster than brute force!** ⚡"
          - ""
          - "Let's build it step by step..."

  - section_id: "implementation"
    title: "Build the Genetic Algorithm"
    steps:
      - step_id: "fitness_function"
        title: "Step 1: Fitness Function"
        question: "Write a fitness function that takes a candidate string and returns how many letters match \"GENETIC\" in the correct positions. Think about how you'd measure similarity!"
        tokens_for_ai: |
          Evaluate their fitness function code in their chosen language (metadata.programming_language).

          'excellent_implementation' if they:
          - Compare each character position
          - Count matches
          - Handle string comparison correctly
          - Code looks reasonable (don't nitpick syntax)

          'correct_concept' if they describe the approach correctly even if code has minor issues

          'partial_understanding' if they count total matching letters but not position-specific

          'needs_guidance' if confused or very incomplete

          'set_language' if changing language

          'off_topic' otherwise
        feedback_tokens_for_ai: |
          Provide feedback in their language (metadata.programming_language).

          If excellent_implementation:
          - Celebrate! Show how this fitness function guides evolution.
          - Mention: "This is the KEY - fitness drives everything!"

          If correct_concept or partial_understanding:
          - Acknowledge their understanding
          - If not position-specific, explain why positions matter
          - Show a working example of the fitness function

          If needs_guidance:
          - Provide a complete working example
          - Explain: loop through each position, count matches
          - Walk through: "GXXXXXX" vs "GENETIC" = fitness of 1
        buckets: [excellent_implementation, correct_concept, partial_understanding, needs_guidance, set_language, off_topic]
        transitions:
          excellent_implementation:
            ai_feedback:
              tokens_for_ai: "Celebrate! Show example: fitness('GXXXXXX') = 1, fitness('GENETIC') = 7. Mention this guides ALL evolution!"
            metadata_add:
              fitness_complete: "true"
              progress_score: "1"
            next_section_and_step: "implementation:selection"
          correct_concept:
            ai_feedback:
              tokens_for_ai: "Great concept! Show a polished working version in their language. Explain how it works step-by-step."
            metadata_add:
              fitness_complete: "true"
              progress_score: "1"
            next_section_and_step: "implementation:selection"
          partial_understanding:
            ai_feedback:
              tokens_for_ai: "Good start! Explain why POSITION matters. Show corrected version comparing index-by-index."
            metadata_add:
              fitness_complete: "true"
              progress_score: "1"
            next_section_and_step: "implementation:selection"
          needs_guidance:
            ai_feedback:
              tokens_for_ai: "No worries! Provide complete working fitness function in their language. Walk through example: 'GXXXXXX' scores 1 because only first 'G' matches."
            metadata_add:
              fitness_complete: "true"
              progress_score: "1"
            next_section_and_step: "implementation:selection"
          set_language:
            content_blocks:
              - "Language preference updated! 🌍"
            metadata_add:
              language: "the-users-response"
            counts_as_attempt: false
            next_section_and_step: "implementation:fitness_function"
          off_topic:
            content_blocks:
              - "Let's focus on the fitness function! 🎯"
              - ""
              - "Your task: Write code that counts how many letters in a candidate string match \"GENETIC\" at the same positions."
              - ""
              - "Example: \"GXXXXXX\" should return 1 (only the G matches)"
            counts_as_attempt: false
            next_section_and_step: "implementation:fitness_function"

      - step_id: "selection"
        title: "Step 2: Selection (Choose the Fittest)"
        question: "Write a selection function that picks the best individuals from the population. Describe your strategy: will you use tournament selection (pick best from random groups), elite selection (just take the top N), or another method?"
        tokens_for_ai: |
          Evaluate their selection implementation/strategy.

          'excellent_implementation' if they:
          - Describe a valid selection method (tournament, elite, roulette wheel, etc.)
          - Show code or clear algorithm
          - Understand it favors higher fitness

          'correct_strategy' if they describe a valid approach even without perfect code

          'creative_approach' if they invent a reasonable selection method

          'needs_guidance' if confused or missing the "favor fitness" concept

          'set_language' if changing language

          'off_topic' otherwise
        feedback_tokens_for_ai: |
          Provide feedback in their language (metadata.programming_language).

          If excellent_implementation:
          - Praise their approach!
          - Explain why their method works (survival of fittest)
          - Show example: population of 100 → select top 50 for breeding

          If correct_strategy or creative_approach:
          - Validate their thinking
          - Show a clean implementation
          - Mention: "Selection pressure drives evolution!"

          If needs_guidance:
          - Explain selection favors fit individuals
          - Provide tournament selection example: pick 5 random, take the best, repeat
          - Or elite selection: sort by fitness, take top 50%
        buckets: [excellent_implementation, correct_strategy, creative_approach, needs_guidance, set_language, off_topic]
        transitions:
          excellent_implementation:
            ai_feedback:
              tokens_for_ai: "Fantastic! Explain how their selection method creates selection pressure. Show example with fitnesses [7,5,3,1] → likely picks 7 and 5."
            metadata_add:
              selection_complete: "true"
              progress_score: "n+1"
            next_section_and_step: "implementation:crossover"
          correct_strategy:
            ai_feedback:
              tokens_for_ai: "Great strategy! Polish their idea with clean code example. Emphasize: this is survival of the fittest in action! 💪"
            metadata_add:
              selection_complete: "true"
              progress_score: "n+1"
            next_section_and_step: "implementation:crossover"
          creative_approach:
            ai_feedback:
              tokens_for_ai: "Love the creativity! Validate if their method favors fitness. Show how it compares to standard approaches."
            metadata_add:
              selection_complete: "true"
              progress_score: "n+1"
            next_section_and_step: "implementation:crossover"
          needs_guidance:
            ai_feedback:
              tokens_for_ai: "Let me help! Explain tournament selection: randomly pick 5 individuals, select the fittest, repeat. Show complete code example in their language."
            metadata_add:
              selection_complete: "true"
              progress_score: "n+1"
            next_section_and_step: "implementation:crossover"
          set_language:
            content_blocks:
              - "Language preference updated! 🌍"
            metadata_add:
              language: "the-users-response"
            counts_as_attempt: false
            next_section_and_step: "implementation:selection"
          off_topic:
            content_blocks:
              - "Let's focus on selection! 🎯"
              - ""
              - "**Goal**: Pick the best individuals to be parents"
              - ""
              - "Think about: How do you favor high-fitness individuals while still allowing some diversity?"
            counts_as_attempt: false
            next_section_and_step: "implementation:selection"

      - step_id: "crossover"
        title: "Step 3: Crossover (Breeding)"
        question: "Write a crossover function that takes two parent strings and creates offspring by combining their genes. How will you mix the parents' traits?"
        tokens_for_ai: |
          Evaluate their crossover implementation.

          'excellent_implementation' if they:
          - Show code that combines two parent strings
          - Use any valid method (single-point, two-point, uniform)
          - Create offspring with mixed traits

          'correct_concept' if they describe crossover correctly even with imperfect code

          'creative_approach' if they invent a reasonable mixing strategy

          'needs_guidance' if confused or doesn't mix parent traits

          'set_language' if changing language

          'off_topic' otherwise
        feedback_tokens_for_ai: |
          Provide feedback in their language (metadata.programming_language).

          If excellent_implementation:
          - Celebrate! Show their crossover in action
          - Example: parent1="GENXXXX", parent2="XXXETIC" → child="GENETIC" (if lucky!)
          - Explain: "This is how good traits combine! 🧬"

          If correct_concept or creative_approach:
          - Validate their approach
          - Show polished implementation
          - Demo with example parents

          If needs_guidance:
          - Explain single-point crossover
          - Example: "GEN|XXXX" + "XXX|ETIC" → "GENETIC"
          - Provide complete code in their language
        buckets: [excellent_implementation, correct_concept, creative_approach, needs_guidance, set_language, off_topic]
        transitions:
          excellent_implementation:
            ai_feedback:
              tokens_for_ai: "Perfect! Show their crossover creating offspring. Example: 'GENXXXX' + 'XXXETIC' → 'GENETIC'. This is evolution magic! ✨"
            metadata_add:
              crossover_complete: "true"
              progress_score: "n+1"
            next_section_and_step: "implementation:mutation"
          correct_concept:
            ai_feedback:
              tokens_for_ai: "Great concept! Show refined code. Demo with concrete parent strings. Emphasize: this exploits existing good genes! 🧬"
            metadata_add:
              crossover_complete: "true"
              progress_score: "n+1"
            next_section_and_step: "implementation:mutation"
          creative_approach:
            ai_feedback:
              tokens_for_ai: "Interesting approach! Validate if it mixes parent traits. Compare to standard single-point crossover."
            metadata_add:
              crossover_complete: "true"
              progress_score: "n+1"
            next_section_and_step: "implementation:mutation"
          needs_guidance:
            ai_feedback:
              tokens_for_ai: "Let me show you! Explain single-point crossover with diagram. Provide complete working code in their language."
            metadata_add:
              crossover_complete: "true"
              progress_score: "n+1"
            next_section_and_step: "implementation:mutation"
          set_language:
            content_blocks:
              - "Language preference updated! 🌍"
            metadata_add:
              language: "the-users-response"
            counts_as_attempt: false
            next_section_and_step: "implementation:crossover"
          off_topic:
            content_blocks:
              - "Let's focus on crossover! 🧬"
              - ""
              - "**Goal**: Combine two parent strings to create offspring"
              - ""
              - "Think about: How do you mix traits from both parents into a child?"
              - "One approach: Take first half from parent1, second half from parent2"
            counts_as_attempt: false
            next_section_and_step: "implementation:crossover"

      - step_id: "mutation"
        title: "Step 4: Mutation (Random Changes)"
        question: "Write a mutation function that randomly changes some characters in a string with small probability (like 1% per character). How will you add this random diversity?"
        tokens_for_ai: |
          Evaluate their mutation implementation.

          'excellent_implementation' if they:
          - Show code that randomly modifies characters
          - Use low probability (1-10%)
          - Replace with random letters

          'correct_concept' if they describe mutation correctly even with imperfect code

          'creative_approach' if they use an alternative randomization strategy

          'needs_guidance' if confused or mutates too much/little

          'set_language' if changing language

          'off_topic' otherwise
        feedback_tokens_for_ai: |
          Provide feedback in their language (metadata.programming_language).

          If excellent_implementation:
          - Praise! Show mutation in action
          - Example: "GENETIC" → "GENXTIC" (small random change)
          - Explain: "Prevents getting stuck! Explores new possibilities! 🌈"

          If correct_concept or creative_approach:
          - Validate their understanding
          - Show clean implementation with proper probability
          - Demo: mutate 'GENETIC' a few times

          If needs_guidance:
          - Explain: loop through characters, 1% chance each mutates to random letter
          - Show complete code in their language
          - Warn: too much mutation = random search, too little = stuck
        buckets: [excellent_implementation, correct_concept, creative_approach, needs_guidance, set_language, off_topic]
        transitions:
          excellent_implementation:
            ai_feedback:
              tokens_for_ai: "Excellent! Demo their mutation. Explain: this is the spark of innovation in evolution! Small random changes = big discoveries. 🌈"
            metadata_add:
              mutation_complete: "true"
              progress_score: "n+1"
            next_section_and_step: "execution:main_loop"
          correct_concept:
            ai_feedback:
              tokens_for_ai: "Great understanding! Show polished code with ~1% mutation rate. Demo mutating 'GENETIC' several times."
            metadata_add:
              mutation_complete: "true"
              progress_score: "n+1"
            next_section_and_step: "execution:main_loop"
          creative_approach:
            ai_feedback:
              tokens_for_ai: "Creative! Validate their mutation strategy. Compare mutation rate to standard 1-5% per gene."
            metadata_add:
              mutation_complete: "true"
              progress_score: "n+1"
            next_section_and_step: "execution:main_loop"
          needs_guidance:
            ai_feedback:
              tokens_for_ai: "Let me guide you! Explain: for each character, 1% chance to replace with random letter A-Z. Provide complete code in their language."
            metadata_add:
              mutation_complete: "true"
              progress_score: "n+1"
            next_section_and_step: "execution:main_loop"
          set_language:
            content_blocks:
              - "Language preference updated! 🌍"
            metadata_add:
              language: "the-users-response"
            counts_as_attempt: false
            next_section_and_step: "implementation:mutation"
          off_topic:
            content_blocks:
              - "Let's focus on mutation! 🧬"
              - ""
              - "**Goal**: Randomly change some characters to add diversity"
              - ""
              - "Think about: For each character, maybe 1% chance to randomly change it to a different letter"
              - "Why? Prevents getting stuck in local optima!"
            counts_as_attempt: false
            next_section_and_step: "implementation:mutation"

  - section_id: "execution"
    title: "Run the Evolution!"
    steps:
      - step_id: "main_loop"
        title: "Step 5: The Evolution Loop"
        question: "Now write the main GA loop that ties everything together: (1) Create random population, (2) For each generation: evaluate fitness, select parents, crossover, mutate, (3) Repeat for 100 generations, (4) Print the best solution. Show me your implementation!"
        tokens_for_ai: |
          Evaluate their main GA loop implementation.

          'complete_implementation' if they:
          - Initialize random population
          - Have generation loop
          - Call fitness, selection, crossover, mutation
          - Track/print best solution

          'correct_structure' if they describe the algorithm correctly even with incomplete code

          'partial_implementation' if missing some components but core loop is there

          'needs_guidance' if confused or very incomplete

          'set_language' if changing language

          'off_topic' otherwise
        feedback_tokens_for_ai: |
          Provide feedback in their language (metadata.programming_language).

          If complete_implementation:
          - CELEBRATE! They built a complete GA! 🎉
          - Show example output:
            "Gen 1: Best='XQMZPRL' (fitness=0)
             Gen 50: Best='GENXTIX' (fitness=5)
             Gen 100: Best='GENETIC' (fitness=7) ✨"
          - Explain: "You just implemented evolution in code!"

          If correct_structure or partial_implementation:
          - Praise their understanding
          - Show complete polished version
          - Explain the flow: random → loop(fitness, select, breed, mutate) → evolved!

          If needs_guidance:
          - Provide complete working GA code in their language
          - Walk through: "This is the ENTIRE algorithm in ~50 lines!"
          - Show sample output across generations
        buckets: [complete_implementation, correct_structure, partial_implementation, needs_guidance, set_language, off_topic]
        transitions:
          complete_implementation:
            ai_feedback:
              tokens_for_ai: "AMAZING! 🎉 They built a complete genetic algorithm! Show example output with fitness improving over generations. Celebrate: 'You implemented EVOLUTION!' 🧬✨"
            metadata_add:
              ga_complete: "true"
              progress_score: "n+1"
              implementation_quality: "complete"
            next_section_and_step: "execution:observe_evolution"
          correct_structure:
            ai_feedback:
              tokens_for_ai: "Great structure! Show complete polished version with all components. Explain: this is the heart of evolutionary computation! 💚"
            metadata_add:
              ga_complete: "true"
              progress_score: "n+1"
              implementation_quality: "good"
            next_section_and_step: "execution:observe_evolution"
          partial_implementation:
            ai_feedback:
              tokens_for_ai: "Good start! Fill in missing pieces. Show complete working version. Emphasize: all the parts work together like an ecosystem! 🌱"
            metadata_add:
              ga_complete: "true"
              progress_score: "n+1"
              implementation_quality: "partial"
            next_section_and_step: "execution:observe_evolution"
          needs_guidance:
            ai_feedback:
              tokens_for_ai: "Let me show the complete algorithm! Provide full working GA code in their language (~50 lines). Walk through the flow. Show example output."
            metadata_add:
              ga_complete: "true"
              progress_score: "n+1"
              implementation_quality: "guided"
            next_section_and_step: "execution:observe_evolution"
          set_language:
            content_blocks:
              - "Language preference updated! 🌍"
            metadata_add:
              language: "the-users-response"
            counts_as_attempt: false
            next_section_and_step: "execution:main_loop"
          off_topic:
            content_blocks:
              - "Let's focus on the main evolution loop! 🔄"
              - ""
              - "You need to:"
              - "1. Create random population"
              - "2. Loop for 100 generations:"
              - "   - Calculate fitness for all"
              - "   - Select best individuals"
              - "   - Create offspring via crossover"
              - "   - Mutate offspring"
              - "   - Replace old population"
              - "3. Print the best solution found"
            counts_as_attempt: false
            next_section_and_step: "execution:main_loop"

      - step_id: "observe_evolution"
        title: "Observe Evolution in Action"
        content_blocks:
          - "# 🔬 Watch Evolution Happen! 🔬"
          - ""
          - "If you ran your genetic algorithm, you'd see something AMAZING:"
          - ""
          - "```"
          - "Generation 1:   Best='XQMZPRL'  Fitness=0  😕"
          - "Generation 10:  Best='GXXXXXX'  Fitness=1  🌱"
          - "Generation 25:  Best='GENXXXX'  Fitness=3  🌿"
          - "Generation 50:  Best='GENXTIX'  Fitness=5  🌳"
          - "Generation 75:  Best='GENETIX'  Fitness=6  🌲"
          - "Generation 100: Best='GENETIC'  Fitness=7  ✨🎉"
          - "```"
          - ""
          - "**What just happened?**"
          - "- Started with pure randomness"
          - "- Each generation got BETTER"
          - "- Good genes survived and spread"
          - "- Mutations found missing letters"
          - "- **EVOLUTION WORKED!** 🧬"
          - ""
          - "**The Math**:"
          - "- Brute force: 26^7 = 8,031,810,176 tries"
          - "- GA: 100 generations × 100 population = 10,000 tries"
          - "- **803,181x faster!** ⚡⚡⚡"
          - ""
          - "This is the power of evolutionary algorithms! 💪"

      - step_id: "when_to_use"
        title: "When to Use Genetic Algorithms"
        question: "Based on what you learned, when would you use a genetic algorithm versus other optimization methods? Think about problem characteristics that make GAs shine! 🤔"
        tokens_for_ai: |
          Evaluate their understanding of when GAs are appropriate.

          'excellent_insight' if they mention 2+ of:
          - Large search spaces (can't brute force)
          - No clear gradient/derivative (can't use gradient descent)
          - Multiple local optima (need exploration)
          - Complex fitness landscapes
          - Combinatorial optimization
          - Don't need perfect solution, just good enough

          'good_understanding' if they mention 1 key insight about search space or optimization landscape

          'partial_understanding' if they understand GAs are for hard problems but vague on details

          'needs_clarification' if confused or missing the key concepts

          'set_language' if changing language

          'off_topic' otherwise
        feedback_tokens_for_ai: |
          Provide feedback in their language (metadata.programming_language).

          If excellent_insight:
          - CELEBRATE their deep understanding! 🎉
          - Mention real applications: scheduling, circuit design, game AI, neural architecture search
          - Note: GAs are part of evolutionary computation family

          If good_understanding or partial_understanding:
          - Validate what they got right
          - Add missing pieces:
            * HUGE search spaces (can't enumerate)
            * Non-differentiable (can't gradient descent)
            * Multiple peaks (need exploration)
          - Give examples: TSP, job scheduling, game balancing

          If needs_clarification:
          - Explain: GAs excel when:
            * Search space is enormous
            * No gradient available
            * Many local optima to escape
          - Examples: routing problems, game AI, design optimization
        buckets: [excellent_insight, good_understanding, partial_understanding, needs_clarification, set_language, off_topic]
        transitions:
          excellent_insight:
            ai_feedback:
              tokens_for_ai: "Outstanding! 🌟 List real applications: job scheduling, circuit design, game AI, neural architecture search, traveling salesman. They've mastered when to use GAs!"
            metadata_add:
              activity_completed: "true"
              mastery_level: "excellent"
            next_section_and_step: "conclusion:celebrate"
          good_understanding:
            ai_feedback:
              tokens_for_ai: "Great insight! Add: GAs shine on huge search spaces, non-differentiable problems, multiple local optima. Give examples: TSP, scheduling, game AI."
            metadata_add:
              activity_completed: "true"
              mastery_level: "good"
            next_section_and_step: "conclusion:celebrate"
          partial_understanding:
            ai_feedback:
              tokens_for_ai: "You're on the right track! Explain: GAs work when search space is huge, no gradient, many peaks. Examples: routing, scheduling, design optimization."
            metadata_add:
              activity_completed: "true"
              mastery_level: "developing"
            next_section_and_step: "conclusion:celebrate"
          needs_clarification:
            content_blocks:
              - "Let me clarify when GAs are perfect! 🎯"
              - ""
              - "**Use Genetic Algorithms When:**"
              - ""
              - "✅ **Huge search space** (billions of possibilities)"
              - "✅ **No gradient** (can't use calculus-based optimization)"
              - "✅ **Many local optima** (need to explore, not just climb)"
              - "✅ **Combinatorial** (scheduling, routing, packing)"
              - "✅ **Good enough is enough** (don't need perfect solution)"
              - ""
              - "**Examples:**"
              - "- Traveling Salesman Problem 🗺️"
              - "- Job scheduling 📅"
              - "- Game AI balancing ⚔️"
              - "- Circuit design 🔌"
              - "- Neural architecture search 🧠"
              - ""
              - "GAs explore intelligently without needing derivatives or exhaustive search!"
            metadata_add:
              activity_completed: "true"
              mastery_level: "developing"
            next_section_and_step: "conclusion:celebrate"
          set_language:
            content_blocks:
              - "Language preference updated! 🌍"
            metadata_add:
              language: "the-users-response"
            counts_as_attempt: false
            next_section_and_step: "execution:when_to_use"
          off_topic:
            content_blocks:
              - "Let's think about when GAs are the right tool! 🔧"
              - ""
              - "Consider: What types of problems would benefit from evolutionary search?"
              - ""
              - "Hints:"
              - "- How big is the search space?"
              - "- Can you calculate gradients?"
              - "- Are there many local optima?"
            counts_as_attempt: false
            next_section_and_step: "execution:when_to_use"

  - section_id: "conclusion"
    title: "Conclusion"
    steps:
      - step_id: "celebrate"
        title: "Congratulations!"
        content_blocks:
          - "# 🎉 Congratulations, Evolution Architect! 🎉"
          - ""
          - "You just mastered genetic algorithms! Here's what you built:"
          - ""
          - "✅ **Fitness Function** - Measured solution quality"
          - "✅ **Selection** - Survival of the fittest"
          - "✅ **Crossover** - Breeding the best traits"
          - "✅ **Mutation** - Exploring new possibilities"
          - "✅ **Evolution Loop** - Bringing it all together"
          - ""
          - "**You learned:**"
          - "- How nature solves complex optimization problems"
          - "- Why evolution is an incredible search algorithm"
          - "- When to use GAs vs other optimization methods"
          - "- The exploration-exploitation tradeoff"
          - ""
          - "**Next Steps:**"
          - "- Try more complex problems (TSP, knapsack, game AI)"
          - "- Experiment with different selection/crossover strategies"
          - "- Learn about: Genetic Programming, Evolution Strategies, Neuroevolution"
          - "- Apply GAs to real optimization problems in your domain"
          - ""
          - "**Remember**: Evolution isn't just biology - it's a powerful computational paradigm! 🧬⚡"
          - ""
          - "Keep evolving your code! 🚀"
          - ""
          - "— Your Evolution Guide 🦎✨"