Learning Objectives:
- Students will implement a decorator given a specification (Create level, Bloom's)
- Students will identify when to use decorators vs. alternative patterns (Analyze level)

Concept Scaffolding:
- Step 1: Functions as objects (prerequisite)
- Step 2: Closures (builds on Step 1)
- Step 3: Wrapper pattern (core)
- Step 4: Decorator syntax (syntax sugar, light)
- Step 5: Parameterized decorators (extension, optional)

→ Assessment: Can they implement @retry(max_attempts)? (validates both objectives)

undefined

def greet(name):
    return f"Hello, {name}"

undefined

def make_multiplier(n):
    def multiply(x):
        return x * n  # Closure: multiply "remembers" n
    return multiply

times_three = make_multiplier(3)
result = times_three(5)  # 15

def original_function(x):
    return x * 2

def wrapper(func):
    def inner(x):
        print("Before calling function")
        result = func(x)  # Call original
        print("After calling function")
        return result
    return inner

**AI Role**: Explain WHY wrapper pattern useful (separation of concerns)

def logger(func):
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        return func(*args, **kwargs)
    return wrapper

**AI Role**: Student writes decorator, AI suggests `*args, **kwargs` pattern

def repeat(times):
    def decorator(func):
        def wrapper(*args, **kwargs):
            for _ in range(times):
                result = func(*args, **kwargs)
            return result
        return wrapper
    return decorator

@repeat(3)
def say_hello():
    print("Hello!")

say_hello()  # Prints "Hello!" three times

**Self-Monitoring Check**:
- ✅ Cognitive load calculated (not arbitrary steps)
- ✅ Worked examples before practice (not explain-then-try)
- ✅ Validation checkpoints (not assumptions)
- ✅ Tier-appropriate load (B1: 3-5 concepts/step)
- ✅ Dependency order correct (foundation → complex)
- ✅ Step count optimal (5 steps, not 12)
- ✅ Layer-aligned (L2 AI collaboration where appropriate)

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