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+# TinyTorch Module Structure Design Document
+
+## Overview
+
+This document defines the standard structure for TinyTorch educational modules, ensuring consistency, educational effectiveness, and maintainability across all components.
+
+## Module Architecture Philosophy
+
+### Core Principles
+
+1. **Educational First**: Every module is designed for learning, not just functionality
+2. **Progressive Complexity**: Start simple, build complexity step by step
+3. **Real-World Connection**: Connect concepts to practical ML applications
+4. **Standalone Learning**: Each module should be self-contained
+5. **Professional Standards**: Use industry-standard patterns and practices
+
+### "Build → Use → Understand" Framework
+
+Each module follows this pedagogical pattern:
+- **Build**: Implement the component from scratch
+- **Use**: Apply it to real data and problems
+- **Understand**: Analyze behavior, trade-offs, and connections
+
+## Standard Module Structure
+
+### File Organization
+
+```
+modules/source/{module_name}/
+├── {module_name}_dev.py           # Main development file (Jupytext format)
+├── README.md                      # Module documentation and guide
+├── tests/                         # Module-specific tests (if needed)
+│   └── test_{module_name}.py      # Comprehensive test suite
+├── data/                          # Module-specific data files (if needed)
+│   └── sample_data.npy
+└── assets/                        # Images, diagrams, etc. (if needed)
+    └── architecture_diagram.png
+```
+
+### Development File Structure (`*_dev.py`)
+
+Every module development file follows this standardized structure:
+
+```python
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.17.1
+# ---
+
+# %% [markdown]
+"""
+# Module {N}: {Title} - {Brief Description}
+
+## 🎯 Learning Objectives
+- ✅ Build {core_concept} from scratch
+- ✅ Use it with real data ({specific_dataset})
+- ✅ Understand {key_insight}
+- ✅ Connect to {next_module} and production systems
+
+## 📚 What You'll Learn
+- **Conceptual**: {concept_explanation}
+- **Technical**: {implementation_details}
+- **Practical**: {real_world_applications}
+
+## 🛠️ What You'll Build
+- **Core Component**: {main_class_or_function}
+- **Supporting Functions**: {helper_functions}
+- **Integration Points**: {connections_to_other_modules}
+
+## 📊 Module Info
+- **Difficulty**: {⭐⭐⭐} (1-5 stars)
+- **Time Estimate**: {X-Y hours}
+- **Prerequisites**: {previous_modules}
+- **Next Steps**: {next_modules}
+"""
+
+# %%
+#| default_exp core.{module_name}
+
+# Standard imports
+import numpy as np
+import matplotlib.pyplot as plt
+from typing import Union, List, Tuple, Optional, Any
+import warnings
+warnings.filterwarnings('ignore')
+
+# Module-specific imports
+from pathlib import Path
+import sys
+
+# Add project root to path for imports
+project_root = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+# %% [markdown]
+"""
+## Step 1: Conceptual Foundation
+
+### What is {Concept}?
+
+**Definition**: {Clear, simple definition with examples}
+
+**Why it matters**: {Real-world motivation and ML context}
+
+**How it works**: {Intuitive explanation before math}
+
+**Visual examples**: {Concrete examples, diagrams, analogies}
+
+**Connection**: {How it builds on previous modules}
+
+### Mathematical Foundation
+
+{Mathematical concepts explained intuitively}
+
+### Real-World Applications
+
+{Specific examples in ML and AI}
+"""
+
+# %% [markdown]
+"""
+## Step 2: Implementation Planning
+
+### Design Decisions
+
+Before we implement, let's think about:
+1. **Interface Design**: How should users interact with this component?
+2. **Data Structures**: What internal representation makes sense?
+3. **Error Handling**: What can go wrong and how do we handle it?
+4. **Performance**: What are the computational considerations?
+5. **Integration**: How does this connect to other modules?
+
+### Implementation Strategy
+
+We'll build this component in stages:
+1. **Core Functionality**: {basic_implementation}
+2. **Enhanced Features**: {advanced_features}
+3. **Integration Points**: {connections}
+4. **Optimization**: {performance_improvements}
+"""
+
+# %% [markdown]
+"""
+## Step 3: Core Implementation
+
+### {Component Name}
+
+Let's implement the core component step by step.
+"""
+
+# %%
+#| export
+class {ComponentName}:
+    """
+    {Component description and purpose}
+    
+    This class implements {specific_functionality} for the TinyTorch framework.
+    
+    Args:
+        {parameter_descriptions}
+    
+    Example:
+        >>> {usage_example}
+    
+    Note:
+        {important_notes_or_warnings}
+    """
+    
+    def __init__(self, {parameters}):
+        """
+        Initialize the {component_name}.
+        
+        TODO: Implement initialization logic
+        
+        APPROACH:
+        1. {step_1_description}
+        2. {step_2_description}
+        3. {step_3_description}
+        
+        EXAMPLE:
+        Input: {input_example}
+        Expected: {expected_behavior}
+        
+        HINTS:
+        - {hint_1}
+        - {hint_2}
+        - {hint_3}
+        """
+        ### BEGIN SOLUTION
+        {instructor_implementation}
+        ### END SOLUTION
+    
+    def {method_name}(self, {parameters}) -> {return_type}:
+        """
+        {Method description}
+        
+        TODO: Implement {method_functionality}
+        
+        APPROACH:
+        1. {implementation_step_1}
+        2. {implementation_step_2}
+        3. {implementation_step_3}
+        
+        EXAMPLE:
+        Input: {concrete_input_example}
+        Expected output: {concrete_output_example}
+        Your code should: {specific_behavior_description}
+        
+        HINTS:
+        - {specific_hint_1}
+        - {specific_hint_2}
+        - {specific_hint_3}
+        """
+        ### BEGIN SOLUTION
+        {instructor_implementation}
+        ### END SOLUTION
+
+# %% [markdown]
+"""
+### 🧪 Comprehensive Test: {Component Name}
+
+Let's test our implementation thoroughly to make sure it works correctly.
+"""
+
+# %% nbgrader={"grade": true, "grade_id": "test-{component}-comprehensive", "locked": true, "points": 25, "schema_version": 3, "solution": false, "task": false}
+import pytest
+
+class Test{ComponentName}:
+    """Comprehensive test suite for {ComponentName}."""
+    
+    def test_initialization(self):
+        """Test component initialization."""
+        # Test basic initialization
+        component = {ComponentName}({basic_params})
+        assert {basic_assertion}
+        
+        # Test with different parameters
+        component2 = {ComponentName}({different_params})
+        assert {different_assertion}
+    
+    def test_core_functionality(self):
+        """Test core component functionality."""
+        component = {ComponentName}({params})
+        
+        # Test basic operation
+        result = component.{method_name}({input_data})
+        expected = {expected_result}
+        assert {assertion}, f"Expected {expected}, got {result}"
+        
+        # Test with different inputs
+        result2 = component.{method_name}({different_input})
+        assert {different_assertion}
+    
+    def test_edge_cases(self):
+        """Test edge cases and boundary conditions."""
+        component = {ComponentName}({params})
+        
+        # Test empty input
+        {edge_case_tests}
+        
+        # Test large input
+        {large_input_tests}
+        
+        # Test invalid input
+        with pytest.raises({ExpectedException}):
+            component.{method_name}({invalid_input})
+    
+    def test_integration(self):
+        """Test integration with other components."""
+        {integration_tests}
+
+def run_comprehensive_tests():
+    """Run all tests with educational feedback."""
+    print("🔬 Running comprehensive {component_name} tests...")
+    
+    test_class = Test{ComponentName}()
+    tests = [
+        ('Initialization', test_class.test_initialization),
+        ('Core Functionality', test_class.test_core_functionality),
+        ('Edge Cases', test_class.test_edge_cases),
+        ('Integration', test_class.test_integration)
+    ]
+    
+    passed = 0
+    total = len(tests)
+    
+    for test_name, test_func in tests:
+        try:
+            test_func()
+            print(f"✅ {test_name}: PASSED")
+            passed += 1
+        except Exception as e:
+            print(f"❌ {test_name}: FAILED - {e}")
+    
+    print(f"\n📊 Results: {passed}/{total} tests passed")
+    if passed == total:
+        print("🎉 All {component_name} tests passed!")
+        print("📈 Progress: {ComponentName} ✓")
+        return True
+    else:
+        print("⚠️  Some tests failed - check your implementation")
+        return False
+
+# Execute tests
+success = run_comprehensive_tests()
+
+# %% [markdown]
+"""
+## Step 4: Real-World Application
+
+### Using {ComponentName} with Real Data
+
+Let's see how our component works with actual data from {dataset_name}.
+"""
+
+# %%
+# Load real data for demonstration
+{real_data_loading_code}
+
+# Apply our component
+print("🔬 Testing with real data...")
+component = {ComponentName}({real_params})
+result = component.{method_name}(real_data)
+
+print(f"✅ Real data processing successful!")
+print(f"Input shape: {real_data.shape}")
+print(f"Output shape: {result.shape}")
+print(f"Sample output: {result[:5]}")  # Show first 5 elements
+
+# %% [markdown]
+"""
+### Visualization and Analysis
+
+Let's visualize what our component does to understand it better.
+"""
+
+# %%
+# Create visualization
+plt.figure(figsize=(12, 4))
+
+# Input visualization
+plt.subplot(1, 3, 1)
+{input_visualization_code}
+plt.title('Input Data')
+
+# Process visualization
+plt.subplot(1, 3, 2)
+{process_visualization_code}
+plt.title('{Component} Processing')
+
+# Output visualization
+plt.subplot(1, 3, 3)
+{output_visualization_code}
+plt.title('Output Data')
+
+plt.tight_layout()
+plt.show()
+
+print("📊 Visualization shows how {component_name} transforms the data")
+
+# %% [markdown]
+"""
+## Step 5: Integration and Next Steps
+
+### Connection to Other Modules
+
+This {component_name} connects to the broader TinyTorch ecosystem:
+
+- **Previous modules**: {previous_connections}
+- **Next modules**: {next_connections}
+- **Production use**: {production_applications}
+
+### Performance Considerations
+
+{performance_analysis}
+
+### Advanced Features (Optional)
+
+{advanced_features_description}
+"""
+
+# %% [markdown]
+"""
+## 🎯 Module Summary
+
+### What You've Built
+- ✅ **{ComponentName}**: {achievement_1}
+- ✅ **Real Data Integration**: {achievement_2}
+- ✅ **Comprehensive Testing**: {achievement_3}
+- ✅ **Visualization**: {achievement_4}
+
+### Key Insights
+- **Technical**: {technical_insight}
+- **Practical**: {practical_insight}
+- **Conceptual**: {conceptual_insight}
+
+### Next Steps
+- **Immediate**: {next_immediate_step}
+- **Advanced**: {next_advanced_step}
+- **Integration**: {next_integration_step}
+
+### Success Criteria
+Your module is complete when:
+1. **All tests pass**: Comprehensive testing shows everything works
+2. **Real data works**: Component processes actual ML data correctly
+3. **Integration ready**: Component exports to `tinytorch.core.{module_name}`
+4. **Understanding**: You can explain how and why it works
+
+Ready to move to the next module? Let's go! 🚀
+"""
+```
+
+## README Structure
+
+Every module should have a comprehensive README following this template:
+
+```markdown
+# {Module Name} Module
+
+## 📊 Module Info
+- **Difficulty**: {⭐⭐⭐} (1-5 stars)
+- **Time Estimate**: {X-Y hours}
+- **Prerequisites**: {previous_modules}
+- **Next Steps**: {next_modules}
+
+## Overview
+
+{Brief description of what this module teaches and why it matters}
+
+## Learning Goals
+
+{Specific learning objectives}
+
+## What You'll Implement
+
+{Detailed description of components to build}
+
+## Files
+
+{Description of all files in the module}
+
+## Usage
+
+{Code examples showing how to use the module}
+
+## Testing
+
+{Instructions for running tests}
+
+## Development Workflow
+
+{Step-by-step development process}
+
+## Key Concepts
+
+{Important concepts and takeaways}
+
+## Troubleshooting
+
+{Common issues and solutions}
+```
+
+## Testing Integration
+
+### Comprehensive Notebook Testing
+
+Each module includes comprehensive tests within the notebook:
+
+1. **Immediate Feedback**: Tests run as students implement
+2. **Educational Context**: Tests explain what they're checking
+3. **Professional Structure**: Uses pytest patterns
+4. **Visual Feedback**: Clear pass/fail indicators
+5. **Progress Tracking**: Shows completion status
+
+### Test Categories
+
+1. **Initialization Tests**: Component creation and setup
+2. **Functionality Tests**: Core operations and methods
+3. **Edge Case Tests**: Boundary conditions and error handling
+4. **Integration Tests**: Connections to other modules
+5. **Real Data Tests**: Performance with actual datasets
+
+## Visual Design Guidelines
+
+### Progress Indicators
+- 🔬 Testing phase
+- ✅ Success indicators
+- ❌ Failure indicators
+- 📊 Results summary
+- 🎉 Completion celebration
+- 📈 Progress tracking
+
+### Educational Formatting
+- **Bold** for key concepts
+- `Code` for technical terms
+- > Quotes for important notes
+- Lists for step-by-step processes
+- Tables for comparisons
+
+## Data Integration Standards
+
+### Real Data Requirements
+- Use production datasets (CIFAR-10, ImageNet, etc.)
+- Include data loading and preprocessing
+- Show performance with realistic scales
+- Demonstrate practical applications
+
+### Visualization Standards
+- Input/Process/Output flow diagrams
+- Before/after comparisons
+- Performance metrics
+- Error analysis plots
+
+## Export and Integration
+
+### NBDev Integration
+- `#| default_exp core.{module_name}` for package destination
+- `#| export` for production code
+- `#| hide` for instructor solutions
+- Proper imports and dependencies
+
+### Package Structure
+```
+tinytorch/
+├── core/
+│   ├── {module_name}.py    # Exported module code
+│   └── __init__.py         # Package initialization
+└── __init__.py             # Main package init
+```
+
+## Quality Checklist
+
+### Before Module Completion
+- [ ] All learning objectives addressed
+- [ ] Comprehensive tests implemented and passing
+- [ ] Real data integration working
+- [ ] Visualization and analysis included
+- [ ] README documentation complete
+- [ ] Code exports to package correctly
+- [ ] Integration with other modules tested
+- [ ] Performance considerations addressed
+
+### Educational Quality
+- [ ] Concepts explained clearly
+- [ ] Step-by-step implementation guidance
+- [ ] Real-world connections made
+- [ ] Visual learning aids included
+- [ ] Progressive complexity maintained
+- [ ] Student success criteria defined
+
+## Examples
+
+### Tensor Module Structure
+```python
+# Core tensor operations with comprehensive testing
+# Real data integration with NumPy arrays
+# Visual demonstrations of tensor operations
+# Integration with activation functions
+```
+
+### Activation Module Structure
+```python
+# Mathematical foundations explained
+# Multiple activation functions implemented
+# Real neural network data processing
+# Visualization of activation behaviors
+```
+
+### Layer Module Structure
+```python
+# Linear algebra foundations
+# Dense layer implementation
+# Real image classification example
+# Integration with tensor and activation modules
+```
+
+## Conclusion
+
+This standardized module structure ensures:
+- **Consistency** across all TinyTorch modules
+- **Educational effectiveness** through proven patterns
+- **Professional quality** with industry standards
+- **Maintainability** through clear organization
+- **Scalability** for future module additions
+
+Every module following this structure provides students with a complete, professional learning experience that builds both understanding and practical skills. 
\ No newline at end of file
diff --git a/docs/development/testing-design.md b/docs/development/testing-design.md
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+# TinyTorch Testing Design Document
+
+## Overview
+
+This document analyzes the current testing architecture and proposes a unified approach that eliminates redundancy while maximizing educational value and development efficiency.
+
+## Current Testing Structure (Analysis)
+
+### What We Have Now
+
+1. **Inline Tests** (in `*_dev.py` files)
+   - NBGrader cells with immediate feedback
+   - Test individual functions after implementation
+   - Labeled as "unit tests" but really immediate feedback
+   - Visual feedback with emojis and progress tracking
+
+2. **Module Tests** (in `tests/test_*.py` files)
+   - Comprehensive pytest suites
+   - Test entire module functionality
+   - Professional test structure with classes and fixtures
+   - Edge cases and error handling
+
+3. **Integration Tests** (planned)
+   - Cross-module workflows
+   - End-to-end pipelines
+
+4. **System Tests** (planned)
+   - Performance and scalability
+   - Production scenarios
+
+### Problems with Current Approach
+
+1. **Redundancy**: Testing the same functions twice with different approaches
+2. **Complexity**: Students need to understand two testing paradigms
+3. **Maintenance**: Changes require updating tests in multiple places
+4. **Artificial Distinction**: "Unit vs Module" tests are testing the same code
+5. **Scattered Feedback**: Tests are in different files with different formats
+
+## Proposed Unified Testing Architecture
+
+### Core Principle: Progressive Testing Within Notebooks
+
+Instead of separate test files, integrate comprehensive testing directly into the educational notebooks using a **"Build → Test → Build → Test"** rhythm.
+
+### Four-Stage Testing Pipeline
+
+```
+📚 Notebook Tests (Progressive)    →    🔗 Integration Tests    →    🚀 System Tests
+   ↓                                        ↓                         ↓
+Individual functions                   Cross-module workflows      Production scenarios
+Immediate feedback                     End-to-end pipelines       Performance & scale
+Educational context                    Real ML workflows          Robustness testing
+```
+
+### Stage 1: Progressive Notebook Testing
+
+**Replace both inline tests and module tests with comprehensive notebook testing:**
+
+```python
+# %% [markdown]
+"""
+### 🧪 Comprehensive Test: Tensor Creation
+
+This tests all tensor creation scenarios with real data and edge cases.
+"""
+
+# %% nbgrader={"grade": true, "grade_id": "test-tensor-creation", "locked": true, "points": 20, "schema_version": 3, "solution": false, "task": false}
+import pytest
+import numpy as np
+
+class TestTensorCreation:
+    """Comprehensive tensor creation tests."""
+    
+    def test_scalar_creation(self):
+        """Test scalar tensor creation."""
+        # Basic scalar
+        scalar = Tensor(5.0)
+        assert scalar.shape == ()
+        assert scalar.size == 1
+        assert scalar.data.item() == 5.0
+        
+        # Different types
+        int_scalar = Tensor(42)
+        assert int_scalar.dtype in [np.int32, np.int64]
+        
+        float_scalar = Tensor(3.14)
+        assert float_scalar.dtype == np.float32
+    
+    def test_vector_creation(self):
+        """Test vector tensor creation."""
+        # From list
+        vector = Tensor([1, 2, 3, 4, 5])
+        assert vector.shape == (5,)
+        assert vector.size == 5
+        assert np.array_equal(vector.data, np.array([1, 2, 3, 4, 5]))
+        
+        # From numpy array
+        np_array = np.array([10, 20, 30])
+        vector_from_np = Tensor(np_array)
+        assert np.array_equal(vector_from_np.data, np_array)
+    
+    def test_matrix_creation(self):
+        """Test matrix tensor creation."""
+        matrix = Tensor([[1, 2], [3, 4]])
+        assert matrix.shape == (2, 2)
+        assert matrix.size == 4
+        expected = np.array([[1, 2], [3, 4]])
+        assert np.array_equal(matrix.data, expected)
+    
+    def test_dtype_handling(self):
+        """Test data type handling."""
+        # Explicit dtype
+        float_tensor = Tensor([1, 2, 3], dtype='float32')
+        assert float_tensor.dtype == np.float32
+        
+        # Auto dtype detection
+        int_tensor = Tensor([1, 2, 3])
+        assert int_tensor.dtype in [np.int32, np.int64]
+    
+    def test_edge_cases(self):
+        """Test edge cases and error conditions."""
+        # Empty tensor
+        empty = Tensor([])
+        assert empty.shape == (0,)
+        assert empty.size == 0
+        
+        # Single element
+        single = Tensor([42])
+        assert single.shape == (1,)
+        assert single.size == 1
+        
+        # Large tensor
+        large = Tensor(list(range(1000)))
+        assert large.shape == (1000,)
+        assert large.size == 1000
+
+# Run the tests with visual feedback
+def run_tensor_creation_tests():
+    """Run tensor creation tests with educational feedback."""
+    print("🔬 Running comprehensive tensor creation tests...")
+    
+    test_class = TestTensorCreation()
+    tests = [
+        ('Scalar Creation', test_class.test_scalar_creation),
+        ('Vector Creation', test_class.test_vector_creation),
+        ('Matrix Creation', test_class.test_matrix_creation),
+        ('Data Type Handling', test_class.test_dtype_handling),
+        ('Edge Cases', test_class.test_edge_cases)
+    ]
+    
+    passed = 0
+    total = len(tests)
+    
+    for test_name, test_func in tests:
+        try:
+            test_func()
+            print(f"✅ {test_name}: PASSED")
+            passed += 1
+        except Exception as e:
+            print(f"❌ {test_name}: FAILED - {e}")
+    
+    print(f"\n📊 Results: {passed}/{total} tests passed")
+    if passed == total:
+        print("🎉 All tensor creation tests passed!")
+        print("📈 Progress: Tensor Creation ✓")
+    else:
+        print("⚠️  Some tests failed - check your implementation")
+    
+    return passed == total
+
+# Execute tests
+run_tensor_creation_tests()
+```
+
+### Benefits of Unified Approach
+
+1. **Single Source of Truth**: All tests in one place
+2. **Educational Context**: Tests explain what they're checking
+3. **Immediate Feedback**: Students see results instantly
+4. **Professional Structure**: Uses pytest patterns within notebooks
+5. **Comprehensive Coverage**: Covers functionality, edge cases, and errors
+6. **Visual Learning**: Clear pass/fail feedback with explanations
+
+### Stage 2: Integration Testing
+
+**Test cross-module workflows in dedicated integration files:**
+
+```python
+# tests/integration/test_basic_ml_pipeline.py
+def test_tensor_to_activations_pipeline():
+    """Test tensor → activation function workflow."""
+    from tinytorch.core.tensor import Tensor
+    from tinytorch.core.activations import ReLU
+    
+    # Create tensor
+    x = Tensor([-1, 0, 1, 2])
+    
+    # Apply activation
+    relu = ReLU()
+    y = relu(x)
+    
+    # Verify pipeline
+    expected = Tensor([0, 0, 1, 2])
+    assert np.array_equal(y.data, expected.data)
+```
+
+### Stage 3: System Testing
+
+**Test production scenarios in dedicated system files:**
+
+```python
+# tests/system/test_performance.py
+def test_tensor_operations_performance():
+    """Test tensor operations with large data."""
+    import time
+    
+    # Large tensor operations
+    large_tensor = Tensor(np.random.randn(10000, 1000))
+    
+    start = time.time()
+    result = large_tensor + large_tensor
+    duration = time.time() - start
+    
+    # Should complete within reasonable time
+    assert duration < 1.0, f"Operation took {duration:.2f}s, expected < 1.0s"
+```
+
+## Implementation Strategy
+
+### Phase 1: Consolidate Notebook Testing
+1. **Remove duplicate tests** - eliminate separate module test files
+2. **Enhance notebook tests** - make them comprehensive with pytest structure
+3. **Add visual feedback** - maintain educational value with progress tracking
+4. **Standardize format** - consistent test structure across all modules
+
+### Phase 2: Implement Integration Testing
+1. **Create integration test taxonomy** - basic ML, vision, data pipelines
+2. **Implement cross-module tests** - verify components work together
+3. **Test real workflows** - end-to-end ML scenarios
+
+### Phase 3: Implement System Testing
+1. **Performance testing** - speed, memory, throughput
+2. **Scalability testing** - large datasets, batch processing
+3. **Robustness testing** - error handling, edge cases
+
+## Module Testing Guidelines
+
+### Structure for Each Module
+
+```python
+# %% [markdown]
+"""
+# Module X: Component Testing
+
+This section contains comprehensive tests for all module functionality.
+Tests are organized by component and include:
+- ✅ Basic functionality
+- ✅ Edge cases
+- ✅ Error handling
+- ✅ Integration points
+"""
+
+# %% [markdown]
+"""
+### 🧪 Component A Tests
+Tests for the first major component...
+"""
+
+# %% nbgrader={"grade": true, "grade_id": "test-component-a", "locked": true, "points": 25, "schema_version": 3, "solution": false, "task": false}
+# Comprehensive Component A tests here...
+
+# %% [markdown]
+"""
+### 🧪 Component B Tests
+Tests for the second major component...
+"""
+
+# %% nbgrader={"grade": true, "grade_id": "test-component-b", "locked": true, "points": 25, "schema_version": 3, "solution": false, "task": false}
+# Comprehensive Component B tests here...
+
+# %% [markdown]
+"""
+### 🧪 Integration Tests
+Tests for how components work together...
+"""
+
+# %% nbgrader={"grade": true, "grade_id": "test-integration", "locked": true, "points": 25, "schema_version": 3, "solution": false, "task": false}
+# Integration tests here...
+```
+
+### Test Execution
+
+Students run tests within notebooks:
+```python
+# All tests run automatically as cells execute
+# No separate commands needed
+# Immediate feedback and progress tracking
+```
+
+Instructors can also run centralized testing:
+```bash
+# Run all notebook tests
+tito test --all
+
+# Run specific module
+tito test --module tensor
+
+# Run integration tests
+tito test --integration
+
+# Run system tests
+tito test --system
+```
+
+## Migration Plan
+
+### Step 1: Audit Current Tests
+- [ ] Identify overlapping tests between inline and module tests
+- [ ] Catalog test coverage gaps
+- [ ] Document test dependencies
+
+### Step 2: Consolidate Testing
+- [ ] Merge inline and module tests into comprehensive notebook tests
+- [ ] Remove duplicate test files
+- [ ] Update CLI to support notebook testing
+
+### Step 3: Enhance Coverage
+- [ ] Add missing edge cases to notebook tests
+- [ ] Improve error handling tests
+- [ ] Add performance considerations
+
+### Step 4: Implement Integration/System Testing
+- [ ] Create integration test taxonomy
+- [ ] Implement cross-module tests
+- [ ] Add system performance tests
+
+## Conclusion
+
+The unified testing approach eliminates redundancy while providing better educational value and development efficiency. Students get comprehensive testing within their learning context, while instructors maintain professional testing standards for production validation.
+
+**Key Benefits:**
+- **Simplified**: One testing approach, not multiple
+- **Educational**: Tests explain what they're checking
+- **Comprehensive**: Full coverage within notebooks
+- **Professional**: Uses industry-standard pytest patterns
+- **Efficient**: No duplicate maintenance burden 
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