This unit teaches students how to use generative and analytical AI tools to improve code quality, automate testing and documentation, and investigate model and dataset resources. Building on foundational AI concepts from earlier units, students will apply AI-driven refactoring, bug detection, TDD, and documentation workflows while surveying major platforms to prepare for advanced integration and deployment in the final unit.
Learning Objectives
- Analyze a codebase and apply AI-generated refactoring suggestions to improve maintainability, demonstrating measurable reductions in complexity or increases in readability
- Apply AI tools to detect, diagnose, and automate fixes for bugs while implementing test-driven development practices and executing unit tests to verify corrections
- Demonstrate creation of clear visual and API documentation using AI-assisted generation and automation techniques, including producing diagrams and versioned docs for a sample project
- Evaluate multiple AI coding platforms and resources (e.g., OpenAI, DeepMind AlphaCode, Google Bard, dataset search, and edX materials) for reliability, data provenance, and suitability, and incorporate ethical and prompt-engineering best practices when selecting tools
Curriculum
- 1 Section
- 15 Lessons
- 30 hours
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- Unit 3 — AI-Assisted Code Development15
- 1.1Generating Refactoring Suggestions20 mins
- 1.2Detecting Bugs with AI20 mins
- 1.3Test-Driven Development with AI20 mins
- 1.4Creating Visual Documentation20 mins
- 1.5Enhancing Code Quality with AI20 mins
- 1.6DeepMind AlphaCode20 mins
- 1.7Google’s Dataset Search20 mins
- 1.8Using AI as a Tutor20 mins
- 1.9Illuminating Generative AI Models20 mins
- 1.10Diving into the OpenAI Platform20 mins
- 1.11Tips and Tricks for Code Generation20 mins
- 1.12Automating Bug Fixes with AI20 mins
- 1.13Automating API Documentation with AI20 mins
- 1.14Google Bard20 mins
- 1.15edX20 mins
Instructor
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