This unit introduces students to essential AI tools and concepts used in modern software development, including code assistants (GitHub Copilot, Amazon CodeWhisperer, Tabnine), prompt engineering, and core machine learning and NLP ideas. Students will practice applying these tools to automate repetitive tasks, format and translate code, debug, and produce basic QA and maintenance artifacts to prepare for deeper project work in subsequent units.
Learning Objectives
- Analyze routine development tasks to identify opportunities for automation and document an AI-assisted workflow that reduces manual effort
- Apply foundational machine learning and natural-language processing concepts to explain how AI code assistants generate and transform code
- Demonstrate effective use of multiple code-assistant and documentation-bot tools (e.g., GitHub Copilot, Amazon CodeWhisperer, Tabnine) to generate, format, translate, and refactor code through iterative prompt improvement
- Evaluate code quality and maintenance needs by writing a basic test plan, classifying common bug types, and recommending AI-enabled debugging and maintenance strategies
Curriculum
- 1 Section
- 16 Lessons
- 30 hours
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- Unit 1 — AI-Assisted Coding Foundations16
- 1.1Banishing Boring Tasks20 mins
- 1.2Decoding Machine and Deep Learning20 mins
- 1.3Navigating GitHub Copilot20 mins
- 1.4Improving Your Prompts20 mins
- 1.5Understanding Project Requirements20 mins
- 1.6Using AI Tools for Code Formatting20 mins
- 1.7Knowing Your Bugs20 mins
- 1.8Translating Code to Other Languages20 mins
- 1.9Writing a Test Plan20 mins
- 1.10Working with Documentation Bots20 mins
- 1.11Knowing the Four Types of Maintenance20 mins
- 1.12Amazon CodeWhisperer20 mins
- 1.13Code.org’s AI Resources20 mins
- 1.14Helping with Syntax20 mins
- 1.15Demystifying Natural-Language Processing20 mins
- 1.16Exploring Tabnine20 mins
Instructor
Free
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