Python Development: From Code to AI

A self-paced Python development course that starts with source code, execution, variables, decisions, loops, and functions, then grows into debugging, data structures, files, Git, modules, packages, program architecture, APIs, automated testing, AI-assisted development, coding-agent control, and a final code defense. Small examples can run inside the Robotnix reader; multi-file projects, Git, virtual environments, packages, network work, pytest, and the capstone use a real Python workspace. Learners progress by evidence, not by a calendar, and no AI tool is required to complete the course.

Audience: 9-12
Structure: 18 units
Delivery: Self-paced

Course overview

This self-paced Python course starts with runnable source code and grows through data, decisions, loops, functions, debugging, files, Git, modules, packages, architecture, APIs, testing, AI-assisted development, coding-agent control, and a final code defense. No AI tool is required to complete the course.

Audience and pace

Grades 9–12. Eighteen open-progress Units use internal route slots rather than calendar deadlines; move forward when code and evidence are complete.

What you will learn

Build Python programs that run, test, explain, and recover from failure.
Use files, Git, packages, APIs, and tests with explicit environment, privacy, and dependency boundaries.
Evaluate AI-assisted changes through requirements, diffs, verification, source review, and ownership evidence.

Course structure

Units progress from execution and state through control flow, functions, debugging, collections, persistence, Git, packages, architecture, APIs, testing, AI-assisted/specification-driven development, coding-agent control, and capstone defense.

Learning evidence and assessment

Learners submit code, runtime and test evidence, debugging records, Git history, architecture notes, and design defenses. Small examples run in the reader; larger projects use a real Python workspace with explicit setup evidence.

Expectations and boundaries

Do not expose secrets, run unreviewed code, claim generated code as understood, or modify repositories outside authorized scope. AI assistance requires review and disclosure where it materially contributed.

Capstone

Capstone & Code Defense requires a real Python application, requirements/tests/runtime evidence, architecture and Git history, documented AI assistance if any, and a defense of ownership and limitations.

Access and support

Use the browser reader for core examples and an approved local workspace for multi-file/project work. Alternative evidence paths are available when local setup is not immediately available.