Workflow first
Students learn how to plan tasks, define acceptance criteria, review diffs, and keep control of what gets shipped.
AI Coding Workflow
Learn to use Claude Code responsibly for planning, building, testing, debugging, and shipping real software projects.
This course teaches students and builders how to work with Claude Code as an AI coding partner while still owning the architecture, quality, security, and final decisions behind the code.
Course Overview
Claude Code is an agentic coding tool from Anthropic that can read a codebase, edit files, run commands, and integrate with development tools. The course turns that power into a disciplined workflow students can understand, review, and control.
Students learn how to plan tasks, define acceptance criteria, review diffs, and keep control of what gets shipped.
Sessions emphasize readable code, testing, debugging, refactoring, Git hygiene, and careful verification.
Students learn privacy, permissions, citation, academic honesty, and how to avoid blindly accepting generated code.
The course can culminate in a small web app, automation tool, API project, or polished coding portfolio feature.
These links are useful for official course, exam, credential, or platform details.
Student Fit
The starting point is adjusted to the student's age, coding background, school workload, and long-term goals.
Prerequisites
A bridge track is available when a student needs foundations before the main curriculum.
Curriculum
The curriculum moves from setup and prompting into project execution, testing, debugging, Git workflows, safety, and a capstone build.
Students install and configure the development environment needed for guided Claude Code work.
Students learn how to describe tasks clearly enough for useful implementation plans.
Students practice using AI to understand unfamiliar projects without losing their own understanding.
Students build small features through planning, incremental changes, and human review.
Students learn to ask Claude Code for tests, interpret failures, and verify fixes.
Students learn professional habits for reviewing diffs and preparing changes for sharing.
Students learn where AI coding tools can fail and how to protect privacy, security, and academic integrity.
Students complete a guided project with planning, implementation, testing, review, and presentation.
Practice
Practice is project-based. Students use Claude Code for real development tasks, then explain and verify every important change.
Outcomes
Learning Format
Why Code Scholars
Students get direct coaching, careful correction, and a course path that turns practice into visible progress.
Students learn how real developers are beginning to use agentic coding tools in practical workflows.
Every AI-assisted change is reviewed, tested, and explained so the student keeps ownership.
The course can produce a working project students can discuss with confidence.
Students learn permissions, privacy, secrets, and academic honesty before high-autonomy workflows.
Planning, testing, Git, and debugging stay central even when AI helps write code.
The course can start from basic setup or move quickly into advanced project workflows.
Schedule a consultation to discuss current level, goals, timeline, and the best starting point.