Course Overview
The AI-Powered Software Development Course teaches students how modern coding assistants and agents can support software delivery without replacing developer judgement. Students learn to define requirements, describe features clearly, generate and review code, debug errors, refactor safely, test changes, and build applications with tools such as Cursor, Codex, Claude Code, and GitHub Copilot. The course develops both AI-assisted speed and the technical discipline needed to understand the resulting software.
- Use AI coding tools to plan, generate, debug, and improve software features
- Learn why human review, testing, and clear requirements are still essential
- Practice prompt-to-code workflows through small apps and reusable features
- Build a final AI-assisted project that demonstrates both tool use and judgment
Who This Course Is For
Coding Beginners
Students who want AI help while learning programming concepts and project thinking.
Web Students
Learners with basic HTML, CSS, or JavaScript who want faster project workflows.
Freelancers
People who want to prototype small tools, landing pages, and client features more efficiently.
AI Learners
Students who want a practical bridge between AI tools and software development.
Product Thinkers
Learners who want to turn ideas into working prototypes with structured AI assistance.
Career Changers
People exploring development who want modern tools but still need proper guidance.
What You Will Learn
- OKPrompt-to-code workflows for describing features, constraints, files, edge cases, and expected behavior clearly.
- OKAI-assisted planning for breaking an app idea into screens, components, data, forms, and user actions.
- OKCode review habits so students can inspect generated code, understand changes, and avoid careless copy-paste work.
- OKDebugging with AI by reading errors, asking better questions, testing fixes, and confirming behavior.
- OKRefactoring and improvement workflows for cleaner structure, reusable components, accessibility, and performance basics.
- OKVersion control and change discipline so students understand how to protect projects while iterating with AI.
- OKMini app development using AI support while learning the reasoning behind each feature.
- OKFinal project presentation that explains the problem, prompts, code decisions, testing, and improvements.
Tools and Technologies
- Cursor and AI-enabled code editors
- OpenAI Codex and Claude Code concepts
- GitHub Copilot workflows
- Git, debugging, testing, and deployment tools
Course Requirements
- Basic computer and file management skills
- A laptop suitable for development practice
- Basic coding knowledge is helpful but not required
- Commitment to review and understand AI-generated code
How You Will Learn
Specification First
Turn a clear problem and acceptance criteria into an organised development plan.
AI-Assisted Building
Use coding agents and assistants to implement, explain, test, and refine software features.
Quality Review
Check generated code for correctness, security, maintainability, and user experience.
Projects You Will Build
Prompt-Built Landing Page
A responsive page planned with requirements and improved through review prompts.
Interactive Web Tool
A small calculator, planner, or helper app with form logic and user feedback.
Bug Fix Challenge
A project where students diagnose errors, request fixes, test changes, and explain the solution.
AI Refactor Task
An exercise that improves messy code into cleaner components or functions.
Mini Dashboard
A simple dashboard with cards, lists, filters, and generated UI enhancements.
Final AI Coding Project
A complete small app built with AI assistance, tested, reviewed, and presented professionally.
Course Modules
Module 1: AI Coding Foundations+
- - Skills covered: tool roles, requirements, prompts, limitations, and review habits.
- - Mini project: generate and inspect a small UI feature.
- - Learning outcome: use AI as a development assistant, not a blind replacement.
Module 2: Planning Features with AI+
- - Skills covered: user flows, screens, components, acceptance criteria, and edge cases.
- - Mini project: plan a small web tool.
- - Learning outcome: write prompts that produce more useful code because the requirements are clear.
Module 3: Prompt-to-Code Workflows+
- - Skills covered: Cursor-style editing, Codex concepts, Copilot suggestions, file changes, and iteration.
- - Mini project: build an interactive component.
- - Learning outcome: move from idea to working feature with controlled AI support.
Module 4: Debugging and Code Review+
- - Skills covered: reading errors, isolating bugs, asking focused questions, testing fixes, and reviewing diffs.
- - Mini project: solve a bug fix challenge.
- - Learning outcome: debug more confidently without accepting unsafe suggestions.
Module 5: Refactoring and Quality Improvement+
- - Skills covered: reusable components, naming, cleanup, accessibility checks, and performance basics.
- - Mini project: refactor a messy feature.
- - Learning outcome: make AI-assisted code cleaner and easier to maintain.
Module 6: Final AI-Assisted App+
- - Skills covered: planning, prompt logs, code review, testing, deployment notes, and presentation.
- - Mini project: final AI coding project.
- - Learning outcome: show both AI tool skill and software development judgment.
