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Keep shoppingGenerative AI Coding Readiness Check
27 students
Generative AI Coding Readiness Check
Measure your readiness to effectively use generative AI tools in your software development workflows ( Java version )
Created byOlufisayo Omojokun, Nimisha Roy
Last updated 6/2026
English
What you'll learn
- Assess your readiness to responsibly integrate generative AI software development tools into coding workflows.
- Detect flaws, hidden bugs, and inefficiencies in AI-generated code.
- Evaluate your Java OOP, debugging, and algorithmic reasoning skills.
- Complete a capstone loop: tests-first → prompt engineering→ evaluate → fix.
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Coding Exercises
This course includes our updated coding exercises so you can practice your skills as you learn.
See a demo
Course content
7 sections • 20 lectures • 1h 9m total length
Requirements
- 1 or more years of experience with Java programming — you should be comfortable writing classes, methods, and using core OOP concepts like inheritance and encapsulation.
- Familiarity with basic data structures and algorithms, such as arrays, lists, maps, loops, and sorting.
- JUnit or similar testing experience — basic knowledge of writing and running unit tests.
- A computer with Java 17 installed and a simple IDE or text editor (e.g., IntelliJ, VS Code, or Eclipse).
Description
Are you truly ready to code with Generative AI — or are there hidden gaps in your fundamentals?
This course is a hands-on readiness self-check for Java programmers with 1 or more years of experience who want to use Generative AI responsibly, not blindly.
As AI copilots and “vibe coding” become more common, the real challenge isn’t writing less code — it’s knowing when AI output is wrong, inefficient, insecure, or misleading. This course helps you find out where you stand before you rely on AI in serious projects.
Rather than long lectures, this course uses structured diagnostics and micro-labs to expose your strengths, gaps, and blind spots.
What you’ll practice
Through short, focused exercises, you will work on:
Java fundamentals
Encapsulation, constructors, APIs, control flow, and correctness reasoningDebugging & logical reasoning
Catching subtle bugs, infinite loops, incorrect conditionals, and edge casesAlgorithmic thinking
Big-O tradeoffs, data structure selection, and performance awarenessAI critique labs
Identifying over-engineered solutions, fake fixes, hidden bugs, and unsafe patterns in AI-generated codePrompt engineering for developers
Writing structured prompts with roles, goals, constraints, and acceptance testsCapstone GenAI micro-project
A full AI-assisted workflow: tests-first → prompt design → AI generation → verification → reflection
What you’ll walk away with
By the end of the course, you’ll generate your own GenAI readiness verdict. That is, are you:
Ready Now — strong fundamentals and disciplined AI usage
Ready with Guidance — mostly solid, but needs practice with prompts and verification
Not Yet Ready — focus first on strengthening Java, logic, and testing skills
You’ll also leave with:
Model solutions and rubrics for every exercise
A reusable tests-first prompt template for GenAI coding
Who this course is (and isn’t) for
-Java developers who want to use GenAI professionally and safely
-Students preparing for internships, projects, or production code
-Engineers who want to know when not to trust AI output
Isn't for:
-Absolute beginners to Java
-Learners looking for “copy-paste AI shortcuts”
-A traditional lecture-heavy course
Course format
Self-paced, hands-on diagnostic workshop
Approximately 7–10 hours total
Emphasis on thinking, testing, and verification, not memorization
Make the leap from experimenting with AI copilots to using them responsibly, critically, and effectively
Who this course is for:
- Early-career Java programmers with about 1–2 years of experience who want to know if they’re ready to integrate Generative AI into their software development workflow.
- Students and recent graduates who have completed an introductory Java OOP course and want to test their skills before relying on AI coding assistants.
- Self-taught developers who’ve been coding in Java and want a structured way to identify skill gaps before diving into AI-driven software development.
- Teams or individual developers looking for a skills check-up to ensure strong fundamentals in debugging, algorithmic thinking, and prompt crafting before adopting generative AI tools.