Description

AI is beginning to reshape how testing is planned, written, and maintained, and this course helps you build the skills to apply it in ways that actually make your work easier. Whether you are creating test cases, choosing what to run, or reviewing failures, you will learn how to use AI tools to speed things up, reduce repetitive effort, and improve coverage. You will work hands-on with user-friendly AI tools generate test data, build tests from user stories, and support smarter decisions about what to test and when. You will practice spotting flaky or redundant tests, creating self-healing flows, and using AI to explain what went wrong in a failing run. You will also explore how AI can predict risk based on commit history or past bugs, helping you focus on the areas that matter most. The course will show you how to plug AI into common testing workflows, including CI/CD tools like GitHub Actions, and how to write prompts that give you useful, accurate results. You will get examples, use cases, and guided labs that you can use right away in your own projects. This expert-led, one-day course is designed for software testers who are new to AI but already familiar with core testing practices. It is about 50 percent hands-on, with labs built around common tasks that testers perform every day. Whether you are working in a manual, automated, or hybrid role, this course will help you start using AI in ways that are practical, helpful, and easy to build on.