Learn how to use ChatGPT, Claude, and modern AI tools to improve test design, test documentation, automation support, defect analysis, risk assessment, and QA productivity.
Learn through structured written lessons, practical exercises, templates, checklists, and portfolio-building tasks.
A practical course for software testers, QA analysts, automation testers, test leads, and career changers who want to use AI tools properly in real QA work.
Add AI to your everyday testing without losing your judgement.
Use AI to speed up scripting, debugging, and test data.
Analyse requirements, risks, and coverage faster.
Bring AI into planning, refinement, and release testing.
Build modern, in-demand QA skills from the ground up.
Support refinement, sprint planning, and retrospectives.
12 professionally designed modules taking you from AI fundamentals to a complete personal QA workflow.
Overview of how AI is changing testing, what it can do, what it cannot do, and why human judgement still matters.
Understand LLMs, prompts, context, hallucinations, limitations, and responsible AI usage.
Learn how to write clear, structured prompts for test analysis, planning, scenarios, risks, and documentation.
Use AI to review user stories, acceptance criteria, gaps, ambiguities, edge cases, and risks.
Generate, improve, prioritise, and review manual test cases using AI.
Create charters, personas, scenarios, mind maps, and risk-based exploratory testing ideas.
Use AI to improve bug reports, reproduce issues, analyse patterns, and support defect triage.
Use AI to understand endpoints, create test ideas, generate Postman examples, and review API risks.
Use AI to support Playwright, Cypress, Selenium-style thinking, test data, selectors, and debugging.
Use AI during refinement, sprint planning, regression planning, release testing, and retrospectives.
Understand data privacy, confidential information, bias, hallucinations, review controls, and professional responsibility.
Create a repeatable personal AI testing workflow and toolkit.
This course is assessed through practical QA tasks, not a traditional exam.
Short knowledge checks at the end of key modules.
Learners complete realistic QA activities using AI.
Learners build an AI-assisted QA pack for a sample product.
Awarded when the learner completes the course activities.
Six hands-on assignments that build real, portfolio-ready QA artefacts.
Review a user story and identify missing acceptance criteria, risks, and questions.
Create an AI-assisted test case pack.
Build exploratory testing charters using AI.
Improve a poor defect report using AI.
Create API testing ideas from an example endpoint.
Create an AI-assisted regression testing plan.
Create a complete AI QA Portfolio Pack including:
Complete the course activities and final portfolio project to earn your Inside STLC Academy Certificate of Completion.
No, the course is suitable for manual testers and beginners, although some automation examples are included.
No, learners can complete the course using free or commonly available AI tools.
Yes, but it is especially powerful for testers who already understand basic QA concepts.
Yes, learners receive a certificate of completion after completing the course activities.
No, the course uses practical assignments and a final portfolio project instead of a traditional exam.
Join Inside STLC Academy and build a professional, AI-assisted testing workflow.