AI for QA / Testing

Overview

AI for QA / Testing focuses on using AI assistants and coding copilots to speed up and strengthen quality work. You will work through a…

What you will learn

  • Map AI to QA activities and prompt effectively for test assets (Module 1).
  • Detect requirement ambiguity and generate traceable, risk-prioritized test cases (Module 2).
  • Produce masked, edge-case-rich synthetic test data and environment documentation (Module 3).
  • Build and maintain automated UI tests with an AI coding assistant (Module 4).

Prerequisites

  • Required: Software testing fundamentals and basic scripting or automation exposure. Machine with an IDE and Node.js or Python. GitHub Copilot access [confirm whether provided].
  • Helpful: Familiarity with Playwright or Selenium and a CI tool. If you have no automation background, pair this with an automation foundation course.

Curriculum

  1. Module 1: AI in the Testing Lifecycle

    • Topics: Test planning to reporting; benefits and limitations; human judgement in quality; requirement-driven prompts; output formats for test assets; reviewing AI output critically.
    • Deliverable: Prompt toolkit. Completion check: Module 1 assessment.

    Hands-on:

    • Practice: QA Prompt Toolkit. Prompts for planning, case design, and bug reports.

  2. Module 2: Requirements Analysis and Test Design

    • Topics: Ambiguity detection; acceptance criteria generation; traceability; boundary, equivalence, and decision tables with AI; exploratory charters; risk-based prioritization.
    • Deliverable: Test suite. Completion check: Module 2 assessment.

    Hands-on:

    • Practice: Requirements to Test Suite. User stories to reviewed, traceable cases.

  3. Module 3: Test Data and Environments

    • Topics: Generating realistic datasets; edge cases and negative data; privacy and masking; setup documentation; mock services; data refresh strategies.
    • Deliverable: Data set and generator. Completion check: Module 3 assessment.

    Hands-on:

    • Practice: Synthetic Data Factory. Masked, edge-case-rich data for a sample app.

  4. Module 4: AI-Assisted Test Automation

    • Topics: Generating page objects and tests; refactoring and self-healing locators; reviewing generated code; Playwright and Selenium patterns; data-driven and BDD tests; maintenance.
    • Deliverable: Automated suite. Completion check: Module 4 assessment.

    Hands-on:

    • Practice: Automation Sprint. UI suite built with an AI coding assistant and reviewed.

  5. Module 5: API, Performance, and Security Testing with AI

    • Topics: Test generation from specifications; contract tests; negative scenarios; performance scripts and analysis; security test ideas; accessibility checks.
    • Deliverable: API and load pack. Completion check: Module 5 assessment.

    Hands-on:

    • Practice: API and Load Test Pack. Tests from a spec plus a performance script.

  6. Module 6: Defect Analysis, Logs, and Reporting

    • Topics: Bug report generation; duplicate and root-cause analysis; severity suggestions; log analysis; flaky test detection; quality dashboards.
    • Deliverable: Triage report. Completion check: Module 6 assessment.

    Hands-on:

    • Practice: Failure Triage. Root-cause summaries and a quality report.

  7. Module 7: Testing AI-Based Applications

    • Topics: Non-deterministic outputs; evaluation datasets and rubrics; prompt injection and safety tests; data quality and bias checks; model regression; production monitoring.
    • Deliverable: Chatbot test plan and results. Completion check: Module 7 assessment.

    Hands-on:

    • Practice: Chatbot Test Plan. Evaluation suite with safety cases.

  8. Module 8: AI Testing Tools, Strategy, and Governance

    • Topics: AI-native platform capabilities and evaluation criteria; CI/CD integration; build vs buy; AI use policy for QA; skills roadmap; measuring quality and speed gains.
    • Deliverable: Roadmap. Completion check: Module 8 assessment.
    • Capstone (independent work, [CAPSTONE_HOURS] hours): A complete AI-assisted test strategy for an application: requirements analysis, test suite, synthetic data, automated tests, API and performance tests, defect triage, and governance.

    Hands-on:

    • Practice: QA AI Adoption Roadmap. Two tools evaluated and a six-month roadmap.

Course fee

$699 $399

What's included

  • Live cohort + recordings: Attend every session live and revisit the recordings for one year.
  • Real lab environment: Practise on a hands-on environment that mirrors real projects.
  • Live doubt-clearing support: Ask questions and get answers in real time during every session.
  • Assessments and lab activities: Apply what you learn through graded assessments and guided labs.
  • Learning assets: Keep the slides, notes, and reference material for good.
  • Role-readiness guidance: Mentor guidance to get you ready for the role you are targeting.
  • Verifiable certificate: Earn a shareable certificate on completing the hands-on capstone.
  • Global peer community: Learn with a global cohort and stay connected after the program.

In short

AI for QA / Testing is a 24-hour instructor-led course covering Module 1: AI in the Testing Lifecycle, Module 2: Requirements Analysis and Test Design, Module 3: Test Data and Environments. It includes guided practical labs.

Key facts

  • Course: AI for QA / Testing
  • Duration: 24 hours
  • Modules: 8
  • Format: instructor-led live training

Frequently asked questions

Do I need automation experience?

Basic scripting or automation exposure is required. The automation labs use starter projects, but you should be comfortable reading code.

Which framework is used?

Playwright or Selenium for UI, open-source tools for API and performance. Patterns transfer to other frameworks.

Does this cover testing AI applications?

Yes. Module 7 covers evaluating non-deterministic LLM features, safety tests, and ML regression. This is unique to this course in the catalog.

Do I need GitHub Copilot?

Module 4 uses an AI coding assistant. [Confirm whether a Copilot license is provided or required.] An equivalent assistant can be substituted.

How is this different from GitHub Copilot for Developers?

That course covers development broadly. This course focuses on QA workflows from requirements to reporting, plus testing AI systems.

Are AI testing platforms taught hands-on?

They are demonstrated and evaluated in Module 8. Hands-on labs use open-source tools so every learner can follow without a paid platform.

What do I keep?

A prompt toolkit, test suite, data generator, automated suite, API and load pack, triage report, chatbot test plan, and roadmap.

Is a corporate version available?

Yes, with labs on your framework and application.

Topics covered

AI for QA / Testing, Artificial intelligence, Corporate training