Objective:
Understand why AI is critical for future QA, master basic AI tools, and generate your first real test strategy.

Topics Covered:
Why AI-Enhanced QA is non-optional in modern softwareOverview: ChatGPT, Gemini, Grok, Copilot, CodeiumFundamentals of Prompt Engineering for QA tasksLive Demo: nop-qa.portnov.com walkthroughIn-Class Hands-On:
Create an AI workspace (ChatGPT + Gemini + Copilot accounts)Craft prompts for generating test strategies.Generate a functional and regression strategy for nopCommerce QA portal.Mini-Project Deliverable:
Submit 1 AI-generated test strategy document customized for https://nop-qa.portnov.com/.

Objective:
Learn how to "feed" AI the right way to generate powerful, usable artifacts.

Topics Covered:
How to prepare clear, AI-friendly test documentationWhat AI needs: clear requirements, acceptance criteria, flowsHow to structure documentation for maximum LLM output qualityIn-Class Hands-On:
Prepare basic business and functional requirement documentation for nopCommerce QA site.Generate a detailed Test Plan from the documentation using AI.Mini-Project Deliverable:
Submit structured requirements + the AI-generated test plan for nop-qa.portnov.com.

Objective:
Turn test documentation into real test cases, convert them into BDD, and manage them in Jira/Xray.

Topics Covered:
Generating context-aware test cases from requirements using AIBest practices for BDD format (Gherkin syntax)Exporting test cases to Xray-compatible formatLoading tests into Jira with XrayIn-Class Hands-On:
Generate positive, negative, and boundary case test cases.Convert test cases into BDD feature files.Export AI-generated cases in Xray CSV format.Load them into Jira using Xray import.Mini-Project Deliverable:
Jira Board with Test Plan and Test Cases for nop-qa.portnov.com fully loaded.

Objective:
Feed complete system documentation + HTML app archives into AI to identify coverage gaps and optimize.

Topics Covered:
Pull and archive nop-qa.portnov.com locally in HTML formatUpload entire web app + test documentation to AIHow to guide AI to "read" and cross-referenceAnalyzing and optimizing test coverageIn-Class Hands-On:
Feed project archive and documentation into ChatGPT/Gemini/Copilot.Ask AI to find missing test cases and suggest new ones.Optimize regression suite by converting cases into smart BDD format.Mini-Project Deliverable:
Regression Gap Report + optimized BDD test suite for nopCommerce QA.

Objective:
Use AI to generate and debug test automation scripts based on selected BDD scenarios.

Topics Covered:
Selecting the best candidates for early automationSetting up a basic WebDriver or Playwright base frameworkPrompting AI to generate test automation scriptsDebugging scripts using AI suggestionsIn-Class Hands-On:
Select 5 core BDD scenarios.Generate automation code using ChatGPT/Gemini/Copilot.Debug with AI help until successful execution.Mini-Project Deliverable:
Working end-to-end automation of 1-2 BDD scenarios.

Objective:
Scale automation efforts, debug tricky cases, and polish testing techniques.

Topics Covered:
Continue BDD to automation workflowsDebugging AI-generated flaky or partial scriptsBest practices for modular, clean test codeIn-Class Hands-On:
Automate 2 additional test cases.Debug and stabilize scripts with AI suggestions.Organize tests into feature-based folders.Mini-Project Deliverable:
A mini-regression automation suite (additional 2 test cases working end-to-end).

Objective:
Generate boundary cases with AI, convert tests into Scenario Outlines, and data-drive your automation.

Topics Covered:
How to create adversarial and boundary test dataScenario Outline best practices in BDDFeeding boundary data to tests automaticallyIn-Class Hands-On:
Generate data sets using ChatGPT/Gemini/CopilotUpdate BDD Feature files with Scenario OutlinesInject boundary cases dynamically during test runsMini-Project Deliverable:
1-2 Scenario Outlines covering critical areas like login, checkout, profile management.

Objective:
Start building your final full-stack AI-enhanced QA solution for nop-qa.portnov.com.

Topics Covered:
Scope definition for CapstoneTest Plan, Test Cases, BDD, Automation, Risk Reporting – all in one packageIn-Class Hands-On:
Create project backlog: Test Plan → Test Cases → BDD → Jira Integration → Automation → ReportingCapstone Deliverable (Begin Draft):
Clear project plan and first Test Strategy + Jira Test Plan loaded.

Objective:
Deliver a complete AI-enhanced QA solution end-to-end.

Topics Covered:
Best practices for managing QA projectsRapid debugging, polishing, final reportingIn-Class Hands-On:
Complete BDD-based automation suite.Generate intelligent risk and coverage reports via AI.Finalize Jira/Xray project dashboard.Capstone Deliverable (Final):
Working automation framework + Jira reports + test documentation.

Objective:
Prepare for real-world interviews and showcase your Capstone project.

Topics Covered:
Common AI + QA interview questions (and how to dominate them)Live mock interviews using AI feedbackHow to present your Capstone project like a proResume + LinkedIn optimization for AI-QA brandingIn-Class Hands-On:
AI-based mock interviews (using ChatGPT interview simulations)Portfolio polishing sessionLive demo of projects to peersGraduation Deliverable:
Publish your Capstone project on GitHub + LinkedIn.Practice interviews ready to land your next QA job.