If your team is still manually translating requirements into test cases, you’re burning hours on work that AI can do in minutes — and leaving coverage gaps that come back to bite you.
This free course covers exactly how to close that gap: from understanding why responsible AI matters in testing, to seeing a live demo of AI turning natural-language requirements into ready-to-run test assets.
No fluff. No theory for theory’s sake. Just a practical playbook for testers who want to work smarter in 2026.
What you’ll be able to do after this course:
- Spot the hidden costs killing your manual test design process
- Apply the POET framework to keep AI usage responsible and audit-ready
- Use context-driven design and executable specifications to eliminate “happy path” blind spots
- See exactly how AI generates Gherkin and manual test cases from plain-language requirements
- Walk away with a concrete next step for your own team
Course Modules
Module 1 — Why Responsible AI in Testing Actually Matters AI is reshaping software delivery fast. But teams that skip the “responsible” part? They fail fast too. In this session, AI expert Noelle Russell shares her real-world experience helping organizations at Microsoft, AWS, IBM, and more navigate AI adoption — and introduces the POET framework (Precision, Optimize, Ethics, Trust). You’ll see how these four principles help testing teams build AI-powered workflows that scale without blowing up in production.
Key takeaway: A practical framework for scaling AI from enthusiasm to actual execution.
Module 2 — The Hidden Costs of Manual Test Design Most teams underestimate what manual test creation is really costing them: slow delivery, coverage gaps, and compliance exposure. Jonathan Wright — co-author of AI in Testing with Rex Black and committee member for ISO 29119 on testing AI-based systems — breaks down exactly where today’s “happy path” approaches fall short. You’ll learn why context-driven design and executable specifications are the fix.
Key takeaway: Know exactly where your current process is leaking time and coverage.
Module 3 — Live Demo: AI-Generated Test Cases From Requirements This is where it gets concrete. Product Manager Ian Sharp walks through a live demo showing how AI takes natural-language requirements and generates both Gherkin and manual test cases — in minutes, not weeks. You’ll see how it handles context, updates test suites automatically, and keeps sensitive data protected with on-prem deployment.
Key takeaway: A real look at what AI-powered test generation actually looks like in practice.
Module 4 — Putting It All Together: Your Next Step TestGuild founder Joe Colantonio ties the full picture together — responsible AI, the real cost of manual design, and what AI-generated testing means for your team right now. You’ll leave with specific, actionable next steps you can bring back to your organization immediately.
Key takeaway: A clear action plan, not just ideas.
This course is for you if:
- You’re a tester or QA engineer tired of manually translating requirements into test cases
- You’re exploring AI tools for testing but want a grounded, practical starting point
- You manage a QA team and need to make the case for AI-augmented test design
- You want to understand BDD/ATDD and executable specifications without the enterprise sales pitch