Testing Using AI Micro-Credential

AT*SQA Software Testing Micro-Credential

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Testing Using AI Micro-Credential

This micro-credential shows you can use AI as a practical tool in your testing work. That covers test case generation, test data creation, self-healing automation, defect prediction and analysis, and the prompt engineering skills that make all of it work. Passing adds you to the Official U.S. List of Certified Testers and counts toward the AT*SQA AI for Testers certification.

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Learn Testing Using AI through AT*Learn Training

AT*SQA Testing Using AI Body of Knowledge (Syllabus)

Register for the Testing Using AI Micro-Credential Exam

What the Testing Using AI Micro-Credential Covers

This credential is about applying AI tools to make testing faster, more thorough, and less dependent on manual effort. The syllabus is organized around the main areas where AI adds practical value in a QA workflow.

Test case generation and management covers how to use prompt engineering to generate test cases from requirements, user stories, or business processes in any format. It also covers how AI-driven tools maintain traceability, flag coverage gaps, detect when test cases fall out of sync with updated requirements, and how testers can align test cases with code changes while keeping human review in the loop.

Test data generation and management covers the types of data AI tools can produce: data derived from requirements, data built from existing datasets, data extracted from production with appropriate masking and privacy controls, boundary and equivalence data, and manipulated data for specific test conditions. The syllabus addresses data privacy risks that come with using production data alongside an LLM.

Test automation and execution covers self-healing test scripts, which detect UI changes and adapt automation code automatically to reduce maintenance overhead in fast-moving development environments. It also covers visual and UI testing using computer vision, WCAG accessibility checks, and predictive testing that uses code complexity, defect history, and developer patterns to target automation where failures are most likely.

Defect prediction and analysis covers how AI tools mine historical data to predict where defects will occur, how AI-powered root cause analysis works, and how intelligent defect triage can reduce triage time and improve severity classification accuracy. Defect reporting with AI-driven trend analysis and predictive summaries is also covered.

The syllabus finishes with tool selection criteria, mandatory features in AI-augmented testing tools based on Gartner guidance, and an honest discussion of challenges, risks, and ethics: hallucinations in AI-generated outputs, non-deterministic results, model drift, automation bias in human reviewers, security issues like data poisoning, and ethical and IP considerations when using AI tools on proprietary code and data.

What You Will Learn in the Testing Using AI Micro-Credential

• How the role of the tester changes when AI tools are handling generation, execution, and analysis
• How to use prompt engineering to generate test cases, test data, and exploratory test ideas
• The pros and cons of aligning test cases with code versus requirements
• Types of AI-assisted test data generation: requirements-based, existing data, production data, and specific boundary or manipulated data
• How self-healing test scripts work and when human review of auto-healed changes is required
• Visual and UI testing with AI, including cross-browser comparison and WCAG compliance checks
• How predictive testing uses code complexity, defect history, and developer patterns to target testing effort
• How AI supports root cause analysis, intelligent defect triage, and defect trend reporting
• Mandatory features to evaluate when selecting an AI-augmented testing tool
• Technical and reliability risks in AI-based testing tools: hallucinations, non-deterministic outputs, model drift, and automation bias
• Security and ethical considerations when using AI tools with proprietary code and production data

Who Should Earn the Testing Using AI Micro-Credential

Software testers and QA professionals who want to use AI tools effectively in their daily testing work, including those responsible for test automation, test data management, or defect analysis. No prior experience with AI testing tools is required, though familiarity with AI basics from the AI Introduction for Testers micro-credential is helpful.

How to Earn This Credential

Download the free syllabus to study, then register for the exam on the purchase page for $39. If you want structured video instruction and exam prep materials, the AT*Learn Testing Using AI training is available for $49.

Passing the exam adds you to the Official U.S. List of Certified and Credentialed Software Testers, which employers use to verify credentials. This credential is the fourth of four micro-credentials in the AT*SQA AI for Testers certification. The others are AI Introduction for Testers, What to Test in AI-Based Systems, and How to Test AI-Based Systems. AT*SQA also offers the ISTQB AI Testing and ISTQB Testing with Generative AI certifications for testers who want a broader AI testing certification.

Testing Using AI: Common Questions

Do I need to know how to code to earn this credential?
No. The syllabus focuses on how to use AI tools for testing tasks including prompt engineering, reviewing AI-generated outputs, and understanding tool capabilities. It does not require writing automation code, though some sections cover how AI can generate automation scripts that testers then review and validate.

What is prompt engineering and why does it matter for testers?
Prompt engineering is the practice of writing specific, well-structured instructions to get useful output from an AI tool. For testers, it is the key skill for generating accurate test cases, useful test data, and targeted exploratory test ideas. The syllabus includes a prompt engineering cheat sheet with worked examples for common testing tasks.

How is this different from the How to Test AI-Based Systems micro-credential?
How to Test AI-Based Systems focuses on testing a system that has AI in it. Testing Using AI focuses on using AI as a tool to do the work of testing, regardless of what kind of system is being tested. The two credentials cover different ground and both count toward the AT*SQA AI for Testers certification.

Does AT*SQA offer other AI testing credentials?
Yes. This is one of four micro-credentials in the AT*SQA AI for Testers certification, which also covers AI fundamentals, what to test in AI-based systems, and how to test AI-based systems. AT*SQA also offers the ISTQB AI Testing and ISTQB Testing with Generative AI certifications.