AI · AIC-58

AI for QA and Software Testing

12 hours 2 days

Last updated 25 Sep 2026

AI for QA & Software Testing ยกระดับงานทดสอบซอฟต์แวร์ด้วย AI

AI for QA and Software Testing is a 12-hour training course by IT Genius Institute. Most QA time disappears into repetitive work: writing test cases one by one from incomplete documentation, preparing test data set by set, chasing scripts that…

Training schedule

No public rounds are open right now — register your interest and we will contact you when the next round opens, or request an in-house session for your team.

Corporate training quote

Most QA time disappears into repetitive work: writing test cases one by one from incomplete documentation, preparing test data set by set, chasing scripts that broke because a screen changed slightly, and then summarizing results for the team. When time runs short someone has to choose what gets tested and what gets waved through, and that is exactly where bugs slip into production. This course uses AI to cut that repetitive work away and gives the time back to deciding what actually needs testing and what carries the most risk. Learners apply AI across the whole discipline: analyzing requirements to surface risk,

designing test cases with standard techniques such as equivalence partitioning and boundary value analysis, generating thorough test data without using real customer records, running directed exploratory testing, testing APIs against the spec, writing and repairing automation scripts, selecting a risk-based regression set, analyzing failures and writing defect reports a developer can act on immediately, and reporting quality to management. A dedicated section covers where AI cannot be trusted and must always be checked. It closes with a capstone that plans and tests one system end to end. (2 days, 6 hours per day, 12 hours in total, Beginner to Intermediate level.)

Objectives

Apply AI across every stage of QA work and know which stages always need human review
Analyze requirements to surface risk and scope the testing where it matters
Design test cases with standard techniques and use AI to cover the cases usually missed
Generate thorough, safe test data without using real customer records
Run directed exploratory testing with charters and auditable session notes
Test APIs from the spec, covering both happy paths and error conditions
Use AI to write, repair and maintain automation scripts that do not turn brittle
Select a risk-based regression set when testing time is limited
Analyze failures, write clear defect reports and summarize quality for management

Who this course is for

QA engineers and software testers who want less repetition and better coverage
Manual testers who want to start automating without a programming background
Developers who test their own work before handing it to the team
QA leads and test managers who plan testing and report on quality
Business analysts and product owners who sign off before release

Prerequisites

An understanding of the software development process and some exposure to testing
Confident computer and browser skills; programming is not required
Being able to read basic code makes the automation sessions go more smoothly
Bring one system or website you look after to use throughout the workshops

Curriculum

Course Details

A 2-day course, 6 hours per day (12 hours in total, 09:00-16:00), delivered as lectures with workshops against a real system. Beginner to Intermediate level. The course focuses on testing thinking and process rather than a deep dive into any single tool, so it transfers to other frameworks. Anyone wanting a Playwright-specific deep dive should take the separate Playwright End-to-End Web Testing course. Learners take home a prompt template set covering every QA stage, templates for test plan, test case, defect report and quality report, plus sample scripts.

Day 1: Designing Tests with AI

Section 1: AI in QA Work and Where It Belongs

The QA work that consumes the most time and where AI genuinely helps
What AI cannot do for you, such as judging what is risky enough to test
An overview of the full path from planning to quality reporting
Confidentiality risk when customer data meets external AI tools
Preparing tools and accounts for both days

Section 2: Requirement Analysis and Test Planning

Reading requirements to find ambiguity and unstated conditions
Using AI to generate the questions to send back to the dev team before testing starts
Risk assessment to decide what gets tested first
What a concise, genuinely usable test plan contains
Lab: write a test plan from the requirements of your own system

Section 3: Test Case Design with Standard Techniques

Equivalence partitioning and boundary value analysis
Decision tables and state transition for complex conditions
Writing test cases with clear preconditions, steps and expected results
Using AI to extend the set into the cases people habitually miss
Removing redundant cases so the suite does not bloat
Lab: build a test case set for a real feature and check its coverage

Section 4: Preparing Test Data

Types of test data and the edge cases that must always be present
Using AI to generate realistic synthetic data without touching real customer records
Anonymization and the precautions required under the Personal Data Protection Act
Preparing data sets so repeated runs give identical results
Lab: build a test data set covering every case you designed

Section 5: Directed Exploratory Testing

The difference between scripted testing and exploratory testing
Writing a charter to set the scope and goal of each session
Using AI to form hypotheses about where the system is likely to break
Recording what you did and what you found so it can be audited later
Lab: run an exploratory session against a real system and summarize the findings

Section 6: API Testing

Reading an OpenAPI spec to derive what must be tested
Designing tests for happy paths, invalid data and unauthorized access
Verifying status codes, response structure and error messages
Using AI to build the API test set from the spec and checking it is complete
Lab: build and run an API test set in Postman from a real spec

Day 2: Automation, Analysis and Reporting

Section 7: Writing Automation Scripts with AI

Deciding which test cases are worth automating and which should stay manual
Turning test cases into scripts with AI and reviewing what it produced
Choosing selectors that survive interface changes
Structuring scripts so they can be maintained rather than thrown away
Lab: convert test cases into automation scripts and run them against a real system

Section 8: Keeping Scripts from Turning Brittle

Why test scripts break so often and how to prevent it while writing them
Handling waits and page state so results stay stable
Dealing with flaky tests and deciding whether to fix or delete them
Using AI to analyze why a script failed and propose a fix
Lab: repair a script broken by an interface change

Section 9: Risk-Based Regression Selection

Why running the full regression suite every time stops being viable
Analyzing which parts of the system a code change touches
Using AI to propose the test set to run from the change list
Tiering suites into smoke, critical path and full regression
Scheduling runs across the development cycle to fit the time available

Section 10: Failure Analysis and Defect Reports

Reading a failure and separating a real bug from a problem in the script itself
What a defect report needs so a developer can act without asking questions back
Using AI to write clean reproduction steps and assign severity
Grouping bugs that share a root cause to avoid duplicate reports
Lab: write defect reports from the real test results produced in class

Section 11: Quality Reporting and the Limits of AI

Quality metrics that mean something versus metrics that make teams game the numbers
Summarizing quality status for management without technical detail
Where AI typically fails in testing, such as test cases that look good but test nothing
Reviewing AI output before use and keeping accountability with people
Lab: produce a one-page quality report for the test cycle just completed

Section 12: Capstone and Real-World Adoption

Capstone: plan and test one feature set from requirement analysis through to the report
Presenting the reasoning behind your scope and test order
How to bring this process back to your team, starting where it pays off fastest
Workshop: present your work and review the approach with the instructor

Frequently asked questions

Who is AI for QA and Software Testing for, and what background is needed?

Built for QA engineers and software testers who want less repetition and better coverage · Manual testers who want to start automating without a programming background · Developers who test their own work before handing it to the team Background you should have: An understanding of the software development process and some exposure to testing · Confident computer and browser skills; programming is not required Not sure the fit is right? Talk to our team on LINE @itgenius or call 02-570-8449.

How much does AI for QA and Software Testing cost and how long does it run?

THB 8,900 (currently THB 8,010 on promotion). The course runs 12 hours. The fee covers course materials, lunch and refreshments throughout. Pay by bank transfer to the company account, confirm it on our payment page, and we can issue the receipt or tax invoice in your company's name.

Do I get a certificate?

Yes. Everyone who completes the course receives a Certificate of Completion from IT Genius Institute. Each certificate carries its own number, and anyone holding that number can verify it online on our certificate page, so you can add it to your portfolio or pass it to HR as evidence of training.

Where does the training take place, and is there an online option?

You can attend onsite at IT Genius Institute or arrange to join online, and we also run it as a private in-house session for your team. Ask about dates and venues on LINE @itgenius or call 02-570-8449.

What if I fall behind or miss a session — can I retake it?

Yes. You may retake the same course free of charge in a later round, under the institute's conditions. Tell our team which course and round you attended, and we will check it and offer you the rounds that still have seats. Ask us on LINE @itgenius or call 02-570-8449.

How do I enrol, or request a quotation for my company?

Enrol online with the registration form on this page. You can register several attendees at once and enter your tax ID and billing address for the tax invoice. Or request a company quotation straight from the quote button. For anything else call 02-570-8449 or reach us on LINE @itgenius.