AiU® Certified Tester in AI (CTAI)

The course is providing a good introduction and overview of artificial intelligence methods used nowadays, starting from basic definitions to the different forms of AI model testing, online as well as offline. The particularities of risks, quality attributes and strategies for testing AI applications are outlined. In the last part it is demonstrated how AI is making testing tools smarter. Following this course will lead to a broad understanding of the topic.

After the course, successful participants will be able to:

  • Understand current trends, industry applications of Artificial Intelligence (AI) using Machine Learning (ML).
  • Compare different implemented ML algorithms to help choose the most suitable one.
  • Evaluate models for both supervised and unsupervised learning.
  • Design and execute test cases for AI systems.
  • Use various methods for bringing transparency into model workings.
  • Define a test strategy for testing of AI systems.
  • Understand where AI can be used in manual testing and in test automation.
  • Use AI based test execution tools to automate tests.


No public sessions are currently scheduled. We will be pleased to set up an on-site course or to schedule an extra public session (in case of a sufficient number of candidates). Interested? Please let us know.

Intended for

  • Testing professionals wishing to widen their testing scope towards testing of Artificial intelligence applications.
  • Testing professionals wishing to acquire more in-depth knowledge of Artificial intelligence in test tools.
  • Other stakeholders who wish to have a deeper understanding of Artificial intelligence in general and testing in particular.
  • Everyone preparing for the "AiU Certified Tester in AI" examination.


It is recommended that candidates hold the ISTQB® CTFL certificate. See ISTQB Certified Tester - Foundation Level (CTFL).

They should also have basic knowledge of a programming language (Java or Python or R). See Java programming, Python fundamentals or R for data analytics.

Having basic knowledge of statistics is an asset. See Statistics fundamentals.

It is also recommended to have some software development or testing experience.

Main topics

The course is structured according to the AiU Certified Tester in AI syllabus (see This way you can relate the topics covered in the course to the syllabus.

  • Introduction to Artificial Intelligence: introducing artificial intelligence (AI), Machine Learning (ML) and Deep Learning (DL).
  • Overview of testing AI systems: off-line and online testing of AI applications, data preparation and pre-processing (outlier detection, dimension reduction), imputation and visualization
  • Metrics for supervised (Accuracy, Precision, Recall/sensitivity, Specificity and F1-score) and unsupervised learning (Inertia and Rand score, Support, Confidence and Lift metrics) to find the best AI model
  • Explainable AI: examination and evaluation of complex (DL models) models by varying input variables and observing variations in outcomes while constructing a simple interpretable model
  • Risks and test strategy for AI systems
  • AI in testing: application of AI in the test process itself, smart dashboards and test automation tools

Training method

Instructor-led classroom training. The exam is done at the end of the third course day (60 minutes).


3 days.

Course leader (accredited ISTQB training provider).