ISTQB Artificial Intelligence (AI) Tester

AI fundamentals and testing context

The course starts with an overview of what artificial intelligence and machine learning are, how they differ from conventional software, and what implications these differences have for quality assurance and testing.

Understanding AI behaviour and quality characteristics

You explore the characteristics that make AI systems difficult to test, such as bias, explainability, adaptability and non-deterministic behaviour, and you learn how these influence test strategy and design.

Machine learning concepts relevant to testing

The training introduces machine learning workflows, data preparation and quality, model selection, training, validation and performance metrics — all from a tester’s perspective to inform good test design.

Test design techniques for AI systems

You learn how to choose and apply test design techniques that are suitable for AI-based systems, including specialised approaches such as metamorphic testing and adversarial testing.

Test environments and tools

The course covers the setup and use of test environments for AI systems, including virtual test environments and how to use AI tools to support automated testing and defect analysis.

Challenges and risk management

You gain an understanding of common challenges when testing AI systems — such as concept drift, data bias, transparency issues and test oracles — and how to address these within a test plan.