This course is designed to help software testers and QA professionals understand how to test AI-based systems effectively and confidently, in alignment with the ISTQB Artificial Intelligence Tester Certification syllabus. You will start by building a solid foundation in Artificial Intelligence and Machine Learning concepts, explained in a clear and tester-friendly way — without requiring any data science or programming background. The course then dives into the unique characteristics of AI systems, such as non-deterministic behavior, learning models, and data dependency, and how these characteristics impact testing activities.
Throughout the course, you will learn how to:
- Identify AI-specific risks and quality challenges
- Validate and test training, test, and operational data
- Detect and analyze bias, fairness, and ethical risks
- Design effective test strategies and test cases for AI systems
- Understand model behavior, outputs, and limitations
- Apply appropriate testing techniques across the AI lifecycle
The content is structured to support both practical understanding and exam preparation, with clear explanations, examples, and exam-oriented guidance that map directly to the ISTQB learning objectives. Whether you are preparing for the ISTQB AI Tester exam, working on AI-enabled projects, or simply want to future-proof your testing skills, this course will give you the knowledge and mindset required to test AI systems responsibly and effectively. By the end of this course, you will be able to approach AI testing with confidence, understand where traditional testing fits — and where new approaches are required.
