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ISTQB® Certified Tester - AI Testing (CT-AI)
AI systems make decisions that were once reserved for humans. Testing them requires more than classic testing expertise – data, models, and dynamic behaviour create new demands for quality assurance.
Key topics
- Understanding the characteristics of AI systems in a testing context.
- Verifying and validating machine learning models.
- Managing test data and ensuring data integrity.
- Testing explainability, fairness, and bias in AI systems.
- Developing testing strategies for adaptive and self-learning systems.
- Implementing governance, compliance, and risk management in AI projects.
Prerequisite
Anyone with experience in software testing or quality assurance and a basic understanding of development processes can get started directly.
Target group
QA professionals, test analysts, IT project managers, development teams, and those responsible for AI-powered products and data analytics projects.
Structured testing procedures for AI create reliability, traceability, and trust in data-based applications – and help organisations take responsibility for their intelligent systems.
Course content
Fundamentals of artificial intelligence
- Getting to know AI terminology and the AI effect.
- Distinguishing between weak, strong, and superior AI.
- Comparing traditional systems with AI-supported approaches.
- Classifying the fundamental technological approaches to AI.
- Knowing the development platforms for AI applications.
- Understanding the technical infrastructure for AI systems.
- AI as a Service (AIaaS) at a glance.
- Using pre-built models in a targeted way.
- Understanding regulatory frameworks and AI standards.
Standards and ethical requirements for AI
- Assessing the adaptability and versatility of AI systems.
- Classifying levels of independence and autonomy.
- Testing progress and learning ability in context.
- Understanding bias and prejudice as a quality risk.
- Knowing the ethical principles in AI deployment.
- Identifying risks and manipulation incentives.
- Ensuring transparency, traceability, and interpretability.
- Considering data protection and security in AI systems.
Machine learning at a glance
- Understanding ML categories and their application areas.
- Following the typical ML process.
- Choosing the right learning approach.
- Knowing the decision criteria for algorithm selection.
- Recognising and avoiding overfitting and underfitting.
Data processes in machine learning
- Planning data processing in the ML process in a structured way.
- Dividing data sensibly into training, validation, and test sets.
- Understanding common data quality challenges.
- Understanding the impact of data quality on model performance.
- Labelling data for supervised learning.
Performance measures and metrics in ML
- Interpreting and applying the confusion matrix.
- Using advanced performance measures for classification, regression, and clustering.
- Knowing and classifying the limitations of performance measures.
- Selecting appropriate metrics for each task.
- Using reference sets for ML evaluation.
Neural networks in machine learning
- Understanding the structure and workings of artificial neural networks.
- Knowing and evaluating coverage metrics for network structures.
Fundamentals of AI software testing
- Understanding the requirements for testing AI systems.
- Classifying testing levels in the AI context.
- Building a structured data basis for AI tests.
- Recognising and testing bias in AI automation.
- Structuring evidence for AI modules.
- Identifying and testing for concept drift.
- Selecting the right ML testing strategy.
Quality assurance for AI applications
- Testing learning systems in a structured way.
- Verifying autonomous systems for correctness.
- Identifying distortions and random factors.
- Dealing with uncertainty in AI systems.
- Systematically testing complex AI architectures.
- Evaluating transparency and explainability.
- Using test oracles for AI-specific scenarios.
- Defining quality objectives and acceptance criteria.
Specific testing procedures for AI
- Accounting for adversarial attacks and data poisoning in tests.
- Applying pairwise comparison tests.
- Using confrontational testing procedures.
- Conducting A/B tests for AI variants.
- Using metamorphic testing methods.
- Developing practical test scenarios for AI systems.
- Systematically selecting the right testing procedure for AI.
Test infrastructure for AI systems
- Selecting and setting up suitable platforms for AI testing.
- Building and using digital simulation environments for intelligent systems.
AI as a support in the testing process
- Using intelligent systems in testing in a targeted way.
- Deploying AI to evaluate error reports.
- Generating test cases with AI support.
- Optimising regression tests using AI.
- Predicting error probabilities with AI methods.
- Testing graphical user interfaces with AI tools.
Up to 100% funding for your continuing education
Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.
Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.
What does AI testing mean in software quality assurance?
- AI testing refers to the systematic examination of AI-based systems and machine learning models. The focus is on data quality, model behaviour, bias detection, traceability, risk analysis, and specialised testing methods for learning systems.
What topics does the ISTQB® AI Testing training cover?
- Core topics include AI and ML fundamentals, testing strategies for AI systems, data validation, bias detection, model evaluation, suitable testing environments, and risks and quality assurance for AI-powered applications.
What should I bring to the AI testing training?
- Basic knowledge of software testing is recommended, ideally complemented by an ISTQB Foundation certificate. Background knowledge in data analysis, machine learning, or AI concepts makes it easier to get started with the testing methods.
Who benefits most from ISTQB® AI Testing?
- The training is aimed at software testers, test managers, QA engineers, data scientists, AI engineers, developers, and quality assurance managers in AI and data-driven projects.
Why is AI testing gaining importance in software development?
- AI systems make autonomous decisions based on training data. Testing procedures must therefore systematically evaluate data quality, model behaviour, fairness, robustness, and transparency to reduce risks and bias effectively.
How does testing AI systems differ from classic testing approaches?
- The focus is on data dependency, non-deterministic results, and learning models. Classic methods are extended with data validation, model evaluation, bias analysis, and continuous quality assurance.
What does an ISTQB® AI Testing certification offer me?
- The certification demonstrates in-depth expertise in AI testing strategies. Organisations benefit from structured quality assurance, while professionals prove their competence in a rapidly growing technology field.
In which business areas is AI testing applied?
- Typical areas of application include AI-powered decision-making processes, image recognition, speech processing, and predictive analytics. The goal is reliable quality, secure systems, and traceable results in data-driven applications.
