- Free taster
The first 12 lectures are free: Introduction and Module 1. Sign in to save progress; no card needed. The remaining 50 lectures and final assessment are in the paid course. Complete each lecture and try its quiz to unlock the next; any result counts.
Choose a module to see its lectures. The course follows CT-AI v1.0; certification and version details.
M-00
IntroductionFree
Welcome, what the ISTQB CT-AI certification is, and how the course is laid out.
3 lecturesShow lectures +Hide lectures −
- 01Welcome to the course!Free
- 02About ISTQB Tester AI certificationFree
- 03About the courseFree
M-01
Introduction to AI TestingFree
Foundational concepts: what AI is, the kinds of AI systems, the technologies and frameworks behind them, and the standards that now apply.
9 lecturesShow lectures +Hide lectures −
- 04Lecture 1: Definition of AI and AI EffectFree
- 05Lecture 2: Narrow, General and Super AIFree
- 06Lecture 3: AI-based and Conventional SystemsFree
- 07Lecture 4: AI TechnologiesFree
- 08Lecture 5: AI Development FrameworksFree
- 09Lecture 6: Hardware for AI-Based SystemsFree
- 10Lecture 7: AI as a Service (AIaaS)Free
- 11Lecture 8: Pre-Trained ModelsFree
- 12Lecture 9: Standards, Regulations, and AIFree
M-02
Quality Characteristics for AI-Based Systems
The quality attributes that are specific to AI: adaptability, autonomy, bias, ethics, transparency, and safety.
8 lecturesShow lectures +Hide lectures −
- 13Lecture 1: Flexibility and AdaptabilityQuiz
- 14Lecture 2: AutonomyQuiz
- 15Lesson 3: EvolutionQuiz
- 16Lecture 4: BiasQuiz
- 17Lecture 5: EthicsQuiz
- 18Lecture 6: Side Effects and Reward HackingQuiz
- 19Lecture 7: Transparency, Interpretability and ExplainabilityQuiz
- 20Lecture 8: Safety and AIQuiz
M-03
Machine Learning (ML) - Overview
How ML actually works under the hood: the forms of learning, the workflow, algorithm choice, and the overfitting trap.
5 lecturesShow lectures +Hide lectures −
- 21Lecture 1: Forms of MLQuiz
- 22Lecture 2: ML WorkflowQuiz
- 23Lecture 3: Selecting a Form of MLQuiz
- 24Lecture 4: Factors Involved in ML Algorithm SelectionQuiz
- 25Lecture 5: Overfitting and UnderfittingQuiz
M-04
ML - Data
Where most models really go wrong: data preparation, train/validation/test splits, quality issues, and labelling.
5 lecturesShow lectures +Hide lectures −
- 26Lecture 1: Data Preparation as Part of the ML WorkflowQuiz
- 27Lecture 2: Training, Validation and Test Datasets in the ML WorkflowQuiz
- 28Lecture 3: Dataset Quality IssuesQuiz
- 29Lecture 4: Data Quality and its Effect on the ML ModelQuiz
- 30Lecture 5: Data Labeling for Supervised LearningQuiz
M-05
ML Functional Performance Metrics
Reading a model honestly: the confusion matrix, the metrics for classification, regression and clustering, and where those metrics lie to you.
5 lecturesShow lectures +Hide lectures −
- 31Lecture 1: Confusion MatrixQuiz
- 32Lecture 2: Add ML Functional Performance Metrics for Classification, Regression and ClusteringQuiz
- 33Lecture 3: Limitations of ML Functional Performance MetricsQuiz
- 34Lecture 4: Selecting ML Functional Performance MetricsQuiz
- 35Lecture 5: Benchmark Suites for ML PerformanceQuiz
M-06
ML Neural Networks and Testing
Neural networks and the coverage measures used to test them.
2 lecturesShow lectures +Hide lectures −
- 36Lecture 1: Neural NetworksQuiz
- 37Lecture 2: Coverage Measures for Neural NetworksQuiz
M-07
Testing AI-Based Systems - Overview
Putting it into practice: specification, test levels, test data, automation bias, documenting a component, and concept drift.
7 lecturesShow lectures +Hide lectures −
- 38Lecture 1: Specification of AI-Based SystemsQuiz
- 39Lecture 2: Test Levels for AI-Based SystemsQuiz
- 40Lecture 3: Test Data for Testing AI-Based SystemsQuiz
- 41Lecture 4: Testing for Automation Bias in AI-Based SystemsQuiz
- 42Lecture 5: Documenting an AI ComponentQuiz
- 43Lecture 6: Testing for Concept DriftQuiz
- 44Lecture 7: Selecting a Test Approach for an ML SystemQuiz
M-08
Testing AI-Specific Quality Characteristics
The hard part: testing self-learning, autonomous, probabilistic systems, plus bias, explainability and the oracle problem.
8 lecturesShow lectures +Hide lectures −
- 45Lecture 1: Challenges Testing Self-Learning SystemsQuiz
- 46Lecture 2: Testing Autonomous Self-Learning SystemsQuiz
- 47Lecture 3: Testing for Algorithmic, Sample and Inappropriate BiasQuiz
- 48Lecture 4: Challenges Testing Probabilistic and Non-Deterministic AI-Based SystemsQuiz
- 49Lecture 5: Challenges Testing Complex AI-Based SystemsQuiz
- 50Lecture 6: Testing Transparency, Interpretability and Explainability of AI-Based SystemsQuiz
- 51Lecture 7: Test Oracles for AI-Based SystemsQuiz
- 52Lecture 8: Test Objectives and Acceptance CriteriaQuiz
M-09
Methods and Techniques for the Testing of AI-Based Systems
The toolkit: adversarial attacks and data poisoning, pairwise, back-to-back, A/B, metamorphic, and experience-based testing.
7 lecturesShow lectures +Hide lectures −
- 53Lecture 1: Adversarial Attacks and Data PoisoningQuiz
- 54Lecture 2: Pairwise TestingQuiz
- 55Lecture 3: Back-to-Back TestingQuiz
- 56Lecture 4: A/B TestingQuiz
- 57Lecture 5: Metamorphic Testing (MT)Quiz
- 58Lecture 6: Experience-Based Testing of AI-Based SystemsQuiz
- 59Lecture 7: Selecting Test Techniques for AI-Based SystemQuiz
M-10
Test Environments for AI-Based Systems
Setting up the test and virtual environments these systems need.
1 lectureShow lectures +Hide lectures −
- 60Lectures 1: Test and Virtual Environments for AI-Based SystemsQuiz
M-11
Using AI for Testing
Turning it around: the AI technologies that help you test, from defect analysis to interface automation.
2 lecturesShow lectures +Hide lectures −
- 61Lecture 1: AI Technologies for TestingQuiz
- 62Lectures 2: Using AI for Testing: From Defect Analysis to Interface AutomationQuiz
M-12
Final Assessment
A comprehensive mock exam covering all course material, run under real timed conditions.
40 questionsShow lectures +Hide lectures −
- 63Final Mock Exam - ISTQB CT-AI · 40 questions · 65% to passExam