Learning paths that show how I teach complex engineering ideas.
This section is presented as a course-design portfolio: each learning path demonstrates how I convert professional software engineering, AI testing, and quality assurance experience into structured lessons, assessment prompts, and student-friendly explanations.
Instructional signature
- Transforms complex AI/testing concepts into sequenced learning outcomes and memorable flashcards.
- Connects theory to authentic industry cases: probabilistic AI, CI/CD quality gates, risk-based testing, and governance.
- Models assessment-ready thinking with concise definitions, misconception checks, and certification-style prompts.
- Supports academic job applications by showing curriculum design, instructional clarity, and reflective teaching practice.
Course examples grounded in QA leadership, software architecture, and delivery constraints.
ISTQB AI Testing, GenAI testing, and test automation engineering learning paths.
Flashcards, scenarios, risk prompts, and decision checklists for active learning.
Designed to evidence curriculum design, assessment literacy, and student mentoring.
Learning paths and teaching artifacts
Each card highlights a teachable subject, suitable for workshops, undergraduate/graduate modules, professional certification preparation, or academic demonstrations.
Advanced Level Test Automation Engineering (CTAL-TAE)
Design, implement, verify, and continuously improve sustainable test automation solutions. This course follows the CTAL-TAE v2.0 syllabus and trains you to reason through architecture, tool selection, CI/CD integration, reporting, and long-term maintainability using exam-style scenarios.
AI Testing (CT-AI)
Learn how AI-based systems differ from conventional software, how ML works, and how to test AI-specific quality risks using practical methods and techniques aligned with the ISTQB CT-AI syllabus and sample exam rationales.
Testing with Generative AI (CT-GenAI)
Learn how to use Generative AI to support software test tasks safely and effectively. You will practice prompt engineering, manage GenAI risks (privacy, security, environmental impact), and understand LLM-powered test infrastructure (RAG, agents, fine-tuning, LLMOps) and organizational adoption (shadow AI, strategy, skills, governance) aligned with the CT-GenAI syllabus and sample exam rationales.