Nasser JAZDI
Germany
Generative Artificial Intelligence in Automation
New Paradigms for Quality, Testing, Reliability, and Safety—and the Future of Engineering Education
Automation technology is facing a profound transformation. While advances in quality, testing, reliability, and safety have been achieved over decades through more precise models, formal methods, and more powerful tools, generative artificial intelligence (GenAI) is the first technology that not only analyzes or optimizes, but also actively generates knowledge, forms hypotheses, and interacts with engineers. This qualitative leap fundamentally challenges established assumptions in automation research and teaching.
This plenary lecture takes a deliberately visionary perspective and highlights the role of GenAI in the central AQTR topics of quality, testing, reliability, and safety. It begins with a brief and intuitive classification of GenAI – not from the perspective of model architectures, but based on its new capabilities: cross-domain abstraction, synthesis of heterogeneous information, context-based reasoning, and interactive problem solving. These characteristics mark the transition from classic engineering tools to cognitive assistance systems.
The research section shows how GenAI does not replace established methods in testing, FMEA, safety analysis, and reliability assessment, but rather expands them. Using concise examples, it discusses how generative systems can derive test cases, generate error hypotheses, structure safety arguments, or support experts in analyzing complex error situations. At the same time, new scientific questions arise: How can generative results be validated? How do responsibility and liability change in safety-critical systems? And what quality criteria are required for AI-supported engineering processes?
The second focus of the presentation is on the consequences for engineering education in automation. When students use GenAI systems that generate code, models, analyses, and documentation, traditional teaching and examination formats lose their significance. The presentation therefore addresses key questions: What does competence mean in the age of GenAI? What should continue to be taught and examined—and what should no longer be? What skills do future automation engineers need to responsibly design quality, reliability, and safety in AI-supported systems?
The presentation concludes with the thesis that generative AI is not a short-term trend, but a paradigm shift for automation technology. Those who want to ensure quality, testing, reliability, and safety in the future must not only use GenAI, but also actively shape it scientifically – in research, teaching, and industrial practice.
Biography
Dr.-Ing. Nasser Jazdi is Academic Director and Deputy Director of the Institute of Industrial Automation and Software Engineering at the University of Stuttgart, Germany. He received his Dipl.-Ing. in Electrical Engineering in 1997 and his Dr.-Ing. in Electrical Engineering and Information Technology in 2003, both from the University of Stuttgart. Since 2003, he has played a key role in research, teaching, and academic leadership at IAS, and he currently also serves as a Visiting Professor at Anhui University in China.
His research focuses on generative AI and large language models for industrial automation, reliability and safety of automation systems, dynamic reliability assessment using machine learning, and AI‑enabled engineering systems. Dr. Jazdi has supervised more than 330 theses, coordinated numerous industrial and publicly funded research projects, and authored over 200 conference papers, 18 journal articles, and multiple book contributions. His work has also led to several patent applications in collaboration with industry.
Dr. Jazdi is an IEEE Senior Member, Vice Chair for Education of IFAC TC 3.3 Telematics, and serves on the VDI‑GPP Safety and Reliability Advisory Boards. He is a reviewer for the Alexander von Humboldt Foundation and has been a Visiting Researcher at the Lotfi Zadeh Lab at UC Berkeley.