The design of complex systems has become an increasingly important challenge in engineering, industry, and technological innovation. As systems become more interconnected and dynamic, designers must address conflicting requirements, uncertainty, and the interactions between technical, human, and environmental factors. In this context, TRIZ provides a valuable framework for understanding design contradictions and developing innovative solutions through structured reasoning and the analysis of technological evolution. At the same time, Artificial Intelligence (AI) is opening new perspectives for supporting creativity, exploring alternative solutions, and making better use of engineering knowledge. Bringing TRIZ and AI together offers an opportunity to rethink how complex systems are designed, moving beyond conventional trial-and-error approaches toward more systematic, creative, and informed decision-making.

This Special Issue aims to explore how the combination of TRIZ and Artificial Intelligence can contribute to the design, analysis, and improvement of complex systems. It welcomes original research addressing AI-assisted TRIZ, intelligent identification and resolution of technical and physical contradictions, functional modeling, inventive design methods, system architecture development, and the optimization of complex engineering solutions.

Contributions may also examine the role of generative AI, machine learning, knowledge-based systems, digital twins, and multi-agent approaches in supporting design activities throughout the system lifecycle. Particular attention will be given to research that connects methodological advances with practical engineering challenges, demonstrating how these combined approaches can improve design quality, reduce unnecessary complexity, and support the development of reliable, sustainable, and adaptable systems. Both theoretical contributions and industrial case studies are encouraged to strengthen the links between scientific research and real-world applications.