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Abstract Complexity is a core characteristic of concern for systems engineering practice. Verification, while a pervasive process in the engineering of systems, has been given relatively less research focus in terms of its complexity than system complexity has. We have proposed the Verification Complexity Framework as a formal definition of verification complexity to initiate a dialogue on distinguishing verification complexity from system complexity. The framework is designed to cover both static and dynamic complexity through the time axis and the hierarchical complexity layers, covering from external effects to the verification structures. This framework provides a common vocabulary for verification complexity, where both its definition and measurements can be discussed. In this paper, we showcase the application of VCF to a notional project. 

Abstract System verification is used to check that the system has been built in accordance with its requirements. In executing a verification strategy, each verification activity produces certain information artifacts that are then used as evidence in the assessment of the compliance of the system against its requirements. The process of reasoning through verification artifacts is cognitive and subjective, as the engineer combines their knowledge and expertise along with the information available in the evidence collected through verification activities. Through an experimental study with practitioners, this paper shows that engineers use some of this knowledge implicitly in their verification assessment and do not explicitly express it when formally articulating the justification to declare the compliance of a system. 

Abstract This paper presents an innovative educational approach used in a sophomore systems engineering course, where LEGO Mindstorms robots are integrated to accelerate the understanding of complex systems engineering concepts. While hands‐on learning tools like LEGO Mindstorms are often used in engineering education, our approach uniquely emphasizes the unpredictability and complexity inherent in real‐world systems engineering. Rather than focusing solely on technology or project completion, we incorporate controlled disruptions during exercises, such as modifying project requirements, changing team compositions, or removing key components from the kits. These disruptions simulate dynamic environments, requiring students to adapt, manage resource limitations, and navigate evolving constraints. This approach bridges the gap between theory and practice, allowing students to rapidly prototype, test, and observe the impacts of their engineering decisions in real time. This paper describes in detail the instructional approach and presents the different experiences that mimic real‐life projects that emerge during the learning activities.