On (Human) Learning in Design Space Exploration and the Impact of AI Assistants

Design space exploration (DSE) is a foundational activity in early‐phase systems engineering aimed at identifying promising designs through modeling, optimization, and analysis. With the rapid emergence of AI‐powered assistants, engineers can now delegate increasingly complex DSE tasks, from generating candidate designs to analyzing entire tradespaces. While this delegation may improve efficiency and solution quality, it raises an important question: what is lost when engineers offload cognitive effort to AI in DSE? This article argues that DSE is not only an optimization process but also an information discovery and learning activity through which engineers develop intuition, uncover trade‐offs, and build defensible rationales for design decisions. Drawing on some of our recent empirical studies, we examine how AI assistance affects both DSE performance and learning, highlighting a nuanced relationship between the two. The findings suggest that while AI can enhance DSE outcomes, excessive automation may hinder learning, particularly for less experienced engineers. We conclude with implications for designing AI assistants that balance performance gains with the need to preserve and enhance human understanding in engineering design.