AI-assisted design synthesis and human creativity in engineering education

The growing integration of AI into educational and professional settings raises urgent questions about how human creativity evolves when intelligent systems guide, constrain, or accelerate the design process. Generative AI offers structured suggestions and rapid access to ideas, but its role in adopting genuine innovation remains contested. This paper investigates the dynamics of human-AI collaboration in challenge-based design experiments, applying established creativity metrics: fluency, flexibility, originality, and elaboration in order to evaluate outcomes and implications in an engineering education context. Through an exploratory quasi-experimental study, a comparison of AI-assisted and human-only teams was conducted across four dimensions of creative performance: quantity, variety, uniqueness, and quality of design solutions. Findings point to a layered outcome: although AI accelerated idea generation, it also encouraged premature convergence, narrowed exploration, and compromised functional refinement. Human-only teams engaged in more iterative experimentation and produced designs of higher functional quality and greater ideational diversity. Participants' self-perceptions of creativity remained stable across both conditions, highlighting the risk of cognitive offloading, where reliance on AI may reduce genuine creative engagement while masking deficits through inflated confidence. Importantly, cognitive offloading is not directly measured in this study; rather, it is introduced here as a theoretically grounded interpretive explanation that helps contextualize the observed disconnect between performance outcomes and self-perceived creativity. These results bring opportunities and risks. On the one hand, AI can support ideation and broaden access to concepts; on the other, overreliance risks weakening iterative learning and the development of durable creative capacities. The ethical implications are significant, raising questions about accountability and educational integrity when outcomes emerge from human-AI co-creation. The study argues for process-aware and ethically grounded frameworks that balance augmentation with human agency, supporting exploration without eroding the foundations of creative problem-solving. The study consolidates empirical findings with conceptual analysis, advancing the discussion on when and how AI should guide the creative process and providing insights for the broader debate on the future of human-AI collaboration.