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The aim is to explore how explainability mechanisms can enhance AI's role in conceptual design.

Explainable AI in Conceptual Design: An LLM-Powered Framework to Enhance Originality and Credibility

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The aim is to explore how explainability mechanisms can enhance AI's role in conceptual design.
Developed an explainable AI design framework incorporating divergence and convergence cycles.
Conducted a controlled user study involving 88 design engineering participants.
Analyzed data using linear mixed-effects models and controlled for Type I and II errors.
XAI significantly improved originality and credibility of design outputs.
No difference was detected in the usefulness and feasibility of the designs.
Evidence supports enhanced human-AI collaboration in conceptual design tasks.

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User study demonstrates improved originality and credibility of AI-assisted designs using explainable AI approaches.

Medina et al. (2026) studied this question.

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