Keeping Text-to-Image Generation Aligned with Requirements: Need-Priority–Driven Co-Creation

Abstract

Text-to-image generation supports rapid concept exploration in design sprints, but improvisational prompts often cause two failures: priority dilution (critical improvement foci are buried) and weak input structure (mixed information in one sentence), which reduces stability and traceability. This study proposes a needs-priority–driven prompt framework that links decision documentation to generative ideation. Universal-Design-derived need items (C1–C6) are weighted using AHP with consistency checking (CR ≤ 0.10). QFD, integrated with AHP weights, translates weighted needs (WHATs) into engineering characteristics (HOWs/ECs) and yields a ranked Top-6 EC set. The ranked ECs are rewritten into a fixed-field structured prompt to guide Midjourney exploration and improve auditability from priorities to visible design cues. The contribution is a transferable “ranked ECs to structured prompt” specification for requirement-aligned generative exploration under short-cycle constraints.

Keywords: Generative AI, Text-to-image, Structured Prompting, Need Prioritization, Universal Design

DOI: 10.54941/ahfe1008017