Refining Research Questions for AI-Assisted Knowledge Retrieval in Interior Design: An Exploratory Study of Expert Judgment

Abstract

AI-assisted knowledge retrieval is increasingly used to support interior designers during early-stage exploration; however, current “single-shot” and keyword-driven paradigms fail to align with the iterative, interpretive, and responsibility-laden nature of expert design reasoning, requiring designers to articulate ill-defined needs precisely and offering limited support for multi-criteria comparison and accountable decision-making. This exploratory qualitative study investigates how senior interior designers structure judgment across three phases of material selection—searching, comparing, and deciding—and where AI-assisted tools support or fail to support expert reasoning. Semi-structured interviews were conducted with three senior professionals who each have 15–20 years of experience. Verbatim transcripts were analyzed using inductive thematic coding using open, axial, and thematic clustering, yielding ten codes organized into three higher-level themes: comparison-stage difficulties, expert judgment logic, and perceived roles of AI. Findings show that experts value AI for accelerating visual exploration and broadening references, but experience breakdowns during comparison due to nomenclature confusion, persistent gaps between online information and local supply-chain realities, and the lack of structured failure-based evidence. Expert judgment is anchored in constructability reasoning, tactile and physical verification, psychological matching with client intent, and reliance on trusted human networks. Based on these structures, we derive interface design implications that emphasize uncertainty awareness through time-stamped availability and risk flags, evaluation transparency through inspectable criteria and trade-offs, responsibility boundary clarification, and professional agency preservation through adjustable comparison frameworks. This human-centered framing positions AI-assisted knowledge retrieval as a collaborative decision-support system that augments—rather than replaces—expert judgment in high-stakes material decision-making.

Keywords: AI-assisted Knowledge Retrieval, Interior Design, Material Comparison, Expert Judgment, Human–AI Collaboration, Uncertainty Awareness, Exploratory Qualitative Research

DOI: 10.54941/ahfe1007504