This study investigates how undergraduate students’ material perception of wood is structured through color expression and emotional language in AI-mediated spatial visualization. Thirty-two spatial images were generated by second-year interior architecture students using AI following a lecture on wood materiality. Participants selected target wood species and emotional keywords, then submitted descriptive statements documenting their material selection intent. RGB values extracted from wood-dominant regions were converted to CIELAB coordinates (L*, a*, b*) for quantitative analysis, and integrated with emotional vocabulary data through a mixed-method approach. Results indicate that wood color representations were concentrated in the mid-lightness range (L* 30–50, 65.6%), with all cases distributed in the positive chromatic quadrant, confirming consistent perception of wood as a warm-toned, yellow-dominant material. K-means cluster analysis identified three typological groups — high-chroma mid-lightness, mid-chroma mid-lightness, and low-chroma low-lightness — each systematically corresponding to distinct emotional vocabulary patterns. Cross-analysis confirmed that material perception formed through theoretical learning is consistently externalized via prompt verbalization and structured into identifiable color typologies through AI-mediated visualization. These findings suggest that generative AI functions as a mediating system that translates learners’ internalized material perception into visual color expression, providing empirical foundations for emotion-centered design education frameworks.
Ju young Kim (Sun,) studied this question.