Shaping Human-Driven Validation in AI-Integrated Design Education
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Generative artificial intelligence (AI) is increasingly used in design education to support early-stage tasks such as programming, ideation, and environmental analyses. Yet its educational value depends on how students learn to question, verify, revise, and justify AI-supported outputs rather than uncritical adoption. The present study compares two pedagogical cases: a text-to-program workflow in a Council for Interior Design Accreditation (CIDA)-accredited interior architecture course and a performance-based mass study workflow in a National Architectural Accrediting Board (NAAB)-accredited architecture course. Using Linguistic Informatics and Geometric Synthesis (LIGS) as a cross-modal analytical framework, this study analyzes 1,081 structured reflection responses from 61 students, 9 AI-generated draft programs, 9 revised adjacency matrices, and environmental performance outputs from 20 design options. The findings show that AI became pedagogically meaningful when students treated generated material as provisional input for judgment. In Case A, students used a Keep, Revise, Reject pattern to evaluate AI-generated programmatic suggestions against user needs, adjacency logic, code-related concerns, and spatial feasibility. In Case B, students used environmental feedback from the simulations to compare massing alternatives and interpret the trade-offs among different criteria in design and environmental performance. These findings were understood as illustrative rather than representative. Across both cases, AI-supported learning depended on human-driven validation, trust calibration, metacognitive awareness, and decision ownership. The present study offers exploratory comparative evidence for a human-centered and a performance-based approach to AI-integrated design education.
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