Generative artificial intelligence (AI) is rapidly integrating into design education, yet its effects on student learning processes remain empirically underexplored. This study reports a sequential mixed-methods investigation across a built-environment faculty at a research-intensive university, drawing on 24 faculty interviews, a survey of 32 instructors with regression modelling, and five clustered discussions involving 31 participants. Faculty accounts converge on a pattern in which AI amplifies existing differences in student readiness rather than producing uniform improvements. Consistent with cognitive offloading theory, students with stronger foundations use AI to extend reasoning and accelerate iteration, while those with weaker foundations delegate formative cognitive work to AI, producing coherent outputs without corresponding understanding. This divergence is compounded by a loss of process visibility, as AI-generated outputs no longer reliably indicate how students arrived at their work, and by the erosion of frictional learning stages through which competence is built. Faculty have responded with adaptive strategies including process-oriented assessment redesign and deliberate reintroduction of cognitive constraint, but report no scalable solution. Exploratory quantitative analysis (n = 32) identifies perceived pedagogical relevance rather than seniority or career stage as the primary predictor within the model of faculty AI positivity. A significant negative association between theoretical course orientation and AI positivity suggests that uniform integration strategies risk producing uneven outcomes across course types. Effective AI integration in design education requires differentiated strategies responsive to course epistemology and student preparation level, assessed through process-visible methods rather than output quality alone.
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