Table 1_Reconfiguring graphic design education for generative AI in higher education: a human-agency and adaptive-curriculum framework.xlsx

This study examines that problem within graphic design education as a bounded empirical case.</p>Methods<p>A survey of 270 participants was analysed using exploratory factor analysis (N = 265 complete cases), and semi-structured interviews with 12 design academics and practitioners were analysed using a frozen rule-based codebook across 650 meaning units.

The quantitative and qualitative strands were analysed independently and integrated through a joint display.</p>Results<p>The quantitative analysis yielded a broad, sample-specific Transformative–Normative response orientation and a narrower Curricular Diagnostic Critique cluster; neither is interpreted as a validated latent construct. The final qualitative dataset contained 504 normative/transformative units (77.5%), 35 diagnostic units (5.4%), and 111 units retained as X0 (17.1%).