Generative artificial intelligence (GenAI) has transformed design education, yet growing evidence suggests that the fluency of AI-generated outputs may create a “fluency illusion”—a metacognitive bias whereby learners conflate polished AI artifacts with genuine cognitive mastery. A critical unresolved question is how to quantitatively diagnose this AI-induced fluency illusion without disrupting the natural learning process. This study introduces MBS-AIGC, a purpose-built AI-supported design education platform grounded in the Meaning–Behavior–Spirit (MBS) cultural cognition model for Chinese intangible cultural heritage. Drawing on the industrial soft-sensor paradigm, we computationally formalized six behavioral soft-sensor indicators from the digital interaction traces of 71 undergraduate design students over a four-week instructional period and applied K-means clustering to identify latent engagement patterns. Three distinct human–AI collaboration profiles emerged: Deep Explorers (n = 41), Progressive Builders (n = 16), and Surface Operators (n = 14). Crucially, expert-assessed cognitive flexibility significantly differentiated the three groups (F(2, 68) = 5.66, p = 0.005, η2 = 0.143), whereas a conventional self-report questionnaire failed to distinguish among them (F(2, 36) = 0.29, p = 0.748), providing preliminary empirical evidence for the fluency illusion in design education. By addressing the lack of objective diagnostic tools for metacognitive miscalibration, this research contributes a scalable, zero-intrusion behavioral soft-sensor framework that enables educators to decode human–AI collaboration patterns and mitigate the fluency illusion in creative learning environments.
Tang et al. (Mon,) studied this question.