Beijing—A recent academic investigation combining structural equation modeling (SEM) with fuzzy‑set qualitative comparative analysis (fsQCA) has quantified the impact of designer identity on Chinese design students' intention to use AI‑generated content (AIGC) tools. The research, conducted by a team at the Shanghai Academy of Fine Arts, surveyed 742 undergraduate design majors across six top‑tier universities, offering the first large‑scale, mixed‑method insight into how self‑concept shapes technology adoption in the creative curriculum.
The study at a glance
Researchers first built a classic SEM framework to test direct relationships among four constructs: designer identity, perceived usefulness of AIGC, creative self‑efficacy, and intention to adopt. The model achieved robust fit indices (CFI = 0.96, RMSEA = 0.04), confirming that a strong, professional designer identity positively predicts perceived usefulness, which in turn drives adoption intention.
Key variables explored
- Designer identity: measured by students’ alignment with professional values, aesthetic confidence, and perceived role in the design ecosystem.
- Perceived usefulness: the belief that AIGC tools can enhance speed, originality, or client communication.
- Creative self‑efficacy: confidence in generating novel concepts, with or without AI assistance.
- Adoption intention: self‑reported likelihood of integrating tools like Midjourney, DALL‑E, or Adobe Firefly into coursework.
To capture configuration‑level effects, the team applied fsQCA, revealing three distinct pathways that lead to high adoption intention. The most prevalent pathway combined a strong designer identity with high creative self‑efficacy, regardless of perceived usefulness. A secondary route showed that even students with moderate identity could adopt AIGC if they perceived strong usefulness and received peer endorsement.
Why designer identity matters for AI adoption
Designer identity is more than a résumé bullet; it is a lived narrative that informs how students evaluate new tools. In the Chinese context, where design education often balances traditional craftsmanship with rapid digitalization, a clear professional self‑concept can either reinforce or resist AI integration. Students who see themselves as “innovative problem‑solvers” are more likely to view AIGC as an extension of their creative toolkit, whereas those whose identity leans heavily on hand‑drawn mastery may treat AI as a threat to authenticity.
Moreover, the study highlights cultural nuances. The collectivist ethos prevalent in Chinese campuses amplifies peer influence—a factor that surfaced in the fsQCA configurations. When classmates champion AIGC, even identity‑ambivalent students report higher adoption intent, suggesting that identity interacts with social proof in shaping technology uptake.
Implications for design curricula
For educators, the findings translate into a concrete, actionable shift: embed identity‑building exercises alongside AIGC training. Rather than presenting AI tools as a separate module, curricula should weave them into projects that require students to articulate their design philosophy, critique AI‑generated drafts, and iterate with human‑centric decisions.
Takeaway: Design programs that couple reflective identity workshops with hands‑on AIGC labs see a 27 % increase in student adoption rates, according to the study’s predictive simulation. By foregrounding the designer’s self‑concept, schools can turn potential resistance into a catalyst for innovative practice.
