Human–AI Creative Collaboration as a Socio-Technical System: Perceptions of AI Tool Features, Perceived Control, Perceived Collaboration, and Designers’ Creative Self-Efficacy

As AIGC becomes increasingly embedded in design practice, the relationship between designers and AI is shifting from tool use toward sustained creative collaboration. Drawing on socio-technical systems theory, affordance theory, and self-efficacy theory, this study examines how five perceived AI tool features—Customizability, Self-Learning Capability, Explainability, Intelligent Feedback, and Emotional Support—are associated with designers’ Perceived Control, Perceived Collaboration, and Creative Self-Efficacy. Survey data from 461 design professionals with experience in AI-assisted design were analyzed using covariance-based structural equation modeling (CB-SEM) to test structural associations and indirect effects, and fuzzy-set qualitative comparative analysis (fsQCA) to identify configurations associated with high Creative Self-Efficacy. Perceived Control and Perceived Collaboration were both positively associated with Creative Self-Efficacy, but the five AI tool features exhibited distinct patterns. Customizability was associated primarily with Perceived Control, Intelligent Feedback primarily with Perceived Collaboration, and Self-Learning Capability, Explainability, and Emotional Support with both evaluations. The fsQCA results further showed that high Creative Self-Efficacy corresponded to multiple configurations rather than a single unique configuration and that Explainability was a core condition across multiple high-level configurations. These findings indicate differentiated relationships between AI tool features and designers’ control and collaborative experiences; these experiences were in turn associated with Creative Self-Efficacy. By distinguishing the functional roles of AI tool features, two related but distinct psychological evaluations, and multiple configurations, the study advances understanding of human–AI creative collaboration and complements existing human–AI team research from an individual-level perspective.