Behavioral differentiation during the AIGC image generation waiting period: effects on creativity in conceptual
1Pan Tianshou College of Architecture, Art and Design, Ningbo University, Ningbo, China.
Introduction:
Artificial intelligence-generated content (AIGC) offers new ways to generate inspiration for conceptual design, yet limited attention has been paid to the waiting period between prompt submission and AI image output. This study examined behavioral differentiation during this interval and investigated how the task relevance and cognitive load of waiting-period behaviors affect creativity in conceptual design.
Methods:
Three experiments were conducted with design students. Experiment 1 identified and classified core waiting-period behaviors (N = 30); Experiment 2 measured the cognitive load of representative behaviors using the NASA-TLX (N = 24); and Experiment 3 employed a behavior-based 2 × 2 conceptual framework (N = 60), in which four representative waiting-period behaviors were selected to represent different combinations of task relevance (high/low) and relative workload (higher/lower), and their creative performance was compared.
Results:
The results showed that the AIGC generation waiting period was not a passive interval but involved multiple identifiable behavioral patterns. Creative performance differed across the four representative behavioral conditions. Specifically, the overall creativity score in the GP condition was significantly lower than the scores in the RS, DB, and RB conditions, whereas no significant differences were observed among the latter three conditions. These findings further suggest that the subsequent influence of waiting-period behaviors on creativity may depend not only on the level of cognitive demand but also on whether cognitive resources remain directed toward the ongoing creative task.
Discussion:
This study further interprets AIGC generation waiting-period behaviors from three interrelated functional perspectives: task continuity, cognitive expenditure, and temporary disengagement/incubation. This functional account provides an empirical basis for understanding the cognitive role of the AIGC generation waiting period and for optimizing AI-assisted conceptual design workflows.
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