Title: Guided Creativity: Generative AI as an Intermediary in Crowdsourced Design

Speaker:Xuekang Wu (Zhejiang University)

Time: 10:00(Wednesday),November 4th, 2026

Venue: Room712, Mingde Business Building (Zhongguancun Campus)

Language: Chinese & English

 

Abstract：

Creative platforms often help designers improve their work by showing them successful exemplars. Yet this practice can trigger creative fixation, where exposure to existing solutions constrains originality in subsequent work. To address this tension, we introduce AI intermediation, an automated information-sharing mechanism that leverages generative models to create controlled variations that mediate information flows from leading designs to subsequent designers. The approach operationalizes abstraction at scale, preserving quality-relevant core design elements -- visual motifs, color palettes, and structural composition -- while varying surface details that induce fixation. We empirically evaluate this approach in a field experiment with 292 professional designers in a live logo design contest, with a replication in a second industry. Designers in the AI intermediation condition produce (1) higher-quality work than those with no exposure to exemplars, and (2) more original work than those directly exposed to original exemplars. Unpacking the mechanism, we find that AI variations effectively transmit core design elements, providing a foundation for the quality of the final designs. Furthermore, rather than serving as templates for imitation, the variations enable designers to translate underlying design logic into novel forms: their submissions substantially outperform professionally refined versions of the same variations on both quality and originality. Consequently, AI intermediation reframes generative models from a tool for individual creative augmentation into an intermediary that enhances human-to-human social learning and fosters creative collaboration.

Short biography：

Xuekang is an Assistant Professor of Marketing at the School of Management, Zhejiang University. His research sits at the intersection of quantitative marketing, artificial intelligence, and digital platforms. He studies how emerging AI technologies are reshaping the ways information and ideas are created, communicated, and used, and how these shifts affect consumer decision-making, creative work, and platform outcomes. Using computational and experimental methods, his work examines human–AI interaction, social learning, and network effects in digitally mediated environments. He received his Ph.D. in Marketing from the Kellogg School of Management at Northwestern University and his bachelor’s degree from the University of Michigan.