Deepening ideas vs. exploring new ones: AI strategy effects in human-AI creative collaboration

Abstract

As artificial intelligence (AI) increasingly participates in creative processes, designing effective human-AI collaboration is crucial. This study addresses a fundamental question: Should an AI partner prioritize deepening existing ideas (exploitation) or diversifying the creative space (exploration)? We investigated this through a controlled experiment with 148 participants, comparing two AI strategies based on March's exploration-exploitation framework. Using a turn-based brainstorming system on the topic of "how to increase café sales," we measured each AI strategy's impact on human trust and idea adoption. Contrary to traditional creativity research that emphasizes divergence, our findings show that the convergent deepening strategy significantly outperformed the diversification approach in both building user trust and encouraging idea adoption. We found that the AI's predictable behavior in the deepening condition was more easily understood by participants, leading to more effective collaboration. These results suggest that for effective human-AI co-creation, AI should act as a supportive, rather than competitive, partner that incrementally develops human-initiated concepts. This approach builds trust and increases the integration of AI's ideas. Our work thus contributes to the understanding of how AI can transition from tool-centric approaches to more collaborative partnerships that potentially enhance human creativity.