Deepening ideas vs. exploring new ones: AI strategy effects in human-AI creative collaboration
Kazuki Komura, Seiji Yamada, Md. Rabiul Awal, Katarzyna Piwowar-Sulej, Katarzyna Piwowar-Sulej, Katarzyna Piwowar-Sulej
TL;DR
This paper explores how AI can best collaborate with humans in creative tasks by comparing strategies that either deepen existing ideas or explore new ones.
Contribution
The study introduces a novel framework for AI-human collaboration, showing that deepening ideas builds more trust and encourages adoption.
Findings
The deepening strategy significantly improved user trust and idea adoption compared to diversification.
Predictable AI behavior in deepening ideas led to more effective collaboration.
AI should act as a supportive partner, not a competitor, to enhance human creativity.
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…
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Taxonomy
TopicsOpen Source Software Innovations · Creativity in Education and Neuroscience · AI in Service Interactions