Human-AI Collaboration for Brainstorming: Effect of the Presence of AI Ideas on Breadth of Exploration
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Date
2024-01-03
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421
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With the widespread adoption of generative large language models (GLMs) such as GPT-3 or ChatGPT for human-AI problem solving, understanding the effect on performance becomes important. Brainstorming is an established approach for generating ideas to solve problems. In this study, we investigate how AI ideas affect the brainstorming performance metric ‘flexibility’, which refers to the breadth of exploration or coverage of the topic. The foundation for our analysis is the data from an experiment (n=52) in which individual participants brainstormed in two conditions: (1) human-only (baseline) and (2) human+AI (treatment). The treatment condition had access to ideas generated via the GLM OpenAI GPT-3.5. Results show significantly higher flexibility for the human+AI as compared to the human-only condition with a large effect size. With our study, we contribute to the literature of electronic brainstorming, brainstorming with GLMs, as well as to the research challenge of human-AI collaboration.
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Collaboration with Intelligent Systems: Machines as Teammates, brainstorming, generative language models, gpt-3.5, human-ai collaboration, performance
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10 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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