Rapid AIdeation: Generating Ideas With the Self and in Collaboration With Large Language Models
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911803658731520 |
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| author | Lim, Gionnieve Perrault, Simon T. |
| author_facet | Lim, Gionnieve Perrault, Simon T. |
| contents | Generative artificial intelligence (GenAI) can rapidly produce large and diverse volumes of content. This lends to it a quality of creativity which can be empowering in the early stages of design. In seeking to understand how creative ways to address practical issues can be conceived between humans and GenAI, we conducted a rapid ideation workshop with 21 participants where they used a large language model (LLM) to brainstorm potential solutions and evaluate them. We found that the LLM produced a greater variety of ideas that were of high quality, though not necessarily of higher quality than human-generated ideas. Participants typically prompted in a straightforward manner with concise instructions. We also observed two collaborative dynamics with the LLM fulfilling a consulting role or an assisting role depending on the goals of the users. Notably, we observed an atypical anti-collaboration dynamic where participants used an antagonistic approach to prompt the LLM. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_12928 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rapid AIdeation: Generating Ideas With the Self and in Collaboration With Large Language Models Lim, Gionnieve Perrault, Simon T. Human-Computer Interaction Generative artificial intelligence (GenAI) can rapidly produce large and diverse volumes of content. This lends to it a quality of creativity which can be empowering in the early stages of design. In seeking to understand how creative ways to address practical issues can be conceived between humans and GenAI, we conducted a rapid ideation workshop with 21 participants where they used a large language model (LLM) to brainstorm potential solutions and evaluate them. We found that the LLM produced a greater variety of ideas that were of high quality, though not necessarily of higher quality than human-generated ideas. Participants typically prompted in a straightforward manner with concise instructions. We also observed two collaborative dynamics with the LLM fulfilling a consulting role or an assisting role depending on the goals of the users. Notably, we observed an atypical anti-collaboration dynamic where participants used an antagonistic approach to prompt the LLM. |
| title | Rapid AIdeation: Generating Ideas With the Self and in Collaboration With Large Language Models |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2403.12928 |