Game Plot Design with an LLM-powered Assistant: An Empirical Study with Game Designers
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929576560558080 |
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| author | Alavi, Seyed Hossein Xu, Weijia Jojic, Nebojsa Kennett, Daniel Ng, Raymond T. Rao, Sudha Zhang, Haiyan Dolan, Bill Shwartz, Vered |
| author_facet | Alavi, Seyed Hossein Xu, Weijia Jojic, Nebojsa Kennett, Daniel Ng, Raymond T. Rao, Sudha Zhang, Haiyan Dolan, Bill Shwartz, Vered |
| contents | We introduce GamePlot, an LLM-powered assistant that supports game designers in crafting immersive narratives for turn-based games, and allows them to test these games through a collaborative game play and refine the plot throughout the process. Our user study with 14 game designers shows high levels of both satisfaction with the generated game plots and sense of ownership over the narratives, but also reconfirms that LLM are limited in their ability to generate complex and truly innovative content. We also show that diverse user populations have different expectations from AI assistants, and encourage researchers to study how tailoring assistants to diverse user groups could potentially lead to increased job satisfaction and greater creativity and innovation over time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02714 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Game Plot Design with an LLM-powered Assistant: An Empirical Study with Game Designers Alavi, Seyed Hossein Xu, Weijia Jojic, Nebojsa Kennett, Daniel Ng, Raymond T. Rao, Sudha Zhang, Haiyan Dolan, Bill Shwartz, Vered Computation and Language Artificial Intelligence Human-Computer Interaction We introduce GamePlot, an LLM-powered assistant that supports game designers in crafting immersive narratives for turn-based games, and allows them to test these games through a collaborative game play and refine the plot throughout the process. Our user study with 14 game designers shows high levels of both satisfaction with the generated game plots and sense of ownership over the narratives, but also reconfirms that LLM are limited in their ability to generate complex and truly innovative content. We also show that diverse user populations have different expectations from AI assistants, and encourage researchers to study how tailoring assistants to diverse user groups could potentially lead to increased job satisfaction and greater creativity and innovation over time. |
| title | Game Plot Design with an LLM-powered Assistant: An Empirical Study with Game Designers |
| topic | Computation and Language Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2411.02714 |