Generative AI in Game Development: A Qualitative Research Synthesis
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arXiv
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| Format: | Preprint |
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2025
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| author | Ternar, Alexandru Denisova, Alena Cunha, João M. Kultima, Annakaisa Guckelsberger, Christian |
| author_facet | Ternar, Alexandru Denisova, Alena Cunha, João M. Kultima, Annakaisa Guckelsberger, Christian |
| contents | Generative Artificial Intelligence (GenAI) is currently reshaping game development practices, production pipelines, and value networks in an unprecedentedly pervasive manner with cascading consequences remaining unclear. In the last five years since GenAI's inception, a growing body of qualitative research has explored these early transformations from different settings and demographic angles. However, these studies often contextualise and consolidate their findings weakly with related work; for research to keep up with and support stakeholders in this development, the current moment calls for a synthesis of the findings emerged thus far. Here, we address this need through a qualitative research synthesis via meta-ethnography. We followed PRISMA-S to systematically search the relevant literature from 2020-2025, including major HCI and games research databases. We then synthesised the ten eligible studies, conducting reciprocal translation and line-of-argument synthesis guided by eMERGe, informed by CASP quality appraisal. We identified nine overarching themes, provide recommendations, and contextualise our insights in wider game production trajectories. With this work, we seek to provide practitioners, researchers and policy-makers with grounded insights to guide practice, research and governance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11898 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative AI in Game Development: A Qualitative Research Synthesis Ternar, Alexandru Denisova, Alena Cunha, João M. Kultima, Annakaisa Guckelsberger, Christian Human-Computer Interaction Generative Artificial Intelligence (GenAI) is currently reshaping game development practices, production pipelines, and value networks in an unprecedentedly pervasive manner with cascading consequences remaining unclear. In the last five years since GenAI's inception, a growing body of qualitative research has explored these early transformations from different settings and demographic angles. However, these studies often contextualise and consolidate their findings weakly with related work; for research to keep up with and support stakeholders in this development, the current moment calls for a synthesis of the findings emerged thus far. Here, we address this need through a qualitative research synthesis via meta-ethnography. We followed PRISMA-S to systematically search the relevant literature from 2020-2025, including major HCI and games research databases. We then synthesised the ten eligible studies, conducting reciprocal translation and line-of-argument synthesis guided by eMERGe, informed by CASP quality appraisal. We identified nine overarching themes, provide recommendations, and contextualise our insights in wider game production trajectories. With this work, we seek to provide practitioners, researchers and policy-makers with grounded insights to guide practice, research and governance. |
| title | Generative AI in Game Development: A Qualitative Research Synthesis |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2509.11898 |