Generative AI in Game Development: A Qualitative Research Synthesis

2025-09-15

AI Summary

Despite extensive research, existing studies lack a systematic integration and holistic understanding of generative artificial intelligence (GenAI)’s impact on game development. This study employs meta-ethnography, guided by the PRISMA-S framework, to systematically identify and synthesize qualitative literature published between 2020 and 2025. Rigorous quality appraisal was conducted using the CASP checklist, and evidence synthesis followed the eMERGe standards. For the first time, we propose a cross-contextual analytical framework for GenAI in game development, distilling nine core themes—spanning technical application patterns, role reconfiguration, evolving human–AI collaboration dynamics, and emergent ethical challenges. The findings address the fragmentation prevalent in prior work, offering a theoretically grounded yet practice-oriented integrative perspective. This framework advances both scholarly understanding and practical decision-making, providing a foundation for industry adoption and future empirical research design.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Philosophy and Ethics of AI: Artificial General Intelligence
• Game Theory and Economic Paradigms: Cooperative Game Theory

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Generative Artificial Intelligence (GenAI) has had a tremendous impact on game production and promises lasting transformations. In the last five years since GenAI's inception, several studies, typically via qualitative methods, have explored its impact on game production from different settings and demographic angles. However, these studies often contextualise and consolidate their findings weakly with related work, and a big picture view is still missing. Here, we aim to provide such a view of GenAI's impact on game production in the form of 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 10 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 trends.

Problem

• Synthesizing fragmented qualitative studies on GenAI
• Addressing lack of big picture view in game production research
• Systematically analyzing GenAI's impact through meta-ethnography methodology

Innovation

• Meta-ethnography synthesis of qualitative studies
• Systematic literature review using PRISMA-S
• Reciprocal translation and line-of-argument synthesis