Generative AI in Game Development: A Qualitative Research Synthesis

Paper Title

Generative AI in Game Development: A Qualitative Research Synthesis

Publication Info

• Topic area: Adoption and impact of Generative AI in game development workflows.
• Keywords: Generative AI, game development, qualitative research synthesis, meta-ethnography, human-AI collaboration, ideation, production pipelines, authorship, governance, ethics.

Background and Problem

• Problem / challenge: Existing qualitative studies on GenAI in game development are fragmented, lack integration, and fail to provide a comprehensive picture of its adoption, use, and impact.
• Significance: Understanding GenAI’s role in game production is crucial for shaping sustainable practices, supporting creative workflows, and addressing ethical and labor concerns in a rapidly evolving industry.
• Motivation and related work: Prior reviews have focused on technical applications of AI in games, but none systematically synthesize qualitative insights on developers’ experiences with GenAI. This paper addresses the gap by consolidating findings from 10 studies conducted between 2020–2025.

Solution

• Proposed approach: A qualitative research synthesis (QRS) using meta-ethnography to interpret and integrate findings from existing studies on GenAI in game development.
• Novelty:
  1. First systematic synthesis of qualitative research on GenAI’s impact in game production.
  2. Identification of nine overarching themes, spanning refinement practices, ideation, efficiency, governance, authorship, and ethics.
  3. Recommendations for researchers, practitioners, and policymakers to guide future practice and governance.
• Procedure and key techniques:
  • Systematic literature search following PRISMA-S guidelines.
  • Meta-ethnography using reciprocal translation and line-of-argument synthesis.
  • Quality appraisal of primary studies using the CASP checklist.
  • Extraction and mapping of second-order interpretations to derive third-order conceptual insights.

Results

• Concrete findings:
  • GenAI is primarily valuable for ideation and prototyping, not autonomous authorship.
  • Human intervention is essential for refining outputs before production inclusion.
  • Efficiency gains are context-dependent, with benefits concentrated in early-stage workflows.
  • Integration challenges arise from pipeline misalignment and artefact constraints.
  • Ethical concerns include originality, authorship disputes, and labor precarity.
• Advantage over baselines:
  • Provides a comprehensive synthesis of qualitative insights, bridging gaps in fragmented studies.
  • Offers actionable recommendations for improving GenAI adoption and governance in game production.
• Experiments / evaluation:
  • Corpus of 10 studies analyzed, spanning diverse contexts (education, professional studios, indie developers).
  • Methodological rigor ensured through CASP appraisal and eMERGe reporting standards.
• Limitations and future work:
  • Limited sample size and uneven empirical coverage across asset types and production stages.
  • Lack of longitudinal studies to track adoption and retention over time.
  • Need for more detailed reporting on model configurations and contextual differences in game development practices.

Summary

This paper synthesizes qualitative research on the adoption and impact of Generative AI in game development, identifying nine key themes that characterize its role in ideation, refinement, efficiency, governance, and authorship. Findings highlight GenAI’s provisional and assistant-like role, with human intervention remaining central to its integration into production pipelines. Ethical and labor concerns, as well as pipeline constraints, shape its adoption and use. The synthesis provides actionable insights for researchers, practitioners, and policymakers, while identifying gaps in empirical coverage and methodological practices that future studies should address.