Generative AI in Game Development: A Qualitative Research Synthesis

TL;DR

This paper delivers the first qualitative research synthesis (QRS) of GenAI’s impact on game production using meta-ethnography guided by PRISMA-S, eMERGe, and CASP. It identifies nine interrelated themes showing that GenAI primarily supports early-stage ideation and human-in-the-loop refinement rather than autonomous creation, with benefits highly contingent on pipeline fit, governance, and provenance. The synthesis reveals tensions around efficiency, authorship, labour precarity, and socio-technical positioning, and it provides concrete recommendations for practice, governance, and future research to responsibly navigate AI-assisted game development. By contextualizing findings within broader industry trends, the paper offers practitioners, researchers, and policymakers grounded insights to shape adoption, standardize practices, and govern GenAI’s role in creative pipelines.

Abstract

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.

Figures (3)

• Figure 1: PRISMA Flow Diagram
• Figure 2: Timeline with key GenAI release dates and data collection intervals for the ten included studies. Studies are colour-coded for demographic focus, and outlines distinguish the study context (green for production; purple for learning). Detailed data for alharthi_genaicreativity_2025 and begemann_gaidev_2024 were obtained via direct correspondence. Data for boucher_resistance_2024 and majgaard_pilot_2024 were supplemented with information from public online sources. For panchanadikar_solodev_2024, the interval reflects the date range of collected social media posts. It was not possible to determine the data collection period for lankes_ai-powered_2023 and shields_generatingtogether_2024 and these are marked in a circular shape.
• Figure 3: Exemplary translation cluster from the full synthesis map (Supplementary Materials) with the "ideation support" interpretation at the top. The hues signal that most interpretations stem from the Production group with only two contributing from the Learning.