How Generative AI Reshapes Game Development: Interpretation of a Research Review (2020-2025)

This article interprets a systematic literature review on the application of generative AI (GenAI) in game development from 2020 to 2025. It uses the meta-ethnography method, follows PRISMA-S and eMERGe guidelines, distills ten core themes, and discusses their profound impacts on developers, researchers, and policymakers.

Since 2020, GenAI technology has experienced explosive growth (including text, image, and 3D asset generation tools). Game development, which relies heavily on creative assets, has become an ideal application scenario. However, the rapid development of the field raises questions: How do developers use these tools? What challenges do they face? What are the experience differences between teams of different sizes (independent vs. large)? Insights need to be extracted through comprehensive qualitative research methods.

The meta-ethnography method is used to integrate multiple research results through "argumentative threads", focusing on "how" and "why"; the CASP tool is used to evaluate the quality of included studies to ensure reliable results; PRISMA-S and eMERGe guidelines are followed to screen literature, enhancing the rigor of the review.

1. Transformation of asset production pipelines: AI improves efficiency, and the role of artists shifts to curation and optimization;
2. New dimensions of creative exploration: Random AI outputs inspire design ideas;
3. Lowering of technical barriers and democratization: Non-professionals can participate, but originality is questionable;
4. Quality control requires manual review: AI outputs are not perfect and need professional adjustments;
5. Copyright and ethical dilemmas: No unified guidelines exist for issues such as training data and copyright ownership;
6. Technical integration challenges: Tool compatibility, standardization, and learning costs;
7. Player acceptance and transparency: Attitudes are complex, requiring transparency;
8. Game testing automation: Bug detection and balance testing;
9. Narrative evolution: LLMs enable dynamic dialogue and plotlines;
10. Lack of governance framework: Need for collaboration between policy, industry, and academia to establish a system.

• Developers: Treat AI as a collaborative partner, invest in AI literacy training, and establish usage policies and quality control processes;
• Researchers: Focus on blank areas such as GenAI's impact on developers' careers, cross-cultural differences, and players' psychological perceptions;
• Policymakers: Establish adaptive governance frameworks to balance innovation with ethical and legal risks.

GenAI is shifting from an efficiency tool to a paradigm, promoting the democratization of game creation and human-AI collaboration. However, technology needs to be combined with human creativity, ethics, and player needs, and humanistic values must be upheld in the future. This review provides an important reference for grasping industry trends and addressing challenges.