With the rapid development of artificial intelligence technology, generative AI technology is gradually reshaping the paradigm of game development. It not only greatly improves the efficiency of game content generation, but also enhances the interactivity and personalized experience of games, which will be a unique new experience for developers and players. This paper focuses on the typical technical architecture and innovative applications of generative AI in the game field, and systematically analyzes the core principles, technical advantages and limitations of typical technologies such as GANs and Diffusion Models. It also derives innovative technologies in this field of generative AI in recent years, such as the PANGeA framework and the Text-to-Game engine. Through innovative technical research, this paper deeply explores the application efficiency of generative AI in scenarios such as character generation, dynamic narrative and code automation, and combines industrial practices such as Inworld AI Character Engine and Roblox's generative tool chain to reveal its potential in improving development efficiency and player immersion. This paper further points out that generative AI still faces challenges in terms of computing resource consumption, content controllability, ethical risks and cross-platform compatibility. In the future, with algorithm optimization and hardware iteration, generative AI is expected to drive the evolution of the gaming industry towards intelligence and personalization through adaptive content generation and multimodal interaction technology.
Shuting Shen (2025) studied this question.