End-to-End Procedural Level Generation in Educational Games with Natural Language Instruction

As the role of procedural content generation in mixed-initiative game design continues to grow, it is crucial to develop an end-to-end approach that enables non-technical designers to artfully guide content generation. Recent advances in large language models, such as GPT-4, are rapidly transforming the landscape of automated generation of text-based content. Large language models have significant potential for mixed-initiative procedural level generation by providing natural language interfaces for designers. This paper presents an end-to-end procedural level generation framework that interprets natural language descriptions of level design constraints and optimization objectives to facilitate the collaborative creation of game levels for a strategy game focused on environmental sustainability education. The framework enables designers to specify a problem domain, goal metrics, and target difficulty via natural language description. It then employs large language models for the semantic extraction of constraints and optimization targets to drive the generation of candidate levels. Generated game levels are evaluated via game-playing agents trained with deep reinforcement learning techniques to ensure the game levels meet the level designer’s specifications. Manual evaluation by authors shows that the proposed framework can effectively transform designers’ natural language descriptions into fully playable game levels that reflect their intended design objectives.