Game Design Pillars are natural language artifacts commonly used in game development to communicate a project's core vision and ensure a coherent player experience. Their linguistic nature aligns well with the strengths of Large Language Models (LLMs), which excel at generating and interpreting natural language, making them strong candidates for supporting mixed-initiative workflows centered on design pillars. In this study, we introduce a formal definition of game design pillars, present an initial prototype -- SPINE -- and investigate the utility of LLMs in the creation and decision-making processes associated with pillar-driven workflows. We begin with a pre-study to identify an appropriate model, comparing \texttt{gemini-2.0-flash} and \texttt{GPT-4o-mini}. Results show that Gemini is better suited to our tasks due to its greater output variety and consistency. We then conduct a case study by deploying the tool at a local game jam. Findings indicate positive reception and clear value in integrating SPINE into early-stage development. Finally, we interview four experts, demonstrating the tool and allowing them to experiment with it in a controlled environment. While individual perspectives vary, the overall perception is encouraging and supports our intuition: LLMs can meaningfully contribute to game design pillar workflows. These early findings highlight the potential of formalizing pillar-driven design as a research space and point toward several promising avenues for future work.

翻译：游戏设计支柱是游戏开发中常用的自然语言制品，用于传达项目核心愿景并确保连贯的玩家体验。其语言特性与擅长生成和解析自然语言的大型语言模型（LLMs）的优势高度契合，使其成为支持以设计支柱为核心的混合主动式工作流程的强力候选者。本研究提出了游戏设计支柱的正式定义，开发了初始原型系统SPINE，并通过实验探究了LLMs在支柱驱动型工作流程的创建与决策过程中的实用价值。我们首先通过预研究对比了\texttt{gemini-2.0-flash}与\texttt{GPT-4o-mini}以选定合适的模型，结果表明Gemini因其更佳的输出多样性和一致性更适配本任务。随后在本地游戏开发节部署该工具开展案例研究，结果证实SPINE在早期开发阶段获得了积极评价与明确价值。最后通过专家访谈（四名参与者）在受控环境中展示并实测该工具。尽管专家个体观点存在差异，但整体反馈令人鼓舞，印证了我们的直觉：LLMs能够切实赋能游戏设计支柱工作流程。这些初步发现揭示了将支柱驱动型设计确立为研究领域的潜力，并为未来工作指明了若干重要方向。