彩票与冲刺街机：通过生成式人工智能实现玩家驱动的游戏编辑
Lottery and Sprint Arcade: Enabling Player-Driven Game Editing with Generative AI
AI总结：
研究通过语音驱动编辑系统让玩家修改复古太空侵略者风格游戏，结合用户研究探讨玩家体验及编辑模式与体验变化的关系，发现语音驱动编辑可支持人机共创，玩家体验积极，编辑有多种策略。
AI中文摘要：
大语言模型正在将游戏生成从离线自动化转向通过自然语言交互进行游戏驱动的修改。本文提出了一个游戏驱动的游戏编辑系统，玩家可以在玩复古太空侵略者风格的街机游戏时，通过基于语音的自然语言命令进行修改。语音指令由大语言模型解释并转换为内部配置参数的结构化更新，在不暴露底层系统细节的情况下实现迭代的游戏-编辑-反馈循环。游戏包含约100个可编辑配置字段，通过增量参数更改实现游戏玩法转变。为研究用户如何体验游戏驱动的人工智能介导编辑以及新兴编辑模式与玩家体验变化的关系，进行了用户研究。参与者能够修改游戏玩法，体验总体积极且工作量适中，交互结果不强烈依赖编程经验。编辑日志分析揭示了不同体验倾向，会话后反思还确定了多种编辑策略。这些发现表明语音驱动编辑能支持在结构化的侵略者风格街机游戏环境中进行可访问的、游戏驱动的人机共创。
英文摘要：
Large language models (LLMs) are shifting game generation from offline automation toward play-driven modification through natural language interaction. In this work, we present a play-driven game editing system that enables players to modify a retro Space Invaders - style arcade game through voice-based natural-language commands during play. Spoken instructions are interpreted by an LLM and translated into structured updates of internal configuration parameters, allowing iterative play - edit - feedback cycles in an invader-style game environment without exposing underlying system details. The game includes approximately 100 editable configuration fields controlling mechanics, visuals, interaction patterns, and audio behavior, enabling gameplay transformation through incremental parameter changes. To investigate how users experience play-driven AI-mediated editing (RQ1) and how emergent editing patterns relate to variations in player experience (RQ2), we conducted a user study combining subjective evaluations, workload measures, and log-based analysis of editing behavior. Participants were able to modify gameplay with generally positive experiences and moderate workload, and interaction outcomes did not strongly depend on prior programming experience. Editing-log analysis revealed distinct experiential tendencies: adjustments to immediately perceptible parameters were associated with higher usability, whereas edits affecting core gameplay structures were more closely associated with enjoyment. Post-session reflections further identified diverse editing strategies, including exploratory experimentation, goal-driven structural modification, and iterative parameter tuning. These findings demonstrate that voice-driven editing can support accessible, play-driven human - AI co-creation within a structured invader-style arcade game environment.
