Large Language Models show promise for AI-assisted storytelling, yet current tools often generate predictable, unoriginal narratives. To address this limitation, we present NarrativeLoom, a multi-persona co-creative system grounded in Campbell's Blind Variation and Selective Retention theory. NarrativeLoom deploys specialized AI personas to generate diverse narrative options (blind variation), while users act as creative directors to select and refine them (selective retention). We designed a controlled study with 50 participants and found that stories co-authored with NarrativeLoom were not only perceived by users as more novel and diverse but were also objectively rated by experts as significantly better across all Torrance Test creativity dimensions: fluency, flexibility, originality, and elaboration. Stories are significantly longer with richer settings and more dialogue. Writing expertise emerged as a moderator: novices benefited more from structured scaffolding. This demonstrates the value of theory-informed co-creative systems and the importance of adapting them to varying user expertise.

翻译：大型语言模型在AI辅助叙事方面展现出潜力，但现有工具生成的叙事往往可预测且缺乏原创性。为应对这一局限，我们提出了叙事织机——一个基于坎贝尔“盲目变异与选择性保留”理论的多角色协同创作系统。该系统部署专业化的AI角色来生成多样化的叙事选项（盲目变异），同时用户作为创意总监进行选择和精炼（选择性保留）。我们设计了一项包含50名参与者的对照研究，发现与叙事织机共同创作的故事不仅被用户认为更具新颖性和多样性，而且专家根据托伦斯创造力测试的所有维度——流畅性、灵活性、独创性和精细性——均给出显著更高的客观评分。这些故事篇幅明显更长，场景更丰富，对话也更多。写作经验被证明是一个调节变量：新手从结构化支架中获益更大。这证明了基于理论的协同创作系统的价值，以及根据用户专业水平进行适配的重要性。