NarrativeLoom: Enhancing Creative Storytelling through Multi-Persona Collaborative Improvisation

Abstract

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.

Figures (9)

• Figure 1: The user workflow of NarrativeLoom. The process consists of three integrated phases: (i) Discovery and Ideation, where users initialize the narrative by entering "sparkles" and selecting parameters such as language and story length; (ii) Collaborative Story Creation, where the system generates diverse beat options using 10 distinct personas, allowing users to tailor, select, and expand beats into full narrative, and edit the text before iterating to the next story beat; and (iii) Iteration, where users build their story progressively by repeating the beat selection and narrative expansion process.
• Figure 2: NarrativeLoom's technical pipeline. The system transforms user sparkles into story beats via multi-persona generation, employing a three-layer prompt architecture (meta-prompt, context integration, generation constraints) across both beat and text generation stages. The rag-based Plot Controller ensures narrative consistency while users iterate through beat selection and text refinement. Purple indicates beat-level operations and green indicates text-level operations. Rectangles represent functional modules, while rounded corners represent data.
• Figure 3: The NarrativeLoom interface implementing the bvsr framework. The six panels illustrate the system's functional components: (a) narrative initialization interface; (b) generation parameter configuration; (c) multi-persona beat selection; (d) structural beat modification interface; (e) narrative text expansion; and (f) dual-mode refinement mechanism. This workflow supports iterative variation and selection while preserving human creative agency.
• Figure 4: The comparison of user evaluation scores between NarrativeLoom and the chatbot. Violin plots show the distribution of user ratings on a 5-point scale across key dimensions. NarrativeLoom consistently achieved higher median scores for creativity-focused metrics like diversity and novelty, while performing comparably on usability, engagement, and coherence. Asterisks denote statistical significance: * indicates $p$ < 0.05.
• Figure 5: Users strategically selected personas and transitioned between them in structured patterns. The data reveals two key user behaviors: (a) selecting personas for specialized "initiator" or "developer" roles, and (b) moving between personas along logical, genre-adjacent pathways.