While large language models (LLMs) are increasingly applied in creative domains, their role in supporting interactive storytelling tailored to creators’ needs remains underexplored. This thesis adopts a creator-centered perspective to examine how LLMs can assist in building interactive narratives, focusing on multi-layered structure editing, automated analysis, target user feedback, and the preservation of authorial control. A multi-stage design was employed: interviews with sixteen creators identified five key design goals, which informed the development of \textit{CoNoder}, a prototype integrating node-graph editing, dual interaction modes, and generation styles, ripple-effect analysis, and simulated feedback. Evaluation results show that \textit{CoNoder} improves creative efficiency, supports morally complex storytelling, and provides structured narrative feedback, though onboarding, expert guidance, and finer control remain areas for improvement. Overall, this research contributes a creator-focused framework and a practical system design approach, highlighting the need for future tools that balance expressive freedom with creative sovereignty.
ACM CHI Conference on Human Factors in Computing Systems