Inspo: Writing Stories with a Flock of AIs and Humans

Chieh-Yang Huang, Sanjana Gautam, Shannon McClellan Brooks, Ya-Fang Lin, Ting-Hao Kenneth Huang

Abstract: Large Language Models (LLMs) have advanced automated writing assistance, enabling complex tasks like co-writing novels and poems. However, real-world writing typically requires various support and collaboration across stages and scenarios. Existing research mainly examines how writers utilize single text generators, neglecting this broader context. This paper introduces Inspo, a web-based editor that incorporates various text generators and online crowd workers. Through a three-phase user study, we examine writers' interactions with Inspo for novel writing. Quantitative analyses of writing logs highlight changes in participants' writing progress and the influence of various text-generation models. Complementing this with qualitative insights from semi-structured interviews, we illustrate participants' perceptions of these models and the crowd. Based on the findings, we provide design recommendations for the next generation of intelligent writing tools and discuss the potential sociocultural implications of integrating AI and human input in the writing process.