Directional Alignment and Narrative Agency in Human-LLM Co-Writing
For researchers and practitioners in human-AI interaction, this work provides empirical evidence of asymmetric roles in co-writing, clarifying how agency is distributed.
The study investigates narrative agency in human-LLM co-writing, finding that humans drive narrative innovation and direction while LLMs act as adaptive amplifiers that sustain coherence and elaborate on human-introduced elements.
We investigate narrative agency in human-LLM creative co-writing, asking who drives story development in turn-based collaboration. Using a new corpus of 87 human-LLM co-written stories, we apply sentiment and semantic modeling to quantify affective alignment and semantic novelty in turn-taking, and directional measures to assess which agent shapes narrative progression. Our results show asymmetric influence: human turns introduce greater semantic novelty and are more likely to shape subsequent developments, whereas LLM contributions predominantly elaborate on human-introduced elements. At the sentiment level, alignment is also asymmetric, but more bidirectional: LLMs exhibit stronger turn-level emotional adaptation than humans, but both agents track each other's emotional valence and LLMs show an independent tendency to more positive emotional baselines. These findings indicate a complementary division of labor in human-LLM co-writing, where humans drive narrative innovation and direction, while LLMs act as adaptive amplifiers that sustain coherence and elaborate emerging narratives.