Alignment, Exploration, and Novelty in Human-AI Interaction

TL;DR

This study explores how humans and large language models co-create narratives, revealing that humans introduce more novelty and exploration, while models primarily drive emotional alignment, highlighting unique aspects of human-AI storytelling.

Contribution

It introduces a scalable framework for analyzing human-AI interaction dynamics, emphasizing the roles of emotional alignment, semantic exploration, and creativity in collaborative storytelling.

Findings

Humans explore broader semantic space and introduce more narrative novelty.

Affective alignment is mainly driven by the AI model, with limited mutual convergence.

Human contributions significantly influence narrative direction and divergence.

Abstract

Human-AI interactions are increasingly part of everyday life, yet the interpersonal dynamics that unfold during such exchanges remain underexplored. This study investigates how emotional alignment, semantic exploration, and linguistic innovation emerge within a collaborative storytelling paradigm that paired human participants with a large language model (LLM) in a turn-taking setup. Over nine days, more than 3,000 museum visitors contributed to 27 evolving narratives, co-authored with an LLM in a naturalistic, public installation. To isolate the dynamics specific to human involvement, we compared the resulting dataset with a simulated baseline where two LLMs completed the same task. Using sentiment analysis, semantic embeddings, and information-theoretic measures of novelty and resonance, we trace how humans and models co-construct stories over time. Our results reveal that affective alignment is primarily driven by the model, with limited mutual convergence in human-AI interaction. At the same time, human participants explored a broader semantic space and introduced more novel, narratively influential contributions. These patterns were significantly reduced in the simulated AI-AI condition. Together, these findings highlight the unique role of human input in shaping narrative direction and creative divergence in co-authored texts. The methods developed here provide a scalable framework for analysing dyadic interaction and offer a new lens on creativity, emotional dynamics, and semantic coordination in human-AI collaboration.