Aiding narrative generation in collaborative data utilization by humans and AI agents

Abstract Narrative generation is growing in importance for data utilization, particularly in the context of co-creation with artificial intelligence (AI) agents. Narratives can, for example, bridge theoretical objects with social understanding and promote human actions. Furthermore, clarifying the narrative generation mechanism is essential for constructing effective relationships between humans and AI agents. However, the narrative generation mechanism in data utilization processes has not been fully elucidated. In this study, we developed a framework called the hierarchical narrative representation (HieNaR) to systematize the structure of narrative generation in data utilization processes. HieNaR comprises twelve levels, ranging from the set of texts down to the particle level (e.g., text, sentence, word, character, and stroke), allowing for a comprehensive analysis of narrative structures. We evaluated the usefulness of HieNaR through case studies, examining both individual user experiences and collaborative work between humans and an AI agent. The results demonstrated that the data utilization process interprets data by inquiring whether it satisfies higher-level expectations. In collaboration, AI agents can be understood as co-creative partners in data utilization, possessing their own worldviews. Through these findings, this study not only elucidates the mechanism of narrative generation in data utilization processes but also provides a foundation for improving human–AI collaboration.