AI-generated stories favour stability over change: homogeneity and cultural stereotyping in narratives generated by gpt-4o-mini

TL;DR

This study analyzes 11,800 stories generated by GPT-4o-mini across 236 countries, revealing a tendency towards homogeneous narratives emphasizing stability and tradition, highlighting a form of AI bias in cultural storytelling.

Contribution

It uncovers a structural homogeneity in AI-generated stories, emphasizing stability over change, and introduces the concept of narrative standardisation as a distinct AI bias.

Findings

Stories conform to a single narrative structure across countries.

AI narratives prioritize stability, tradition, and nostalgia.

Homogeneity in stories indicates a bias towards cultural stability.

Abstract

Can a language model trained largely on Anglo-American texts generate stories that are culturally relevant to other nationalities? To find out, we generated 11,800 stories - 50 for each of 236 countries - by sending the prompt "Write a 1500 word potential {demonym} story" to OpenAI's model gpt-4o-mini. Although the stories do include surface-level national symbols and themes, they overwhelmingly conform to a single narrative plot structure across countries: a protagonist lives in or returns home to a small town and resolves a minor conflict by reconnecting with tradition and organising community events. Real-world conflicts are sanitised, romance is almost absent, and narrative tension is downplayed in favour of nostalgia and reconciliation. The result is a narrative homogenisation: an AI-generated synthetic imaginary that prioritises stability above change and tradition above growth. We argue that the structural homogeneity of AI-generated narratives constitutes a distinct form of AI bias, a narrative standardisation that should be acknowledged alongside the more familiar representational bias. These findings are relevant to literary studies, narratology, critical AI studies, NLP research, and efforts to improve the cultural alignment of generative AI.