# Evaluating Quality of Gaming Narratives Co-created with AI

arXiv: https://arxiv.org/abs/2509.04239 · HTML: https://mlsanity.com/paper/2509.04239/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 22 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Builds on (most cited first, 4 of 4)

- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Language as Reality: A Co-Creative Storytelling Game Experience in 1001 Nights using Generative AI](https://mlsanity.com/paper/2308.12915/) (2023)
- [What Makes a Good Story and How Can We Measure It? A Comprehensive Survey of Story Evaluation](https://mlsanity.com/paper/2408.14622/) (2024)
- [Fictional Worlds, Real Connections: Developing Community Storytelling Social Chatbots through LLMs](https://mlsanity.com/paper/2309.11478/) (2023)

## Built on by (most cited first, 1 of 1)

- [Improving Collaborative Storytelling with a Multi-Agent Framework Based on Large Language Models](https://mlsanity.com/paper/2605.29625/) (2026)

## Abstract (arXiv)

This paper proposes a structured methodology to evaluate AI-generated game narratives, leveraging the Delphi study structure with a panel of narrative design experts. Our approach synthesizes story quality dimensions from literature and expert insights, mapping them into the Kano model framework to understand their impact on player satisfaction. The results can inform game developers on prioritizing quality aspects when co-creating game narratives with generative AI.