Evaluating Quality of Gaming Narratives Co-created with AI

Arturo Valdivia Martinez; Paolo Burelli

doi:10.48550/arXiv.2509.04239

Evaluating Quality of Gaming Narratives Co-created with AI

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

Abstract

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.

Originalsprog	Engelsk
Titel	Proceedings of the 2025 IEEE Conference on Games
Forlag	IEEE
Publikationsdato	2025
DOI	https://doi.org/10.48550/arXiv.2509.04239 https://doi.org/10.1109/CoG64752.2025.11114354
Status	Udgivet - 2025
Begivenhed	IEEE 7th Conference on Games 2025 - Instituto Superior Tecnico, Lisbon, Portugal Varighed: 26 aug. 2025 → 29 aug. 2025 Konferencens nummer: 7 https://cog2025.inesc-id.pt/

Konference

Konference	IEEE 7th Conference on Games 2025
Nummer	7
Lokation	Instituto Superior Tecnico
Land/Område	Portugal
By	Lisbon
Periode	26/08/2025 → 29/08/2025
Internetadresse	https://cog2025.inesc-id.pt/

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Citationsformater

@inproceedings{81e56ec37811444caab57826d766c041,

title = "Evaluating Quality of Gaming Narratives Co-created with AI",

abstract = "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.",

keywords = "AI-generated narratives, Delphi method, Kano model, game narrative quality, player satisfaction",

author = "\{Valdivia Martinez\}, Arturo and Paolo Burelli",

year = "2025",

doi = "10.48550/arXiv.2509.04239",

language = "English",

booktitle = "Proceedings of the 2025 IEEE Conference on Games",

publisher = "IEEE",

address = "United States",

note = "IEEE 7th Conference on Games 2025, IEEE CoG 2025 ; Conference date: 26-08-2025 Through 29-08-2025",

url = "https://cog2025.inesc-id.pt/",

}

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T1 - Evaluating Quality of Gaming Narratives Co-created with AI

AU - Valdivia Martinez, Arturo

AU - Burelli, Paolo

N1 - Conference code: 7

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

KW - AI-generated narratives

KW - Delphi method

KW - Kano model

KW - game narrative quality

KW - player satisfaction

U2 - 10.48550/arXiv.2509.04239

DO - 10.48550/arXiv.2509.04239

M3 - Article in proceedings

BT - Proceedings of the 2025 IEEE Conference on Games

PB - IEEE

T2 - IEEE 7th Conference on Games 2025

Y2 - 26 August 2025 through 29 August 2025

ER -

Evaluating Quality of Gaming Narratives Co-created with AI

Abstract

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