Who embraces AI in play? Exploratory modeling of player preference profiles toward game AI
Ting-Chen Hsu, Jiangxu Lin, Wenran Chen, Zheyuan Zhang, Fei Qin

TL;DR
This study models player attitudes towards game AI across different contexts, identifying seven distinct profiles and exploring their associations with player characteristics to inform more tailored AI integration.
Contribution
It introduces an interpretable profiling approach using Archetypal Analysis to understand complex player attitudes towards game AI across multiple contexts.
Findings
Seven distinct player profiles identified through Archetypal Analysis.
Profile membership correlates with AI literacy, gaming habits, and personality traits.
Provides a vocabulary for segmenting game AI audiences for better design.
Abstract
Artificial intelligence is increasingly entering digital games through diverse functions. While prior work has shown that player attitudes toward game AI are strongly context-dependent, less is known about how these attitudes are structurally combined within different groups of players. This study addresses this gap by modeling players' cross-context AI acceptance as interpretable attitude profiles. Based on questionnaire data from 771 digital game players, we apply Archetypal Analysis (AA) to centered acceptance ratings across eight representative AI application contexts in games. The analysis identifies seven distinctive profiles: AI-Skeptics, Broad AI-Supporters, Creative-Play Explorers, Experience-Oriented Supporters, Systemic Order Advocates, Emotion-Centered Supporters, and Governance-Skeptics. Exploratory one-vs-rest (OvR) logistic regressions further suggest that profile…
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