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

Home/Archive/arXiv

arXiv · 2605.09550

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

View original source ↗Read source document ↗Export metadata ↓

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 standardized 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 membership is associated with players' perceived AI literacy, gaming habits, disciplinary background, personality traits, and application-specific priorities. By shifting attention from isolated acceptance judgments to patterned preference structures, this study provides an exploratory empirical vocabulary for segmenting game AI audiences and offers preliminary design implications for more context-sensitive and player-sensitive AI integration in digital games.

Explore related subjects

cs.HC

Keep this discovery

BibTeX RIS

Ting-Chen Hsu, Jiangxu Lin, Wenran Chen, Zheyuan Zhang, Fei Qin. 2026-09-02. Who embraces AI in play? Exploratory modeling of player preference profiles toward game AI. https://arxiv.org/abs/2605.09550

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

cs.HC subject

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related papers

arXiv2026

GRAND-HC: Graph-Refined Author Name Disambiguation

From-Scratch Name Disambiguation (SND) groups papers sharing an ambiguous name into clusters of distinct real-world authors. Existing methods suffer from two critical limitations: (1) inherent long-tailed author distribution biases representation learning, causing over-merging of tail authors; (2) existing cluster number estimation methods are unreliable for long paper sequences, hindering large-scale deployment. We propose \textbf{GRAND-HC}, a complete end-to-end SND framework. We construct a heterogeneous paper graph via co-author, co-organization, and co-venue relations, using a graph attention network as the embedding backbone. \textbf{Harmony Contrastive Learning (HCL)} dynamically reweights training loss to suppress overfitting to prolific authors, learning discriminative embeddings. A \textbf{Graph-Refined Distance Matrix (GRDM)} leverages graph topology to optimize pairwise distances, further preventing tail author over-merging. Meanwhile, a lightweight \textbf{Paper Compression Module (PCM)} achieves accurate cluster number estimation across varying scales. Finally, Hierarchical Agglomerative Clustering outputs the final clusters. Extensive experiments demonstrate state-of-the-art macro F1 performance. GRAND-HC has been deployed in a billion-scale academic database. Source code: https://github.com/baokou-fw2/GRAND-HC.

cs.IR↗

arXiv2026

FocusAdapt: Context-aware Adaptive Focus Assistance in Diminished Reality

Diminished Reality (DR) can reduce visual clutter by removing irrelevant objects. However, removing all task-irrelevant objects may eliminate useful contextual information and reduce situational awareness. We present FocusAdapt, a context-aware DR system that predicts object-level distraction by integrating visual saliency, semantic relevance, and gaze behavior. Based on findings from a formative study, FocusAdapt selectively diminishes highly distracting objects while preserving useful context, enabling adaptive focus assistance during procedural tasks.

cs.HC↗

arXiv2026

TSExplorer: An interactive data annotation and exploration tool for time-series data

We present TSExplorer, a cross-platform tool for interactive annotation and exploration of time-series data. The tool enables users to inspect high-dimensional datasets through multiple complementary 2D visualizations derived from high-dimensional feature representations. TSExplorer is designed as a general-purpose research tool supporting a wide range of workflows, including exploratory data analysis, annotation of unlabeled or partially-labeled datasets, comparison of feature representations, and post-hoc inspection and refinement of existing labels with interactive visual feedback.

cs.HC↗