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 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.

翻译：人工智能正通过多种功能日益渗透数字游戏。虽然已有研究表明玩家对游戏AI的态度具有强烈的上下文依赖性，但对于这些态度在不同玩家群体中如何结构化组合，我们知之甚少。本研究通过将玩家跨情境的AI接受度建模为可解释的态度特征，弥补了这一空白。基于771名数字游戏玩家的问卷数据，我们对游戏中八个代表性AI应用情境的中心化接受度评分应用了典型点分析（Archetypal Analysis, AA），识别出七种独特特征：AI怀疑论者、广泛AI支持者、创意游戏探索者、体验导向支持者、系统秩序倡导者、情感中心支持者以及治理怀疑论者。探索性一对多逻辑回归进一步表明，特征归属与玩家感知的AI素养、游戏习惯、学科背景、人格特质及特定应用优先级相关。通过将关注点从孤立的接受判断转向模式化的偏好结构，本研究为细分游戏AI受众提供了一个探索性的实证词汇，并为数字游戏中更具情境敏感性和玩家敏感性的AI集成提供了初步设计启示。