Recognizing player state is only one component of affective game adaptation; inferred experience must also be translated into adaptive interventions that modify gameplay or game content. Although player experience modeling and content adaptation are established research areas, fewer studies examine how sensing, modeling, and adaptation are integrated into complete, empirically evaluated gameplay systems. This PRISMA-guided systematic review analyzes 23 empirical studies published from January 1, 2015, to December 31, 2025, that implement a complete experience-driven loop defined here as the combination of player data acquisition, player experience modeling, and adaptive game content. Complete-loop systems were relatively uncommon in the retrieved corpus, and the selected systems were predominantly oriented toward dynamic difficulty adjustment, engagement, rehabilitation, or performance-related goals. Game telemetry was the dominant input modality, while non-invasive sources with affective relevance, such as facial expression analysis and peripheral interaction data, were less common. Knowledge-based methods, including rule-based systems and heuristics, dominated both modeling and adaptation because of their interpretability and low deployment requirements, whereas machine learning approaches were less frequent and remained constrained by data availability, transparency, and runtime integration challenges. Most importantly, affective information was often used to support challenge calibration or related adaptation objectives, while stress, anxiety, horror, and related affective states were rarely addressed as explicit adaptation targets. These findings identify a gap within this review scope: affective information may enter an adaptive loop without making affective state the objective of adaptation.

翻译：识别玩家状态仅是情感游戏自适应的一个组成部分；推断出的体验还必须转化为修改游戏玩法或游戏内容的自适应干预措施。尽管玩家体验建模和内容自适应已是成熟的研究领域，但关注如何将感知、建模和自适应整合为完整的、经实证评估的游戏系统的研究仍较少。本项遵循PRISMA指南的系统综述分析了2015年1月1日至2025年12月31日期间发表的23项实证研究，这些研究实现了完整的体验驱动闭环，该闭环定义为玩家数据采集、玩家体验建模和自适应游戏内容的组合。检索文献中，完整闭环系统相对罕见，所选系统主要面向动态难度调整、参与度、康复或与表现相关的目标。游戏遥测是主要的输入模态，而具有情感相关性的非侵入性来源（如面部表情分析和外围交互数据）则较少使用。基于知识的方法（包括基于规则的系统与启发式方法）因可解释性和低部署需求而在建模和自适应中占据主导地位；机器学习方法则较少被使用，并仍受限于数据可用性、透明度和运行时集成等挑战。最重要的是，情感信息常被用于支持挑战校准或相关自适应目标，而压力、焦虑、恐惧及相关情感状态很少被明确列为自适应目标。这些发现揭示了本综述范围内的一个空白：情感信息可能进入自适应循环，但并未使情感状态成为自适应的目标。