Dynamic difficulty adjustment approaches in video games: a systematic literature review

Providing an appropriate difficulty level in a game is critical for keeping players engaged. Dynamic Difficulty Adjustment (DDA) is a common approach for optimizing player experience by automatically modifying game aspects. This paper reviews literature addressing mechanisms for adjusting video game difficulties in response to players’ performance, emotions, or personality. For this purpose, we examined DDA studies using employed machine-learning techniques, player modeling approaches, data types used to assess players’ states, testbed game genre, and application. Journal and conference articles published up to September 2022 served as the data sources in this review. The findings reveal that most studies have shown significant effects of DDA on parameters such as enjoyment, flow, motivation, engagement, and immersion. In addition, machine-learning and player modeling techniques have recently received more attention in the DDA design. However, given the ever-increasing use of games in various domains, more research is needed to understand player preferences better to adjust game parameters efficiently. By conducting further research into players’ cognitive characteristics, such as visual attention, working memory, and response time, it will be possible to understand players’ preferences better.