In an increasingly digital world, the ability of systems to adapt to individual users has become essential. Among cognitive variables informing such systems, the human short-term memory plays a crucial role, as it is responsible for perceiving, storing and retrieving data. This study explores the modalities in which various information about memory-related processes has been used in the field of Human-Centered AI (HCAI) by analyzing input methods, objectives, application domains and nature of implemented adaptations. By conducting a systematic literature review, the paper aims to address this gap, while identifying current trends and challenges. The 45 papers included in this study revealed that increasing or decreasing complexity of content or adjusting difficulty levels based on accuracy rates or time periods can improve human performance and support learning in computer-based environments. Such personalization are especially useful in the educational and healthcare domains. These results provide a basis of design guidelines for future research of adaptive mechanisms considering memory-related information. This survey is anticipated to be a starting point for upcoming developments in this field or succeeding reviews that could incorporate a wider range of the memory scope.