  When we interact with small screen devices, sometimes we make errors, due to
our abilities/disabilities, contextual factors that distract our attention or
problems related to the interface. Recovering from these errors may be time
consuming or cause frustration. Predicting and learning these errors based on
the previous user interaction and contextual factors, and adapting user
interface to prevent from these errors can improve user performance and
satisfaction. In this paper, we propose a system that aims to monitor user
performance and contextual changes and do adaptations based on the user
performance by using machine learning techniques. Here, we briefly present our
systematic literature review findings and discuss our research questions
towards developing such an adaptive system.
