  Human-machine interfaces (HMI) facilitate communication between humans and
machines, and their importance has increased in modern technology. However,
traditional HMIs are often static and do not adapt to individual user
preferences or behavior. Adaptive User Interfaces (AUIs) have become
increasingly important in providing personalized user experiences. Machine
learning techniques have gained traction in User Experience (UX) research to
provide smart adaptations that can reduce user cognitive load. This paper
presents an ongoing exploration of a method for generating adaptive user
interfaces by analyzing user interactions and contextual data. It also provides
an illustrative example using Markov chains to predict the next step for users
interacting with an app for an industrial mixing machine. Furthermore, the
paper conducts an offline evaluation of the approach, focusing on the precision
of the recommendations. The study emphasizes the importance of incorporating
user interactions and contextual data into the design of adaptive HMIs, while
acknowledging the existing challenges and potential benefits.

Human-Computer Interaction