  To achieve optimal human-system integration in the context of user-AI
interaction it is important that users develop a valid representation of how AI
works. In most of the everyday interaction with technical systems users
construct mental models (i.e., an abstraction of the anticipated mechanisms a
system uses to perform a given task). If no explicit explanations are provided
by a system (e.g. by a self-explaining AI) or other sources (e.g. an
instructor), the mental model is typically formed based on experiences, i.e.
the observations of the user during the interaction. The congruence of this
mental model and the actual systems functioning is vital, as it is used for
assumptions, predictions and consequently for decisions regarding system use. A
key question for human-centered AI research is therefore how to validly survey
users' mental models. The objective of the present research is to identify
suitable elicitation methods for mental model analysis. We evaluated whether
mental models are suitable as an empirical research method. Additionally,
methods of cognitive tutoring are integrated. We propose an exemplary method to
evaluate explainable AI approaches in a human-centered way.
