Notifications from instant messaging applications can interrupt employees' productive time. While there are different ways to influence the notification behavior of instant messengers, such as turning off the application or muting notifications for certain periods of time, these measures require self-discipline and/or often result in missing notifications when not in flow. We have developed an adaptive instant messaging blocker that aims to solve this problem by recognizing the user's flow state at predefined intervals, based on their physiological data and using machine learning methods. As soon as a flow state is recognized, the “do not disturb” status is automatically activated for the duration of the flow state.

We conducted interviews with knowledge workers to evaluate the developed system. Therefore, a qualitative analysis (with MAXQDA) is to be carried out in this Master's thesis in order to evaluate the system on the basis of the interviews conducted.

The majority of explanations that have emerged from XAI research are static and have been based on the assumption that there is a single message to be conveyed by the explanation. Thus, empowering humans to explore explanations for algorithmic decisions interactively seems promising and there are already studies in this regard. For example, research is investigating how data scientists work or tools are being offered to help data scientists use and understand ML algorithms. However, we still need to adjust these interfaces to diverse target groups and capitalize on interactivity, especially for non-expert users.

Thus, as part of this Bachelor thesis, a literature review will be conducted to explore the state-of-the-art on empirical user-based studies in XAI research about interactive explanations and interfaces for diverse target groups.