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User interface design has become increasingly difficult due to the rise of new kinds of electronic devices and the emergence of the Internet of Things (IoT). Further, user interface (UI) designers struggle to adapt their UIs to evolving user needs and preferences. In order to address these issues, we want to support end users in designing their own user interfaces. However, end-user UI design represents a major challenge, given that end users often lack the necessary design skills. We investigated how design recommendations might be used to address the research question on how to help end users during the UI design process? A first step towards answering this question is the analysis of how end users should best get recommendations about potential design improvements. We therefore conducted a survey on how end users would like to get design recommendations, whether they trust user-or machinegenerated recommendations, and whether they agree that their interactions are tracked and shared in order to improve the recommendations. Based on the results of our survey, we present a set of design requirements for the integration of recommendations in end-user UI design tools.

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The automotive industry is working toward driving automation and driver-assistance technology is becoming a norm in modern cars. Warning alert systems support the driver–car interaction and inform drivers about automation system status, upcoming obstacles, or dangers ahead. However, older drivers’ needs are not always addressed in research studies, although they make up a large segment of drivers. Therefore, we conducted a qualitative three-round formative evaluation of a warning alert system using video prototypes in lab and remote settings. The goal was to evaluate visual-, sound-, and speech-based alerts based on: (a) their efficiency in informing drivers about the road situation ahead, and (b) participants’ subjective opinions. We evaluated the system’s efficiency using self-reported data measuring participants’ cognitive load, usability, UX, and ease of use. Also, we conducted interviews to collect subjective feedback about proposed prototypes. In this article, we describe the design of warning alerts and report on their evaluation results. Our results show that speech-based warnings, especially when coupled with visual warnings, are efficient and accepted well by the participants. This article illustrates older drivers’ attitude toward the use of different warning modalities in the driving context.

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