Autonomous vehicles (AVs; SAE levels 4 and 5) face substantial challenges regarding acceptance and UX. Novel human-machine interfaces (HMIs) providing transparent system information could account for those and facilitate adoption. However, since the availability of AVs for early concept studies is limited, context-based interface prototyping is required. This paper demonstrates the prototype and wizard-of-oz-based on-road evaluation of a futuristic windshield HMI concept that visualizes real-time object detections via augmented reality (AR). In a mixed-methods within-subjects study (N = 30), participants assessed three early-stage concept variants to explore whether object detection visualization can counteract the aforementioned challenges. The findings confirm that transparent system feedback can increase understandability, perceived usefulness, and hedonic UX, but the amount and the timing of the provided information are crucial. The applied prototyping method proved suitable for investigating HMI concepts with real-time AR on urban roads. Based on a critical discussion, the paper concludes with design and prototyping recommendations.

Personas allow designers to build empathy towards users, avoiding reliance on stereotypes or assumptions. When it comes to creating personas for people with disabilities, a common procedure is to integrate impairments as dedicated characteristics. However, when considering Deaf and Hard of Hearing (DHH) individuals as target group, we argue that such an approach is incompatible with the latter’s own ability-based identification concepts. Thus, we propose a modification of personas for DHH users by defining them in terms of ability ranges and driving or keeping factors within specific contexts. Starting with assumption personas based on previous research, we conducted focus groups with DHH individuals from which we derived the data and perspectives that formed our modification. With this approach, we want to promote an adequate representation of DHH users among designers while also demonstrating an application of Ability-Based Design to HCI methods.

In this experience report, we describe an approach to ability-based focus groups with sign language users in a remote environment. We discuss our main lessons learned in terms of requirements for sign language-accessibility within research, calling out issues such as the need to address users in their natural language, ensuring translation for all parts of research processes, and including users not only within the conducted method but already within preparation phases. Based on requirements such as these, we argue that HCI research currently faces a dilemma when it comes to hearing researchers working with the sign language user population—having to handle the increasingly emphasized demand for conducting user research with this specific target group while lacking accessible tools and procedures to do so. Concluding our experience report, we address this dilemma by discussing the two sides of its fundamental challenge: Inadequate communication with and insufficient representation of sign language users within research.

In autonomous mobility-on-demand (AMoD) systems, passengers will solely interact with autonomous vehicles via digital user interfaces (UIs). Hence, UIs are crucial for acceptance and user experience (UX). As a foundation for deriving empirically grounded design guidelines, we investigate two approaches for mobile interaction: chatbots and ‘classic’ graphical UIs (GUIs). We evaluated prototypes of both in expert studies (nGUI = 6; nChatbot = 5) and a between-subjects simulator user study (n = 34). The latter enabled participants to experience a complete AMoD journey. While both concepts receive good acceptance and positive UX evaluations, the GUI results in significantly higher attractiveness and user satisfaction ratings. A significant interaction effect reveals a higher intention to use the chatbot in scenarios with a change of plans, but the GUI in ‘happy path’ scenarios. Interview data and emotion curves support this effect. Balancing the concepts‘ advantages and disadvantages, we provide design recommendations and propose to use GUI-based mobile applications with integrated (text-based) conversational elements for future human-AMoD interaction.

Autonomous vehicles (AVs; SAE levels 4 and 5) develop rapidly, whereas appropriate methods for interface design and development for such driverless vehicles are still in their infancy. This paper presents a simple approach for context-based prototyping and evaluation of human-machine interfaces for (shared) AVs in public transportation. It demonstrates how to set up a lightweight immersive video-based AV simulator using real-world video and audio footage captured in urban traffic. In two user studies (n1 = 9; n2 = 31) we investigate presence perception and simulator sickness to provide initial evidence for the suitability of this cost-effective method. Furthermore, with the intent to increase presence perception and technology acceptance, we combine the AV simulator with a human actor imitating a passenger that gets on and off a shared AV ride.

Intensive Care Unit (ICU) professionals have to make lifesaving therapy decisions promptly under high stress and uncertainty. Clinical Decision Support Systems (CDSS) can improve the quality of healthcare by identifying complex statistical connections between patients' parameters and by rapidly presenting the statistically most promising treatment options to physicians. However, HCI aspects are rarely considered when developing CDSSs. This paper describes a field study conducted in three ICUs investigating how physicians and nurses form (volume) therapy decisions and monitor their success. Our findings reveal a continuous decision cycle in which nurses and physicians collaborate synchronously and asynchronously to provide optimal care. Furthermore, the desire to understand how a CDSS generated recommendations varies depending on the user's goals and other contextual factors such as workload. These findings show that CDSSs for ICUs need to (1) specifically facilitate collaboration and (2) support adaptation of the interface to both context and users.

The strains associated with shift work decrease healthcare workers' well-being. However, shift schedules adapted to their individual needs can partially mitigate these problems. From a computing perspective, shift scheduling was so far mainly treated as an optimization problem with little attention given to the preferences, thoughts, and feelings of the healthcare workers involved. In the present study, we explore fairness as a central, human-oriented attribute of shift schedules as well as the scheduling process. Three in-depth qualitative interviews and a validating vignette study revealed that while on an abstract level healthcare workers agree on equality as the guiding norm for a fair schedule, specific scheduling conflicts should foremost be resolved by negotiating the importance of individual needs. We discuss elements of organizational fairness, including transparency and team spirit. Finally, we present a sketch for fair scheduling systems, summarizing key findings for designers in a readily usable way.

In this paper we explore the fusion of a sophisticated user interface prototyping tool with a cognitive modeling approach based on the ACT-R cognitive architecture. The resulting tool allows the creation of complex interactive prototypes for which quantitative performance predictions can be derived by running cognitive models. Such models are automatically generated by monitoring a designer's interaction while completing a task scenario with a prototype. Using the example of a real-world user interface for a software-controlled riveting machine, we demonstrate the scope of the approach and compare model predictions to data gathered in two empirical studies. The encouraging goodness-of-fit of the predictions strongly suggests that the use of such simulated interactions with prototypes provides a promising and cost-effective approach to predict learning trajectories and performance data for routine tasks. We argue that predictive prototyping fosters the acceleration of iteration cycles without resulting in additional expenditures for UX designers.