As automated driving technology continues to advance, the question of how users prefer to be driven in their new, more passive role is becoming increasingly relevant. In this paper, a real-world study on a German motorway with 42 participants was conducted to analyze driving style preferences for conditional automated driving, taking the participants’ personal driving style into account. In the first part, participants’ personal driving style (PDS) was recorded during a manual drive in the first half on a given route. For the second half, participants were asked to demonstrate their desired driving style (DDS) for conditional automated driving. In the second part, participants were driven on the same route in a defensive automated vehicle (AV) while rating driving comfort and safety. Subsequently, the relationship between driving style differences and ratings was analyzed. Furthermore, a comparison between PDS and DDS was performed. The results show that very defensive to moderate drivers perceived the AV’s driving style, being similar to their own, as equally safe but significantly more comfortable than moderate to very aggressive drivers. No influence of driving style differences was found on the increase in trust. However, a significant increase in trust after experiencing an automated vehicle has been observed. Furthermore, the rated system characteristics of anthropomorphism, safety, and overall driving strategy had a significant influence on driving style preferences for AVs. This study makes an important contribution to answering the question of how users want to be driven in conditional automated driving.

Explainable AI (XAI) is discussed as an important feature of AI systems that is required for professional, ethical and commercial reasons. XAI is particularly needed in AI systems that remain relatively closed, i.e. black box, on how to make decisions whether for complexity or commercial sensitivity. Still, many factors play an important role on whether people perceive a need for XAI. The personal relevance of the AI system might play a role, but also individual differences in personality traits or cognitive functions. In order to test these assumptions, 215 participants (129 female) underwent an online experiment testing their objective working memory capacity and completed the Intellect facet of the Big Five Personality traits (as a proxy for self-assessed cognitive capacity). Further, all participants were presented three AI systems scenarios of relatively different degrees of (personal) relevance and, hence, personal investment: skin cancer detection (i), the personalization of social media feeds (ii), and the amount to pay for a car insurance (iii). We observed that people reported a higher need for XAI in scenarios where they were more personally invested (health, payment for insurance > social media); for signficant differences between these scenarios see the result section. Interestingly, working memory capacity was not associated with XAI needs across the three domains, but self-reported Intellect showed a mild positive association with need for an AI explanation in the context of the insurance story. The present study represents a first attempt to investigate objective working memory capacity in the context of need for XAI. Beyond that, the study indicates that personal investment is crucial for determining the need for XAI.

The integration of AI (artificial intelligence) in workplaces has increased automation but often at the cost of transparency, potentially undermining user trust. Adaptable, user-centered systems address this challenge by enhancing users' understanding of the system and tailoring interactions to their needs through adaptable elements that allow them to customize system settings. In an online study with a between-subjects design, 197 participants interacted with an adaptable or non-adaptable system for decision-making to examine the influence of its features on perception (transparency, fairness, control), trust, and intention to use. Results showed that adaptability positively affected perceived transparency, fairness, and especially control. Furthermore, the intention to use was positively influenced both directly by perceived fairness and indirectly through two serial mediation pathways: one from perceived fairness through trust, and another from perceived control through trust. This study underscores the importance of adaptable design elements in human-computer interaction, demonstrating that they enhance user perception and intention to use, whereas AI systems that restrict user involvement and autonomy risk diminishing both trust and intention to use.

When implementing collaborative robot applications, it is paramount to use a human-centered development approach to ensure a positive user experience and increase acceptance. User Experience (UX) design methods involve validating user experience through evaluations as a basic principle. The earlier UX evaluations are carried out, the greater the added value that can be achieved. For collaborative robot applications, especially those involving large robot systems, these early evaluations are challenging since the entire application will not be available until the final stages of development. The proposed methodological approach to this problem utilizes a rudimentary, scaled test setup for early UX evaluations of the entire robot application, enabling user feedback to be incorporated into the development process early on. The method was applied in a project that developed a collaborative robot application for semi-automated liquid cargo handling in inland navigation. UX assessments were carried out using both the rudimentary test setup and a full-scale prototype in a later development phase. The comparison of both assessments proves the applicability of the proposed methodology. Serving as an overarching framework, this methodology encourages developers to test the entire collaborative robot application in user studies at an early stage, thereby gathering valuable user feedback.