TY - JOUR A1 - Döllinger, Nina A1 - Wolf, Erik A1 - Mal, David A1 - Wenninger, Stephan A1 - Botsch, Mario A1 - Latoschik, Marc Erich A1 - Wienrich, Carolin T1 - Resize Me! Exploring the user experience of embodied realistic modulatable avatars for body image intervention in virtual reality JF - Frontiers in Virtual Reality N2 - Obesity is a serious disease that can affect both physical and psychological well-being. Due to weight stigmatization, many affected individuals suffer from body image disturbances whereby they perceive their body in a distorted way, evaluate it negatively, or neglect it. Beyond established interventions such as mirror exposure, recent advancements aim to complement body image treatments by the embodiment of visually altered virtual bodies in virtual reality (VR). We present a high-fidelity prototype of an advanced VR system that allows users to embody a rapidly generated personalized, photorealistic avatar and to realistically modulate its body weight in real-time within a carefully designed virtual environment. In a formative multi-method approach, a total of 12 participants rated the general user experience (UX) of our system during body scan and VR experience using semi-structured qualitative interviews and multiple quantitative UX measures. Using body weight modification tasks, we further compared three different interaction methods for real-time body weight modification and measured our system’s impact on the body image relevant measures body awareness and body weight perception. From the feedback received, demonstrating an already solid UX of our overall system and providing constructive input for further improvement, we derived a set of design guidelines to guide future development and evaluation processes of systems supporting body image interventions. KW - virtual reality KW - avatar embodiment KW - user experience KW - body awareness KW - body weight perception KW - body weight modification KW - body image disturbance KW - eating and body weight disorders Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-292940 SN - 2673-4192 VL - 3 ER - TY - JOUR A1 - Wienrich, Carolin A1 - Carolus, Astrid A1 - Roth-Isigkeit, David A1 - Hotho, Andreas T1 - Inhibitors and enablers to explainable AI success: a systematic examination of explanation complexity and individual characteristics JF - Multimodal Technologies and Interaction N2 - With the increasing adaptability and complexity of advisory artificial intelligence (AI)-based agents, the topics of explainable AI and human-centered AI are moving close together. Variations in the explanation itself have been widely studied, with some contradictory results. These could be due to users’ individual differences, which have rarely been systematically studied regarding their inhibiting or enabling effect on the fulfillment of explanation objectives (such as trust, understanding, or workload). This paper aims to shed light on the significance of human dimensions (gender, age, trust disposition, need for cognition, affinity for technology, self-efficacy, attitudes, and mind attribution) as well as their interplay with different explanation modes (no, simple, or complex explanation). Participants played the game Deal or No Deal while interacting with an AI-based agent. The agent gave advice to the participants on whether they should accept or reject the deals offered to them. As expected, giving an explanation had a positive influence on the explanation objectives. However, the users’ individual characteristics particularly reinforced the fulfillment of the objectives. The strongest predictor of objective fulfillment was the degree of attribution of human characteristics. The more human characteristics were attributed, the more trust was placed in the agent, advice was more likely to be accepted and understood, and important needs were satisfied during the interaction. Thus, the current work contributes to a better understanding of the design of explanations of an AI-based agent system that takes into account individual characteristics and meets the demand for both explainable and human-centered agent systems. KW - explainable AI KW - human-centered AI KW - recommender agent KW - explanation complexity KW - individual differences Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-297288 SN - 2414-4088 VL - 6 IS - 12 ER -