Anthropomorphizing Artificial Intelligence (AI), i.e., ascribing human-like mind or emotions to it, is widespread but varies across individuals. We tested three proposed dispositional predictors of anthropomorphism (need for cognition, need for structure, loneliness) in a general population (N = 307) and an AI expert sample (N = 130). Using a vignette design based on excerpts from a dialogue between the large language model LaMDA and one of its engineers, we found that none of the three dispositional traits predicted anthropomorphism. Instead, higher levels of AI knowledge decreased anthropomorphism across both samples. Experts reported higher AI knowledge and lower anthropomorphism than laypersons. For laypersons, anthropomorphism increased intentions to use LaMDA. For experts it did not, but was correlated with discomfort. In both samples, anthropomorphism was associated with greater moral care, i.e., not switching off LaMDA against "its will". Our findings highlight the role of knowledge and expertise in perceptions of AI.

Occupational exoskeletons are designed to support workers in strenuous tasks and to promote health, yet their implementation and use often present challenges due to the close interaction between wearer and device. This study explored user perceptions of occupational exoskeletons through qualitative focus groups conducted after participants had gained hands-on experience with 16 different devices in four-hour trials. Key findings highlight users’ feedback on system sound, design, and support, movement restriction and wearer comfort, and underscore the important role of bodily sensations—alongside factors, such as usability and appearance—in exoskeleton user experience. A central discovery was the existence of conflicts between user preferences, for instance, between light-weight designs and effective user support. Based on these insights, we highlight implications for human-centered design of exoskeletons and aim to inspire further research within the human–computer interaction community.

Researchers in Human-Robot Interaction (HRI) increasingly consider Virtual Reality (VR) for running user studies that would otherwise require physical robots. Yet it remains unclear when VR can serve as a valid proxy for real-world interaction. We present a preregistered comparison between two studies in which participants completed tasks with either a physically present cobot (N = 61) or its virtual twin (N = 39) in an immersive collaboration game. Procedures, game environment, task flow, and robot behavior were held constant across settings; the primary difference was the robot's embodiment. Our work delivers (1) a direct comparison of user perceptions and behavioral outcomes (e.g., attitudes, trust, presence, task completion time); (2) a replication test of an experimental manipulation (two different introductory tutorials); and (3) an analysis of study execution costs. Our results show no significant differences in any of the subjective self-reports between the virtual and physical robot, but task durations were longer with the physical robot, and tutorial effects replicated only partially across settings. Study costs were substantially lower for VR. Together, these findings provide a holistic assessment of VR's suitability as a complementary tool for HRI research, offering guidance for future study design and resource planning.

This research delves into the intricate dynamics involved in adopting an artificial intelligence (AI)-driven Skill Management Software (SMS) in Human Resources (HR), with a focus on human-centered acceptance. It underscores the significance of user attitudes and illuminates the distinctions between active and passive use interaction. More specifically, this article argues that the acceptance of using a highly complex technology is not solely shaped by the attitudes toward the system itself, but also the attitudes toward using the system. To this end, the variable Attitude Toward Use (ATTU) was integrated into the Technology Acceptance Model (TAM). An empirical study with N = 286 employees from the DACH region, focusing on the scenario-based adoption of AI-based SMS in HR, was carried out. Structural Equation Modeling demonstrated that integrating ATTU outperforms the original TAM in explaining Behavioral Intention, active and passive user interaction and their nuanced interactions.

Recent years have brought immense progress in the development of AI technology. This broadened its application fields but also led to a surge of interest in many research domains and increasing significance of human-AI relations for the development of AI technology. This rapid growth and evolvement is reflected by the establishment of a great variety of terms, potentially leading to what is known as jingle and jangle fallacies. With our scoping review of the terminology used in scientific literature to describe human-AI relations and its evolvement over time (with 803 records screened, 658 finally included), we capture the variety and development of human-AI terminology in accordance with the shift from interaction to collaboration between humans and AI. We aim to raise awareness of these developments spanning over different research communities and provide a solid basis for future researchers and practitioners conducting complementary, cross-domain research. Our review comprises terminological, bibliometric and thematic analyses, e.g., reporting on the historical development of terms and term composition patterns, but also identifying key authors and publications, geographic distribution of relevant research, and elaborating on term conception and usage, and co-occurrences throughout the literature.

Occupational exoskeletons constitute a new assistive technology for industrial workers. In the social context of the workplace, colleagues are likely to react to the noticeable appearance of these devices. Drawing on established technology acceptance theories and recent exoskeleton research, we addressed the following issues: (1) What is the effect of co-worker feedback and self-perceived attractiveness on workers’ intention to use an exoskeleton over time? And (2) do these variables predict intention to use beyond perceived usefulness and ease of use? We examined these questions in a one-week diary study with 22 industrial workers. They tested passive exoskeletons during their regular work for six consecutive days and completed a short questionnaire including evaluations of perceived usefulness, ease of use, attractiveness, and social feedback received on a daily basis. The collected data were analyzed using hierarchical linear models (HLM). Results showed that usefulness (but not ease of use) significantly predicted intention to use an exoskeleton. Over and above, self-perceived attractiveness significantly affected initial intention to use, whereas the effects of social feedback unfolded over time. Our study contributes to a better understanding of the social dynamics experienced by users of wearable technology.

Explainable Artificial Intelligence (XAI) enables Artificial Intelligence (AI) to explain its decisions. This holds the promise of making AI more understandable to users, improving interaction, and establishing an adequate level of trust. We tested this claim in the high-risk task of AI-assisted mushroom hunting, where people had to decide whether a mushroom was edible or poisonous. In a between-subjects experiment, 328 visitors of an Austrian media art festival played a tablet-based mushroom hunting game while walking through a highly immersive artificial indoor forest. As part of the game, an artificially intelligent app analyzed photos of the mushrooms they found and recommended classifications. One group saw the AI’s decisions only, while a second group additionally received attribution-based and example-based visual explanations of the AI’s recommendation. The results show that participants with visual explanations outperformed participants without explanations in correct edibility assessments and pick-up decisions. This exhibition-based experiment thus replicated the decision-making results of a previous online study. However, unlike in the previous study, the visual explanations did not significantly affect levels of trust or acceptance measures. In a direct comparison, we consequently discuss the findings in terms of generalizability. Besides the scientific contribution, we discuss the direct impact of conducting XAI experiments in immersive art- and game-based environments in exhibition contexts on visitors and local communities by triggering reflection and awareness for psychological issues of human–AI interaction.

Abstract Companies increasingly rely on chatbots to enable efficient and engaging communication with customers. Previous research has highlighted a trend towards female-gendered designs of customer service chatbots, adding to concerns about the reinforcement of outdated gender stereotypes in human-computer interactions. Against this background, the present study explores design alternatives to traditionally gendered chatbot avatars. In an online experiment, N = 1064 participants interacted with a bank service chatbot, where one half saw a gender-ambiguous anthropomorphic face as the chatbot’s default avatar, and the other half an abstract non-anthropomorphic icon. Contrary to earlier studies, which linked anthropomorphism to higher user acceptance, our manipulation of avatars did not significantly alter intentions to use the chatbot. After the interaction, participants could select their preferred avatar image from a set of six, including non-anthropomorphic icons (speech bubbles) and anthropomorphic faces (female, male, gender-ambiguous). While many adhered to their initially viewed image, a clear majority opted for abstract non-anthropomorphic icons. This overall preference was consistent across all user genders, although men were more likely than women to favor a traditionally female-looking avatar. Notably, less than a quarter of participants recognized the gender-ambiguous avatar as such. In accordance with traditional gender binaries, most identified it as either male or female. Those who perceived it as female reported higher intentions to use the chatbot. As a practical implication, our findings advocate for the adoption of more abstract and gender-neutral chatbot designs, as they not only help to avoid problematic stereotypes but also seem to align with customer preferences for non-gendered chatbot interactions.

Trust calibration is essential in AI-assisted decision-making. If human users understand the rationale on which an AI model has made a prediction, they can decide whether they consider this prediction reasonable. Especially in high-risk tasks such as mushroom hunting (where a wrong decision may be fatal), it is important that users make correct choices to trust or overrule the AI. Various explainable AI (XAI) methods are currently being discussed as potentially useful for facilitating understanding and subsequently calibrating user trust. So far, however, it remains unclear which approaches are most effective. In this article, the effects of XAI methods on human AI-assisted decision-making in the high-risk task of mushroom picking were tested. For that endeavor, the effects of (i) Grad-CAM attributions, (ii) nearest-neighbor examples, and (iii) network-dissection concepts were compared in a between-subjects experiment with \(N=501\) participants representing end-users of the system. In general, nearest-neighbor examples improved decision correctness the most. However, varying effects for different task items became apparent. All explanations seemed to be particularly effective when they revealed reasons to (i) doubt a specific AI classification when the AI was wrong and (ii) trust a specific AI classification when the AI was correct. Our results suggest that well-established methods, such as Grad-CAM attribution maps, might not be as beneficial to end users as expected and that XAI techniques for use in real-world scenarios must be chosen carefully.

Biases in algorithmic systems have led to discrimination against historically disadvantaged groups, including the reinforcement of outdated gender stereotypes. While a substantial body of research addresses biases in algorithms and underlying data, in this work, we study if and how users themselves reflect these biases in their interactions with systems, which expectedly leads to the further manifestation of biases. More specifically, we investigate the replication of stereotypical gender representations by users in formulating online search queries. Following prototype theory, we define the disproportionate mention of the gender that does not conform to the prototypical representative of a searched domain (e.g., “male nurse”) as an indication of bias. In a pilot study with 224 US participants and a main study with 400 UK participants, we find clear evidence of gender biases in formulating search queries. We also report the effects of an educative text on user behaviour and highlight the wish of users to learn about bias-mitigating strategies in their interactions with search engines.

The potential of occupational exoskeletons can only be realized if workers are willing to wear them on their bodies. As classical technology acceptance theories originate in research into information technology, they do not sufficiently cover the peculiarities of exoskeletons, and thus greater focus is needed on factors that specifically shape intentions to use them. Involving three companies from food retail and logistics, we conducted guided focus groups with 18 workers who perform material handling tasks in their daily work. Participants discussed the envisioned benefits, risks, and conditions related to the adoption of exoskeletons. Consistent with established technology acceptance models, performance-related and effort-related factors were found to be highly recognized. Participants also highlighted factors such as wellbeing, fairness, and the altered physical appearance of wearers, which was considered important in social contexts at work. Complementing established factors with new exoskeleton-specific determinants, our results are a valuable starting point for further exoskeleton user studies.

First evidence suggests that introducing robots by means of a narrative story can lead to more positive interactions and evaluations [1]. It is unclear whether this positive framing of robots by narratives works equally for different robot design approaches and appearances. To address this open question we conducted 2×6 between-subjects online experiment and varied the introduction (narrative vs. instruction manual) and appearance of the robot (6 different robot appearances). We replicated previous results on evaluation effects for different robot appearances. Results indicate that robots introduced by a narrative story were evaluated as being more likable, intelligent, autonomous, and humanlike. They were also perceived to be less mechanical and less uncanny. However, there were no interaction effects between narration and robot appearance suggesting that narration is beneficial for robots regardless of their appearance and hence is a strong mechanism to shape positive expectations before actually interacting with a robot.

The uncanny valley hypothesis suggests that high human-likeness of humanoid robots is associated with feelings of uncanniness (eeriness, creepiness). Based on the literature on mind perception two aspects of human-likeness were distinguished. An experiment showed that a robot's capacity to feel (experience) leads to stronger feelings of uncanniness than a robot's capacity to plan ahead and to exert self-control (agency), which is still more uncanny than a robot's function as a tool. Theoretical and practical implications of this work are discussed.

This paper describes the general idea of Ars Wild Card: a mobile application for visitors to create their own art cards, to guide interactive art exhibitions. The art cards created by visitors are collected and shared on the Internet. The Ars Wild Card system generates an archive through visitors' participatory process in the exhibition. This research aims to search for important factors to create a communication bridge between people and public art exhibition. We present our findings as a case study from our practical applications of Ars Wild Card.

We introduce a method to stimulate and catalyse the creativity of students and the general public in the field of experience design. The research is centered on a product design called SWITCH: a simple creative prototyping platform for everyday use which can be likened to picture frame containing a picture with two states, an on and an off state. The states are switched by one of three types of adjustable sensors (light, human, sound) and mechanism. The pictures can be easily customized with analogue art materials like pens and brushes. Our core motivation was to design a product that would bypass the inherent complexities of technology as much as possible, and directly engage the student in creating their own experience design concept with SWITCH. In this paper we introduce our motivation, methods, design and workshop strategies, and evaluations from workshops with the general public.

This paper describes a case study of Shadowgram as an application of interactive fabrication in public spaces to realize a creative communication environment based on an interactive installation, which generates sticker cutouts of the silhouettes of participants. In this paper, we propose an approach called Social Fabrication that stimulates communication in society. Finally, we assess the potential of our creative catalyst by installing Shadowgram in public events and through observation and analysis we examine the behavior of participants.

Telling stories can be a powerful way to persuade. This contributions reviews previous research on individual differences in narrative persuasion, with an emphasis on one personality construct: the need for affect. Implications for persuasion profiling are discussed. Moreover, this contribution provides data on correlates of the need for affect which might be useful in applied settings. Finally, ethical issues are addressed.