Computers in Human Behavior: Artificial Humans最新文献

Occupational exoskeletons are body-worn technologies capable of enhancing a wearer's naturally given strength at work. Despite increasing interest in their physical effects, their implications for user self-perception have been largely overlooked. Addressing common concerns about body-enhancing technologies, our study explored how real-world use of a robotic exoskeleton affects a wearer's mechanistic dehumanization and perceived attractiveness of the self. In a within-subjects laboratory experiment, n = 119 participants performed various practical work tasks (carrying, screwing, riveting) with and without the Ironhand active hand exoskeleton. After each condition, they completed a questionnaire. We expected that in the exoskeleton condition self-perceptions of warmth and attractiveness would be less pronounced and self-perceptions of being competent and machine-like would be more pronounced. Study data supported these hypotheses and showed perceived competence, machine-likeness, and attractiveness to be relevant to technology acceptance. Our findings provide the first evidence that body-enhancement technologies may be associated with tendencies towards self-dehumanization, and underline the multifaceted role of exoskeleton-induced competence gain. By examining user self-perceptions that relate to mechanistic dehumanization and aesthetic appeal, our research highlights the need to better understand psychological impacts of exoskeletons on human wearers.

The AI revolution is shaping societies around the world. People interact daily with a growing number of products and services that feature AI integration. Without doubt rapid developments in AI will bring positive outcomes, but also challenges. In this realm it is important to understand if people trust this omni-use technology, because trust represents an essential prerequisite (to be willing) to use AI products and this in turn likely has an impact on how much AI will be embraced by national economies with consequences for the local work forces. To shed more light on trusting AI, the present work aims to understand how much the variables trust in AI and trust in humans overlap. This is important to understand, because much is already known about trust in humans, and if the concepts overlap, much of our understanding of trust in humans might be transferable to trusting AI. In samples from Singapore (n = 535) and Germany (n = 954) we could observe varying degrees of positive relations between the trust in AI/humans variables. Whereas trust in AI/humans showed a small positive association in Germany, there was a moderate positive association in Singapore. Further, this paper revisits associations between individual differences in the Big Five of Personality and general attitudes towards AI including trust.

The present work shows that trust in humans and trust in AI share only small amounts of variance, but this depends on culture (varying here from about 4 to 11 percent of shared variance). Future research should further investigate such associations but by also considering assessments of trust in specific AI-empowered-products and AI-empowered-services, where things might be different.

The rapid advancement of artificial intelligence (AI) has brought transformative changes to various aspects of human life, leading to an exponential increase in the number of AI users. The broad access and usage of AI enable immense benefits but also give rise to significant challenges. One way for AI users to address these challenges is to develop AI literacy, referring to human proficiency in different subject areas of AI that enable purposeful, efficient, and ethical usage of AI technologies. This study aims to comprehensively understand and structure the research on AI literacy for AI users through a systematic, scoping literature review. Therefore, we synthesize the literature, provide a conceptual framework, and develop a research agenda. Our review paper holistically assesses the fragmented AI literacy research landscape (68 papers) while critically examining its specificity to different user groups and its distinction from other technology literacies, exposing that research efforts are partly not well integrated. We organize our findings in an overarching conceptual framework structured along the learning methods leading to, the components constituting, and the effects stemming from AI literacy. Our research agenda – oriented along the developed conceptual framework – sheds light on the most promising research opportunities to prepare AI users for an AI-powered future of work and society.

Virtual influencers, or fictional CGI-generated social media personas, are gaining popularity. However, research lacks information on how they compare to human influencers in shaping consumer attitudes and purchase intent. This study examines whether perceived homophily and para-social relationships mediate the effect of influencer type on purchase intent and the moderating effect of perceived authenticity. A 2 × 2 between-subjects experiment manipulated influencer type (virtual vs. human) and product type (hedonic vs. utilitarian). Young adult participants viewed an Instagram profile of a lifestyle influencer. Authenticity, perceived homophily, para-social relationship, and purchase intent were measured using established scales. Perceived homophily and para-social relationships mediate the effect of influencer type on purchase intent. A significant interaction showed that perceived authenticity moderated the mediated pathway, such that the indirect effect via para-social relationship and perceived homophily was stronger for human influencers. Maintaining an authentic persona is critical for virtual influencers to sway consumer behaviours, especially for audiences less familiar with social media.

Artificial Intelligence (AI) is present in various modern systems, but it is still subject to acceptance in many fields. Medical diagnosis, autonomous driving cars, recommender systems and robotics are examples of areas in which some humans distrust AI technology, which ultimately leads to low acceptance rates. Conversely, those same applications can have humans who over rely on AI, acting as recommended by the systems with no criticism regarding the risks of a wrong decision. Therefore, there is an optimal balance with respect to trust in AI, achieved by calibration of expectations and capabilities. In this context, the literature about factors influencing trust in AI and its calibration is scattered among research fields, with no objective summaries of the overall evolution of the theme. In order to close this gap, this paper contributes a literature review of the most influential papers on the subject of trust in AI, selected by quantitative methods. It also proposes a Main Path Analysis of the literature, highlighting how the theme has evolved over the years. As results, researchers will find an overview on trust in AI based on the most important papers objectively selected and also tendencies and opportunities for future research.

The reformed Assessment For Learning (AFL) practices the design of activities and evaluation and feedback processes that improve student learning. While Artificial Intelligence (AI) has blossomed as a field in education, less work has been done to examine the studies and challenges reported between AFL and AI. We conduct a review of the literature to examine the state of work on AFL and AI in the education literature. A review of articles in Web of Science, SCOPUS, and Google Scholar yielded 35 studies for review. We share the trends in research design, AFL conceptions, and AI challenges in the reviewed studies. We offer the implications of AFL and AI and considerations for future research.

Synthetic visual art is becoming a commodity due to generative artificial intelligence (AI). The trend of using AI for co-creation will not spare artists’ creative processes, and it is important to understand how the use of generative AI at different stages of the creative process affects both the evaluation of the artist and the result of the human-machine collaboration (i.e., the visual artifact). In three experiments (N = 560), this research explores how the evaluation of artworks is transformed by the revelation that the artist collaborated with AI at different stages of the creative process. The results show that co-created art is less liked and recognized, especially when AI was used in the implementation stage. While co-created art is perceived as more novel, it lacks creative authenticity, which exerts a dominant influence. The results also show that artists’ perceptions suffer from the co-creation process, and that artists who co-create are less admired because they are perceived as less authentic. Two boundary conditions are identified. The negative effect can be mitigated by disclosing the level of artist involvement in co-creation with AI (e.g., by training the algorithm on a curated set of images vs. simply prompting an off-the-shelf AI image generator). In the context of art that is perceived as commercially motivated (e.g., stock images), the effect is also diminished. This research has important implications for the literature on human-AI-collaboration, research on authenticity, and the ongoing policy debate regarding the transparency of algorithmic presence.

Advancements in artificial intelligence (AI) over the last decade demonstrate that machines can exhibit communicative behavior and influence how humans think, feel, and behave. In fact, the recent development of ChatGPT has shown that large language models (LLMs) can be leveraged to generate high-quality communication content at scale and across domains, suggesting that they will be increasingly used in practice. However, many questions remain about how knowing the source of the messages influences recipients' evaluation of and preference for AI-generated messages compared to human-generated messages. This paper investigated this topic in the context of vaping prevention messaging. In Study 1, which was pre-registered, we examined the influence of source disclosure on young adults' evaluation of AI-generated health prevention messages compared to human-generated messages. We found that source disclosure (i.e., labeling the source of a message as AI vs. human) significantly impacted the evaluation of the messages but did not significantly alter message rankings. In a follow-up study (Study 2), we examined how the influence of source disclosure may vary by the adults’ negative attitudes towards AI. We found a significant moderating effect of negative attitudes towards AI on message evaluation, but not for message selection. However, source disclosure decreased the preference for AI-generated messages for those with moderate levels (statistically significant) and high levels (directional) of negative attitudes towards AI. Overall, the results of this series of studies showed a slight bias against AI-generated messages once the source was disclosed, adding to the emerging area of study that lies at the intersection of AI and communication.

Academics have focused their research on the rise of non-human entities, particularly virtual humans. To assess the effectiveness of virtual humans in influencing individual behavior through campaigns, we conducted two separate online experiments involving different participant groups: university students (N = 167) and U.S. adults (N = 320). We compared individuals’ responses to video-type pro-environmental campaigns featuring a virtual or actual human scientist as the central figure who provides testimonials about their individual efforts to prevent misinformation about climate change. The results indicate that an actual human protagonist evoked a stronger sense of identification compared to a virtual human counterpart. Nevertheless, we also observed that virtual humans can evoke empathy for the characters, leading individuals to perceive them as living entities who can have emotions. The insights gleaned from this study have the potential to shape the creation of virtual human content in various domains, including pro-social campaigns and marketing communications.