Listening to emotional accounts typically induces an empathic response in a recipient, with the degree of empathy determined by situational features, particularly emotional valence and, presumably, the agency of the narrator in the event. To investigate the role of valence and agency further, I present the results of a study in which 52 participants rated their empathic and emotional reactions to AI-generated avatars narrating a brief account of a positive or negative life event, differing in the degree to which the narrator was responsible for the outcome of the event. In addition, participants completed a recognition task. Consistent with previous studies, emotional valence modulated the ratings, but also agency influenced empathic responding. Interestingly, lower agency elicited the strongest empathic response but resulted in the lowest recognition rate in the subsequent memory task. More generally, this study illustrates the application of AI-generated stimuli in research on social interaction and interpersonal behaviour.
The study investigates the impact of different game genres on gaming behavior among students from 4th grade to 12th grade. Previous research often focused on isolated aspects of gaming without considering genre-specific influences. This study aims to fill that gap by examining how genres affect challenge and achievement, social interaction, flow, gaming time, and spending. Using ANOVA and Sidak post-hoc tests, the analysis included a sample size of 49,569 respondents. The findings indicate significant differences across genres: Multiplayer Online Battle Arena (MOBA) games foster higher levels of challenge, achievement, social interaction, and flow compared to other genres. Players of MOBA games also exhibit the highest engagement in terms of gaming time and financial investment. Role-Playing Games (RPG) and First-Person Shooter (FPS) games also show notable engagement levels but to a lesser extent. These results suggest that game genre significantly shapes gaming behaviors and experiences, highlighting the importance of genre-specific design and marketing strategies. For scholars, this study emphasizes the need for more granular research into game genres. For practitioners, including game developers and service providers, the findings offer insights into enhancing player engagement and tailoring game features to specific genres.
Elderly care is a key area of interest in social robotics, yet research on the practical applications and acceptance of such robots in real-world group settings remains limited. In this study, we use an ethorobotic approach to explore the potential of a socially assistive robot (SAR) to elicit interactive behaviours, such as orienting, approaching, withdrawing, touching, talking, and smiling, from elderly residents and other non-resident individuals in a nursing home. The elderly residents demonstrated high levels of acceptance and curiosity toward the robot, whereas most non-resident staff tended to ignore it. Residents showed significantly more interest in the robot than non-residents, and both groups maintained a consistent level of interaction over the 11-day observation period. These exploratory observations suggest that it is indeed possible to design socially interactive robots capable of sustaining long-term engagement with elderly individuals.
As social robots enter public spaces, there remains a gap in understanding how people imagine and evaluate their roles as social actors. This study explores the social dynamics of human-robot interaction (HRI) using the Method of Empathy-Based Stories (MEBS). Participants imagined encounters with a robot at a youth center, producing 158 stories that reveal culturally situated reasoning grounded in everyday social expectations. Positive interactions were marked by the robot's ability to "pass as social", where adherence to interactional norms enabled smooth exchanges despite technological limitations. In contrast, negative stories exposed failures such as unresponsiveness, rudeness, or lack of social competence, leading to distrust or disappointment. These findings underscore the situated nature of HRI, suggesting that successful interaction depends less on a robot's "real internal states" and more on its capacity to align with normative expectations of context and practice.
As Generative Artificial Intelligence (GAI) becomes increasingly integrated into daily life, understanding how users develop trust in these systems while navigating privacy concerns is critical. This study examines how perceived anthropomorphism, privacy concerns, and dependency influence trust in GAI, drawing on Privacy Calculus Theory (PCT) and Media Dependency Theory (MDT). The findings reveal that users trust GAI more when they perceive it as human-like, but privacy concerns reduce trust, creating a trust-privacy paradox. However, GAI dependency moderates these relationships, strengthening the positive effect of anthropomorphism on trust while weakening the negative impact of privacy concerns. Additionally, privacy concerns partially mediate the relationship between anthropomorphism and trust, suggesting that users who perceive AI as human-like worry less about privacy risks. By integrating PCT and MDT, this study offers a comprehensive framework to understand how trust in AI evolves, not just through rational cost-benefit evaluations (PCT) but also through behavioral adaptation based on dependency (MDT). These insights have practical implications for AI developers and policymakers, emphasizing the need for human-centered AI design, privacy safeguards, and ethical guidelines to foster sustained trust in AI-driven interactions while addressing user concerns.
This research aims to investigate the potential implementation of robotic classes and robotics in modern music education. The research methodology is based on experimental approaches, the questionnaire method, static data analysis techniques, and the descriptive method of analysing the results obtained. A total of 245 students from Chinese music universities and conservatories participated in the survey. The research findings revealed that 88% of the students were willing to transition entirely to robotics-based education. Among them, 90% exhibited familiarity with contemporary technologies and their specific applications in music education. Over half of the students (80%) had grown accustomed to the robotic learning format and saw no need for attending classes with human instructors (85%). Furthermore, 81% of the participants in the study expressed confidence in the potential for complete automation of music education in the future. The majority of the students (94%) welcome the use of robotics in music education.
This study investigates whether the opinions of robotic agents are more likely to influence human decision-making when the robots are perceived as value-aware (i.e., when they display an understanding of human principles). We designed an experiment in which participants interacted with two Furhat robots - one programmed to be Value-Aware and the other Non-Value-Aware - during a labeling task for images representing human values. Results indicate that participants distinguished the Value-Aware robot from the Non-Value-Aware one. Although their explicit choices did not indicate a clear preference for one robot over the other, participants directed their gaze more toward the Value-Aware robot. Additionally, the Value-Aware robot was perceived as more loyal, suggesting that value awareness in a social robot may enhance its perceived commitment to the group. Finally, when both robots disagreed with the participant, conformity occurred in about one out of four trials, and participants took longer to confirm their responses, suggesting that two robots expressing dissent may introduce hesitation in decision-making. On one hand, this highlights the potential risk that robots, if misused, could manipulate users for unethical purposes. On the other hand, it reinforces the idea that social robots might encourage reflection in ambiguous situations and help users avoid scams.
Managing conversational interactions with groups of people is still an open challenge in human-robot interaction, requiring a multi-modal combination of sensory inputs/outputs and dialogue systems. In this paper, we present the development of an integrated multi-modal system connecting a Large Language Model (LLM) with a social robot's perception and action modules for managing situated multi-party interactions. We describe and discuss the exploratory results of a system-wide performance evaluation via a within-subjects user study in which 27 unique pairs of participants interacted with a social robot under two conditions: a multi-party capable system and a baseline system with only single-party capabilities. Participants interacted with the two systems in a combination of task-based and open-ended scenarios, for a total of 108 interactions with each of the two systems. Our evaluation demonstrated a slight preference for the Multi-Party system and a more balanced interaction overall, and highlights potentials and open challenges in the integration of LLMs capabilities into robotic conversational systems.
Social robotics is a multidisciplinary field focused on designing and implementing robots capable of interacting with humans in social environments. However, group conversations challenge robots in interpreting social signals for effective participation. This study evaluates control policies for moderating multi-party conversation dynamics using a humanoid robot. The system employs a cloud-based framework to calculate speaker dominance as a weighted combination of speaking time and word count, while the Louvain algorithm identifies subgroups among participants. Control policies aim to minimize dominance disparities and subgroup formation, fostering balanced participation and group cohesion. A study with 300 middle school students compared these policies to a baseline in which the robot did not address individuals directly. The results demonstrated that the proposed policies reduced dominance gaps and subgroup formation, promoting more balanced interactions. These findings highlight the potential applicability of the approach across education, healthcare, and entertainment.
Examining sentiment in team communications can provide information about trust among teammates. Natural language processing (NLP) models provide an efficient means of sentiment analysis. However, military teams and other professional teams use language that differs from what NLP models are trained on, leading to potentially inaccurate sentiment analysis. This study investigates the novel application of two advanced NLP models, DistilBERT and GPT-2, for sentiment analysis of expert military teams conducting AI-supported combat missions in a high fidelity simulation environment. Our fine-tuning process resulted in improved sentiment classification accuracy. The sentiment measures also correlated with measures of team trust and trust in the AI systems, providing valuable insight into the relationship between sentiment and trust in human-AI teaming scenarios. The generalized approach we describe may be useful for adapting sentiment analysis and NLP techniques to military teams, and may help measure trust dynamics and team states in human machine integrated teams.
Using AI adequately is necessary for user companies to remain competitive. Studies show that nevertheless many companies are hesitant in this regard. In relation to the assumption that people's ability to act is influenced by a lack of trust, particularly in the context of AI, we conducted a study as part of the TrustKI research project to analyze which factors are relevant to documenting trustworthiness in the context of AI. Our evaluation revealed that users demand holistic transparency; the provision of relevant information on the AI solution and proof of technical expertise is not sufficient to build trust, but there is a demand from users for specific information about the respective company. Based on the generally recognized components, we were able to identify further dimensions to provide the required information even more precisely. Thus, the study allows us to propose a preliminary set of information requirements for AI providers.
As human-agent teaming (HAT) research continues to grow, computational methods for modeling HAT behaviors and measuring HAT effectiveness also continue to develop. One rising method involves the use of human digital twins (HDT) to approximate human behaviors and socio-emotional-cognitive reactions to AI-driven agent team members. To help HDT research effectively model human trust in HATs, we offer two lines of insight. First, through a review of the HAT trust literature, we identify key characteristics and attributes of trust that must be considered in order to properly conceptualize, model, and measure trust. Through this review, we outline the theoretical foundations of trust needed for effective HDTs capable of emulating human trust and offer guidance on where and how extant HAT research should translate into HDT modeling and future research. Second, through causal analyses of archival team communication data from a HAT experiment, we supplement theoretical foundations for modeling trust with data-driven insights to guide the trust-related language HDTs may need to effectively emulate human trust. Finally, we discuss implications of these combined theoretical and empirical insights for future HDT research, highlighting the necessity of ongoing validation against human behaviors and the refinement of computational methods. This paper ultimately aims to advance both the fidelity and applicability of HDTs in modeling nuanced human-agent trust dynamics, fostering more effective and realistic human-agent collaborations.
Generative AI agents (GenAIs) powered by Large-language models (LLMs) have emerged as prominent technological advancements. As these sophisticated systems permeate diverse sectors ranging from business to entertainment, their capability to handle moral queries becomes a focal point of exploration. This study investigates how users perceive Delphi, a GenAI trained to respond to moral queries (Jiang et al., 2025). Participants were instructed to interact with the agent, implemented either as a humanlike robot or a web client, to assess its moral competence and trustworthiness. Both agents received high scores for moral competence and perceived morality, yet fell short by not offering justifications for their moral decisions. Despite being deemed trustworthy, participants were hesitant about relying on such systems in the future. This study offers an initial evaluation of an algorithm with moral competence in an embodied human-like interface, paving the way for the evolution of ethical robot advisors.
The increasing integration of Artificial Intelligence (AI) into human teams necessitates a deeper understanding of how to foster effective collaboration. This study investigates how incorporating emojis, as a representation of emotional intelligence, into AI communication influences human-AI team dynamics. Specifically, the study examined how emojis impact human trust in AI teammates, whether different types of emojis yield varied outcomes, and how emoji use affects the perceived performance of both AI and human teammates. A controlled experiment was conducted with participants who collaborated with a simulated AI teammate on a geographic location identification task. The AI teammate's reliability and the use of emojis were manipulated across different experimental conditions. Results showed that neither the AI teammate's reliability nor the use of emojis significantly influenced participants' explicit trust ratings in the AI teammate. These findings highlight the complex interplay of trust, perception, and emotional cues in HAT collaboration.
Team communication content can provide insights into teammates' coordination processes and perceptions of one another. Using a simulated aircraft reconnaissance team task testbed, we investigate how personifying and objectifying communication content relate to people's trust in and anthropomorphism of machine teammates and to overall team performance. A total of 44 participants were paired and assigned to one of two unique team roles alongside a synthetic pilot agent. Instances of verbal personifications and objectifications that occurred during the task were captured and compared to team performance, as well as questionnaire responses related to participants' trust in, and anthropomorphizing of, the synthetic pilot. Verbal personifications were not correlated with trust and anthropomorphism but converged for the two human roles over time, along with a convergence in trust towards the synthetic agent. Verbal objectifications, on the other hand, were negatively correlated with perceived trustworthiness and anthropomorphism of a teammate. Neither verbal personifications nor objectifications were found to be related to team performance. Our findings suggest that people verbally personify machines to ease communication, and that the same processes that underlie tendencies to verbally personify and objectify machines are related to those that influence trust and anthropomorphism.
The rising social value of pet ownership is influenced by social media and evidence of positive effects on well-being, leading to a rise in dog ownership in younger generations. However, the mental health outcomes of this broader shift, especially in India, have not been studied. The study explored the association between dog owners' relationships, mental health, and satisfaction with life among university students. A cross-sectional correlational design was used with 250 students aged 18-25 who were either dog owners or without pets. The dog owners responded to the Monash Dog Owner Relationship Scale, apart from the Mental Health Continuum and Satisfaction with Life Scale. Results showed a non-significant difference between mental health and satisfaction with life between dog owners and non-pet respondents. A positive relationship could not be established between dog ownership, mental health, and satisfaction with life. The dog's gender and breed influenced the owners' emotional bonding and interactions. Low perceived costs were related to a strong emotional bond with the dog, highlighting the complex nature of the pet ownership experience. Dog ownership's effect on students' well-being is not universal and might depend on various individual, cultural, and contextual factors. Exploration of these human-animal interactions is warranted.
One of the characteristics of dialogue is that interlocutors tend to converge on the same linguistic choices, called alignment. In this paper, we aim to investigate whether structural alignment - the tendency to use the same syntactic structures - has a positive effect on cognitive load and task completion in a task-based conversation. To do so, we engage participants in a collaborative task where they have to interact with another interlocutor (actually a bot) and inform each other about the location of landmarks on a map. In one condition the bot aligns with the participant and in the other it does not. Participants are recorded with an eye tracker during the experiment so that we can evaluate cognitive load and performance in the task. We found that when participants interact with an aligning bot, their cognitive load decreases and task completion is facilitated, but only to a certain degree. The results of the study suggest that alignment is a strategy that can be used in order to facilitate task performance.
This research aims to investigate the potential implementation of robotic classes and robotics in modern music education. The research methodology is based on experimental approaches, the questionnaire method, static data analysis techniques, and the descriptive method of analysing the results obtained. A total of 245 students from Chinese music universities and conservatories participated in the survey. The research findings revealed that 88% of the students were willing to transition entirely to robotics-based education. Among them, 90% exhibited familiarity with contemporary technologies and their specific applications in music education. Over half of the students (80%) had grown accustomed to the robotic learning format and saw no need for attending classes with human instructors (85%). Furthermore, 81% of the participants in the study expressed confidence in the potential for complete automation of music education in the future. The majority of the students (94%) welcome the use of robotics in music education.
Motor interference is an effect of movement deviations resulting from activation of mirror neurons due to a counterpart's movements. This paper presents results from a study investigating the impact of elbow configuration changes of a physical robot arm on human elbow configurations, while performing a linear hand movement task. A within-subjects design is chosen with different elbow configuration change conditions (upwards, downwards, no change) presented to the participants. Results show various types of imitation behavior of elbow movements by the participants. Significant differences of the variability of height differences of optically tracked wrist and elbow positions measured by standard deviations of the time series of height difference progressions are found compared to a baseline condition without elbow movements. These results show, that recently found principles in human-human-interaction do also apply to human-robot interaction, where involuntary arm configuration changes induced by a robotic counterpart may lead to unwanted sensitivity and manipulability variations, which could interfere with given interaction tasks.
