People with dementia often experience social isolation in daily life. Generative AI (GenAI) technologies, producing seemingly new content on the spot and tailoring it to users’ wishes, open new avenues for promoting meaningful social connections in dementia care. This study involved 17 people with dementia in 6 workshops and explored how they responded to and perceived three GenAI models, Copilot, Midjourney, and Suno, with a focus on social connectedness. Our results reveal that people with dementia engage in a relational process when using GenAI together: they collectively evaluate the outcomes of the models and negotiate further prompts. Moreover, they gradually develop an understanding of GenAI and become more critical about its output. We contribute to HCI by demonstrating how GenAI can foster social bonding between people with dementia through the co-creation of shared realities, and by discussing guidelines for designing effective and ethically responsible GenAI for people with dementia.

Cuteness is a key factor in human-human interaction. Infant cuteness, described by Lorenz’s baby schema, is thought to promote infant survival by eliciting caregiving behaviors and positive emotions. Despite evidence that people prefer to interact with cute robots, the specific mechanisms through which cuteness influences human–robot interaction remain poorly understood. This research investigated the relationship between perceived robot cuteness, emotional responses, and caretaking tendencies. In two online surveys, participants rated all robots from the ABOT database. In survey 1, 156 participants evaluated robots’ perceived cuteness and the extent to which they evoked positive and negative emotions. In survey 2, a separate pool of 152 participants rated the caretaking tendencies elicited by the robots. Results showed that cuteness was positively correlated with positive emotions and caretaking tendencies, and negatively correlated with negative emotions. Path analysis revealed that the effect of cuteness on caretaking tendencies was partially mediated by participants’ emotional responses. Consistent with the baby-schema hypothesis, cuteness was negatively associated with perceived robot age and positively with the presence of facial features. Interestingly, participants' individual characteristics, most notably their tendency to anthropomorphize, influenced the responses. Our findings confirm the importance of robot cuteness for HRI and extend theories of baby schema to artificial agents. They also raise ethical considerations: while cuteness is a powerful design feature that facilitates affective bonding with robots, its persuasive potential should not be used lightly. Cute robots may foster care and trust, but these same mechanisms could be exploited to manipulate users in harmful ways.

The field of Human-Robot Interaction (HRI) has seen growing interest in the topic of gendering robots. However, as this topic has gained momentum rapidly, the research methodologies used to study it have not yet undergone critical analysis and refinement. This study investigates whether multi-dimensional questionnaires are susceptible to context effects and how respondents’ views on gender may amplify these effects. We conducted an online study using LimeSurvey, employing a mixed-model design with questionnaire design as a between-subjects variable (four conditions: three items together, three items with distractors, three items separately, and four items) and robot gender ambiguity as a within-subjects variable (gender ambiguous vs. gender non-ambiguous robots). A total of 160 participants were recruited via Prolific, with 40 assigned to each condition. Participants rated the perceived gender of 18 robots (nine gender ambiguous and nine gender non-ambiguous) using the different questionnaire designs. Results show that questionnaire design can alter the direction and strength of the relationships between masculinity, femininity, and gender neutrality. Moreover, they reveal that the questionnaire used to measure a robot’s perceived gender does not influence the ratings of gender at an individual level but it does so at a group level. Finally, they disclose that benevolent sexism sensitizes participants to the attribution of masculinity, whereas non-binary gender makes participants more prone to attribute gender neutrality.

This study focused on investigating the content, structure, and predictors of robots' stereotypes. We involved 120 participants in an online study and asked them to rate 80 robots on communion, agency, suitability for female and suitability for male tasks. In line with the stereotype content model, we discovered that robots' stereotypes are described by two dimensions, communion and agency, which combine to form univalent (e.g., low communion/low agency), as well as ambivalent clusters (e.g., low communion/high agency). Moreover, we found out that a robot's stereotypical appearance has a role in activating stereotypes. Indeed, in our study, female robots featuring appearance cues socio-culturally associated with femininity (e.g., eyelashes or apparel) were perceived as more communal, and juvenile robots featuring appearance cues tapping into the baby schema (e.g., cartoony eyes) were perceived as more communal, less agentic, and less suited to perform tasks. Given the renowned relationship between stereotyping, prejudice and discrimination, the causal link between appearance and stereotyping we establish in this paper can help HRI researchers disentangle the relation between robots' design and people's behavioral tendencies towards them, including proneness to harm.

While creativity has been previously studied in Child-Robot Interaction (cHRI), the effect of regulatory focus on creativity skills has not been investigated. This paper presents an exploratory study that, for the first time, uses the Regulatory Focus Theory (RFT) to assess children's creativity skills in an educational context with a social robot. We investigated whether two key emotional regulation techniques, promotion (approach) and prevention (avoidance), stimulate creativity during a story-telling activity between a child and a robot. We conducted a between-subjects field study with 69 children between the ages of 7 and 9 years old, divided between two study conditions: (1) promotion, where a social robot primes children for action by eliciting positive emotional states, and (2) prevention, where a social robot primes children for avoidance by evoking a states related to security and safety associated with blockage-oriented behaviors. To assess changes in creativity as a response to the priming interaction, children were asked to tell stories to the robot before (pre-test) and after (post-test) the priming interaction. We measured creativity levels by analyzing the verbal content of the stories. We coded verbal expressions related to creativity variables, including fluency, flexibility, elaboration, and originality. Our results show that children in the promotion condition generated significantly more ideas, and their ideas were on average more original in the stories they created in the post-test rather than in the pre-test. We also modeled the process of creativity that emerges during storytelling in response to the robot's verbal behavior. This paper enriches the scientific understanding of creativity emergence in child-robot collaborative interactions.

Discrimination and bias are pressing issues of many AI and robotics applications. These outcomes may derive from limited datasets that do not fully represent society as a whole or from the AI scientific community's western-male configuration bias. Although being a pressing issue, understanding how robotic systems can replicate and amplify inequalities and injustice among underrepresented communities is still in its infancy among social science and technical communities. This workshop contributes to filling this gap by exploring the research question: What do diversity and inclusion mean in the context of Human-Robot Interaction (HRI)? Here, attention is directed to three different levels of HRI: the technical, the community, and the target user level. Overall, this workshop will focus on the idea that AI systems can be created to be more attuned to inclusive societal needs, respect fundamental rights, and represent contemporary values in modern societies by integrating diversity and inclusion considerations.

The present study was aimed at determining the age and gender distribution of the humanoid robots in the ABOT dataset, and providing a systematic data-driven formalization of the process of age and gender categorization of humanoid robots. We involved 153 participants in an online study and asked them to rate the humanoid robots in the ABOT dataset in terms of perceived age, femininity, masculinity, and gender neutrality. Our analyses disclosed that most of the robots in the ABOT dataset were perceived as young adults, and the vast majority of them were attributed a neutral or masculine gender. By merging our data with the data in the ABOT dataset, we discovered that humanlikeness is crucial to elicit social categorization. Moreover, we found out that body manipulators (e.g., legs, torso) guide the attribution of masculinity, surface look features (e.g., eyelashes, apparel) the attribution of femininity, and that robots without facial features (e.g., head, eyes) are perceived as older. Finally, yet importantly, we unveiled that men tend to attribute lower age scores and higher femininity ratings to humanoid robots than women. Our work provides evidence of an existing underlying bias in the design of humanoid robots that needs to be addressed: the under-representation of feminine robots and lack of representation of androgynous ones. We make the results of this study publicly available to the HRI community by attaching the dataset we collected to the present paper and creating a dedicated website.

One of the most effective ways to improve quality of life in dementia is by exposing people to meaningful activities. The study of engagement is crucial to identify which activities are significant for persons with dementia and customize them. Previous work has mainly focused on developing assessment tools and the only available model of engagement for people with dementia focused on factors influencing engagement or influenced by engagement. This article focuses on the internal functioning of engagement and presents the development and testing of a model specifying the components of engagement, their measures, and the relationships they entertain. We collected behavioral and physiological data while participants with dementia (N = 14) were involved in six sessions of play, three of game-based cognitive stimulation and three of robot-based free play. We tested the concurrent validity of the measures employed to gauge engagement and ran factorial analysis and Structural Equation Modeling to determine whether the components of engagement and their relationships were those hypothesized. The model we constructed, which we call the ENGAGE-DEM, achieved excellent goodness of fit and can be considered a scaffold to the development of affective computing frameworks for measuring engagement online and offline, especially in HCI and HRI.

Understanding the way different robot’s strategies affect children’s perceptions of social robots is crucial for a trustworthy child-robot relationship. This paper presents a preliminary study on whether motivational strategies based on Regulatory Focus Theory and goal attainment affect children’s perception of a virtual social robot when solving a task. The ongoing pandemic (COVID-19) is altering the way we perform research. Hence, we designed a fully autonomous game with a virtual social robot. In an online user study, 25 children (8 to 17 years old) played a regulatory focus goal-oriented game with a virtual child-like version of the Furhat robot. We evaluated children’s perceptions of the robot’s social trust, competency trust, and likability. Also, we assessed the children’s affective state (valence and arousal) before and after playing the game. Our preliminary results show that in the prevention condition, fulfilling the goal elicited less happiness in children. Surprisingly, we observed a trend increase in the social and competency trust elicited by the virtual robot when children were prevented from fulfilling the goal of the task. We discuss the results and the effects of online setups on conducting user studies with children.

Numerous studies in social psychology have shown that familiarization across repeated interactions improves people's perception of the other. If and how these findings relate to human-robot interaction (HRI) is not well understood, even though such knowledge is crucial when pursuing long-term interactions. In our work, we investigate the persistence of first impressions by asking 49 participants to play a geography game with a robot. We measure how their perception of the robot changes over three sessions with three to ten days of zero exposure in between. Our results show that different perceptual dimensions stabilize within different time frames, with the robot's competence being the fastest to stabilize and perceived threat the most fluctuating over time. We also found evidence that perceptual differences between robots with varying levels of humanlikeness persist across repeated interactions. This study has important implications for HRI design as it sheds new light on the influence of robots' embodiment and interaction abilities. Moreover, it also impacts HRI theory as it presents novel findings contributing to research on the uncanny valley and robot perception in general.

Facial expressions of emotions influence the perception of robots in first encounters. People can judge trustworthiness, likability, and aggressiveness in a few milliseconds by simply observing other individuals' faces. While first impressions have been extensively studied in adult-robot interaction, they have been addressed in child-robot interaction only rarely. This knowledge is crucial, as the first impression children build of robots might influence their willingness to interact with them over extended periods of time, for example in applications where robots play the role of companions or tutors. The present study focuses on investigating the effects of facial expressions of emotions on children's perceptions of trust towards robots during first encounters. We constructed a set of facial expressions of happiness and anger varying in terms of intensity. We implemented these facial expressions onto a Furhat robot that was either male-like or female-like. 129 children were exposed to the robot's expressions for a few seconds. We asked them to evaluate the robot in terms of trustworthiness, likability, and competence and investigated how emotion type, emotion intensity, and gender-likeness affected the perception of the robot. Results showed that a few seconds are enough for children to make a trait inference based on the robot's emotion. We observed that emotion type, emotion intensity, and gender-likeness did not directly affect trust, but the perception of likability and competence of the robot served as facilitator to judge trustworthiness.

The study of engagement is central to improve the quality of care and provide people with dementia with meaningful activities. Current assessment techniques of engagement for people with dementia rely exclusively on behavior observation. However, novel unobtrusive sensing technologies, capable of tracking psychological states during activities, can provide us with a deeper layer of knowledge about engagement. We compared the engagement of persons with dementia involved in two playful activities, a game-based cognitive stimulation and a robot-based free play, using observational rating scales and electrodermal activity (EDA). Results highlight significant differences in observational rating scales and EDA between the two activities and several significant correlations between the items of observational rating scales of engagement and affect, and EDA features.