The identity design of artificial agents carries growing ethical, psychological, and cultural weight, as ubiquitous language models and diverse robotic forms are blended into everyday use. However, structured approaches to designing coherent and interpretable artificial identities remain limited. To address urgent challenges in artificial identity design, including harmful stereotypes and deceptive practices, we introduce the Identity Design (ID) Framework and an accompanying research agenda. Drawing on emerging work on artificial identity in human-robot interaction and taking an interdisciplinary perspective, we propose twelve design principles across three levels: individual (recognisability, behavioural consistency, identity continuity, memory, persistent goals), group (membership signalling, social alignment, role clarity), and societal (benevolence, artificiality, social justice, transparency). The research agenda outlines open questions around the operationalisation and measurement of identity, social dynamics, and ethical considerations for identity design. Together, they lay the groundwork for future research and responsible practice in robotic, virtual, and multi-embodied agents.

Institutional and social barriers in higher education often prevent students with disabilities from effectively accessing support, including lengthy procedures, insufficient information, and high social-emotional demands. This study empirically explores how disabled students perceive robot-based support, comparing two interaction roles, one information based (signposting) and one disclosure based (sounding board), and two embodiment types (physical robot/disembodied voice agent). Participants assessed these systems across five dimensions: perceived understanding, social energy demands, information access/clarity, task difficulty, and data privacy concerns. The main findings of the study reveal that the physical robot was perceived as more understanding than the voice-only agent, with embodiment significantly shaping perceptions of sociability, animacy, and privacy. We also analyse differences between disability types. These results provide critical insights into the potential of social robots to mitigate accessibility barriers in higher education, while highlighting ethical, social and technical challenges.

Self-disclosure and the social sharing of emotions facilitate social relationships and can positively affect people's well-being. Nevertheless, individuals might refrain from engaging in these interpersonal communication behaviours with other people, due to socio-emotional barriers, such as shame and stigma. Social robots, free from these human-centric judgements, could encourage openness and overcome these barriers. Accordingly, this paper reviews the role of self-disclosure and social sharing of emotion in human-robot interactions (HRIs), particularly its implications for emotional well-being and the dynamics of social relationship building between humans and robots. We investigate the transition of self-disclosure dynamics from traditional human-to-human interactions to HRI, revealing the potential of social robots to bridge socio-emotional barriers and provide unique forms of emotional support. This review not only highlights the therapeutic potential of social robots but also raises critical ethical considerations and potential drawbacks of these interactions, emphasising the importance of a balanced approach to integrating robots into emotional support roles. The review underscores a complex but promising frontier at the intersection of technology and emotional well-being, advocating for careful consideration of ethical standards and the intrinsic human need for connection as we advance in the development and application of social robots.

Recent research in affective robots has recognized their potential in supporting human well-being. Due to rapidly developing affective and artificial intelligence technologies, this field of research has undergone explosive expansion and advancement in recent years. In order to develop a deeper understanding of recent advancements, we present a systematic review of the past 10 years of research in affective robotics for wellbeing. In this review, we identify the domains of well-being that have been studied, the methods used to investigate affective robots for well-being, and how these have evolved over time. We also examine the evolution of the multifaceted research topic from three lenses: technical, design, and ethical. Finally, we discuss future opportunities for research based on the gaps we have identified in our review – proposing pathways to take affective robotics from the past and present to the future. The results of our review are of interest to human-robot interaction and affective computing researchers, as well as clinicians and well-being professionals who may wish to examine and incorporate affective robotics in their practices.

Subjective self-disclosure is an important feature of human social interaction. While much has been done in the social and behavioural literature to characterise the features and consequences of subjective self-disclosure, little work has been done thus far to develop computational systems that are able to accurately model it. Even less work has been done that attempts to model specifically how human interactants self-disclose with robotic partners. It is becoming more pressing as we require social robots to work in conjunction with and establish relationships with humans in various social settings. In this paper, our aim is to develop a custom multimodal attention network based on models from the emotion recognition literature, training this model on a large self-collected self-disclosure video corpus, and constructing a new loss function, the scale preserving cross entropy loss, that improves upon both classification and regression versions of this problem. Our results show that the best performing model, trained with our novel loss function, achieves an F1 score of 0.83, an improvement of 0.48 from the best baseline model. This result makes significant headway in the aim of allowing social robots to pick up on an interaction partner’s self-disclosures, an ability that will be essential in social robots with social cognition.

This study presents a novel robot-led approach to assessing children’s mental wellbeing using a Vision Language Model (VLM). Inspired by the Child Apperception Test (CAT), the social robot NAO presented children with pictorial stimuli to elicit their verbal narratives of the images, which were then evaluated by a VLM in accordance with CAT assessment guidelines. The VLM’s assessments were systematically compared to those provided by a trained psychologist. The results reveal that while the VLM demonstrates moderate reliability in identifying cases with no wellbeing concerns, its ability to accurately classify assessments with wellbeing concerns remains limited. Moreover, although the model’s performance was generally consistent when prompted with varying demographic factors such as age and gender, a significantly higher false positive rate was observed for girls, indicating potential sensitivity to gender attribute. These findings highlight both the promise and the challenges of integrating VLMs into robot-led assessments of children’s wellbeing.

As social robots and other artificial agents become more conversationally capable, it is important to understand whether the content and meaning of self-disclosure towards these agents changes depending on the agent’s embodiment. In this study, we analysed conversational data from three controlled experiments in which participants self-disclosed to a human, a humanoid social robot, and a disembodied conversational agent. Using sentence embeddings and clustering, we identified themes in participants’ disclosures, which were then labelled and explained by a large language model. We subsequently assessed whether these themes and the underlying semantic structure of the disclosures varied by agent embodiment. Our findings reveal strong consistency: thematic distributions did not significantly differ across embodiments, and semantic similarity analyses showed that disclosures were expressed in highly comparable ways. These results suggest that while embodiment may influence human behaviour in human–robot and human–agent interactions, people tend to maintain a consistent thematic focus and semantic structure in their disclosures, whether speaking to humans or artificial interlocutors.

Loneliness and stress are prevalent among young adults and are linked to significant psychological and health-related consequences. Social robots may offer a promising avenue for emotional support, especially when considering the ongoing advancements in conversational AI. This study investigates how repeated interactions with a social robot influence feelings of loneliness and perceived stress, and how such feelings are reflected in the themes of user disclosures towards the robot. Participants engaged in a five-session robot-led intervention, where a LLM-powered QTrobot facilitated structured conversations designed to support cognitive reappraisal. Results from linear mixed-effects models show significant reductions in both loneliness and perceived stress over time. Additionally, semantic clustering of 560 user disclosures towards the robot revealed six distinct conversational themes. Results from Kruskal-Wallis H-test demonstrate that participants reporting higher loneliness and stress, more frequently engaged in socially focused disclosures, such as friendship and connection, whereas lower distress was associated with introspective and goal-oriented themes (e.g., academic ambitions). By exploring both how the intervention affects well-being, as well as how well-being shapes the content of robot-directed conversations, we aim to capture the dynamic nature of emotional support in human–robot interaction.

Socially Assistive Robots are studied in different child–robot interaction settings. However, logistical constraints limit accessibility, particularly affecting timely support for mental wellbeing. In this work, we have investigated whether online interactions with a robot can be used for the assessment of mental wellbeing in children. The children (N = 40, 20 girls and 20 boys; 8–13 years) interacted with the Nao robot (30–45 mins) over three sessions, at least a week apart. Audio-visual recordings were collected throughout the sessions that concluded with the children answering user perception questionnaires pertaining to their anxiety toward the robot, and the robot’s abilities. We divided the participants into three wellbeing clusters (low, med, and high tertiles) using their responses to the Short Moods and Feelings Questionnaire (SMFQ) and further analyzed how their wellbeing and their perceptions of the robot changed over the wellbeing tertiles, across sessions and across participants’ gender. Our primary findings suggest that (I) online-mediated interactions with robots can be effective in assessing children’s mental wellbeing over time, and (II) children’s overall perception of the robot either improved or remained consistent across time. Supplementary exploratory analyses have also revealed that the gender of the children affected their wellbeing assessments with interactions effectively distinguishing between varying levels of wellbeing for both boys and girls for the first session and only for boys during the second session. The analyses have also revealed that girls have a higher opinion of the robot as a confidante as compared with boys. Findings from this work affirm the potential of using online-mediated interactions with robots for the assessment of the mental wellbeing of children.

The recent developments in Large Language Models (LLMs) mark a significant moment in the research and development of social interactions with artificial agents. These agents are widely deployed in a variety of settings, with potential impact on users. However, the study of social interactions with agents powered by LLMs is still emerging, limited by access to the technology and to data, the absence of standardised interfaces, and challenges to establishing controlled experimental setups using the currently available platforms. To address these gaps, we developed LEXI, LLMs Experimentation Interface, an open-source tool for deploying artificial agents powered by LLMs in social interaction behavioural experiments. Using a graphical interface, LEXI allows researchers to build agents and deploy them in experimental setups along with forms for collecting self-reported data while collecting interaction logs. The outcomes of usability testing indicate LEXI’s broad utility, high usability, and minimal mental workload requirement, with benefits observed across disciplines. A proof-of-concept study exploring the tool’s efficacy in evaluating social human–agent interactions was conducted, resulting in high-quality data. A comparison of empathetic versus neutral agents indicated that people perceive empathetic agents as more social, and write longer and more positive messages towards them.

The interdisciplinary nature of Child-Robot Interaction (CRI) fosters incorporating measures and methodologies from many established domains. However, when employing CRI approaches to sensitive avenues of health and wellbeing, caution is critical in adapting metrics to retain their safety standards and ensure accurate utilisation. We conducted a secondary analysis to previous empirical work, investigating the reliability and construct validity of established psychological questionnaires such as the Short Moods and Feelings Questionnaire (SMFQ) and three subscales (generalised anxiety, panic and low mood) of the Revised Child Anxiety and Depression Scale (RCADS) within a CRI setting for the assessment of mental wellbeing. Through confirmatory principal component analysis, we have observed that these measures are reliable and valid in the context of CRI. Furthermore, our analysis revealed that scales communicated by a robot demonstrated a better fit than when self-reported, underscoring the efficiency and effectiveness of robot-mediated psychological assessments in these settings. Nevertheless, we have also observed variations in item contributions to the main factor, suggesting potential areas of examination and revision (e.g., relating to physiological changes, inactivity and cognitive demands) when used in CRI. Our findings highlight the importance of verifying the reliability and validity of standardised metrics and assessment tools when employed in CRI settings, thus, aiming to avoid any misinterpretations and misrepresentations.

Self-disclosing to others can benefit emotional well-being, but socio-emotional barriers can limit people’s ability to do so. Self-disclosing towards social robots can help overcome these obstacles as robots lack judgment and can establish rapport. To further understand the influence of affective factors on people’s self-disclosure to social robots, this study examined the relationship between self-disclosure behaviour towards a social robot and people’s emotional states and their perception of the robot’s responses as comforting (i.e., being emphatic). The study included 1160 units of observation collected from 39 participants who conversed with the social robot Pepper (SoftBank Robotics) twice a week for 5 weeks (10 sessions in total), answering three personal questions in each session. Results show that perceiving the robot’s responses as more comforting was positively related to self-disclosure behaviour (in terms of disclosure duration in seconds, and disclosure length in number of words), and negative emotional states, such as lower mood, and higher feelings of loneliness and stress, were associated with higher rates of self-disclosure towards the robot. Additionally, higher rates of introversion significantly predicted higher rates of self-disclosure towards the robot. The study reveals the meaningful influence of affective states on how people behave when talking to social robots, especially when experiencing negative emotions. These findings may have implications for designing and developing social robots in therapeutic contexts.

Conversational recommender agents are artificially intelligent recommender systems that provide users with individually-tailored recommendations by targeting individual needs and communicating in a flowing dialogue. These are widely available online, communicating with users while demonstrating human-like (anthropomorphic) social cues. Nevertheless, little is known about the effect of their anthropomorphic cues on users’ resistance to the system and recommendations. Accordingly, this study examined the extent to which conversational recommender agents’ anthropomorphic cues and the type of recommendations provided (user-initiated and system-initiated) influenced users’ perceptions of control, trustworthiness, and the risk of using the platform. The study assessed how these perceptions, in turn, influence users’ adherence to the recommendations. An online experiment was conducted among users with conversational recommender agents and web recommender platforms that provided user-initiated or system-initiated restaurant recommendations. The results entail that user-initiated recommendations, compared to system-initiated, are less likely to affect users’ resistance to the system and are more likely to affect their adherence to the recommendations provided. Furthermore, the study’s findings suggest that these effects are amplified for conversational recommender agents, demonstrating anthropomorphic cues, in contrast to traditional systems as web recommender platforms.

Since interactions with social robots are novel and exciting for many people, one particular concern in this specific area of human-robot interaction (HRI) is the extent to which human users will experience the interactions positively over time, when the robot’s novelty is particularly salient. In the current paper, we investigated users’ experience in long-term HRIs; how users perceive the ongoing interactions and the robot’s ability to sustain it over time. Therefore, here we examine the effect of the repeated measures (10 testing sessions) and the discussion theme (Covid-19 related vs general) on the way participants experienced the interaction quality with a social robot and perceived the robot’s communication competency over time. We found that despite individual differences between the participants, over time participants found the interactions with Pepper to be of higher quality and that Pepper’s communication skills got better. Nevertheless, our results also stressed that the discussion theme has no meaningful nor significant effect on the way people perceive Pepper and the interaction.

Following the success of the first edition of Robo-Identity, the second edition will provide an opportunity to expand the discussion about artificial identity. This year, we are focusing on emotions that are expressed through speech and voice. Synthetic voices of robots can resemble and are becoming indistinguishable from expressive human voices. This can be an opportunity and a constraint in expressing emotional speech that can (falsely) convey a human-like identity that can mislead people, leading to ethical issues. How should we envision an agent's artificial identity? In what ways should we have robots that maintain a machine-like stance, e.g., through robotic speech, and should emotional expressions that are increasingly human-like be seen as design opportunities? These are not mutually exclusive concerns. As this discussion needs to be conducted in a multidisciplinary manner, we welcome perspectives on challenges and opportunities from variety of fields. For this year's edition, the special theme will be “speech, emotion and artificial identity”.

One limitation of social robots has been the ability of the models they operate on to infer meaningful social information about people's subjective perceptions, specifically from non-invasive behavioral cues. Accordingly, our paper aims to demonstrate how different deep learning architectures trained on data from human-robot, human-human, and human-agent interactions can help artificial agents to extract meaning, in terms of people's subjective perceptions, in speech-based interactions. Here we focus on identifying people's perceptions of their subjective self-disclosure (i.e., to what extent one perceives to be sharing personal information with an agent). We approached this problem in a data-first manner, prioritizing high quality data over complex model architectures. In this context, we aimed to examine the extent to which relatively simple deep neural networks could extract non-lexical features related to this kind of subjective self perception. We show that five standard neural network architectures and one novel architecture, which we call a Hopfield Convolutional Neural Network, are all able to extract meaningful features from speech data relating to subjective self-disclosure.

When interacting with embodied agents, users often rely on a variety of cues from the agent’s embodiment to form perceptions of animacy and intelligence, including its appearance and behaviour. Due to chatbots’ disembodiment, users’ perceptions of a chatbot’s animacy and intelligence are mostly dependent on the textual properties of the dialogue. The current study aims to investigate the mediating role of perceptions of chatbot’s intelligence and animacy on users’ perceptions of the chatbot’s anthropomorphism. An online experiment was conducted with a chatbot and a web platform. Both systems asked users three basic questions for providing a restaurant recommendation. By communicating the same content via different modalities of communication (i.e., flowing dialogue and traditional web interface); this study compares the differences in these perceptions between chatbots to traditional web platforms. The results of a mediation analysis entail that the chatbot was perceived as more animate than the web platform, and accordingly, it was perceived as more anthropomorphic than the web platform as users’ perceptions of animacy fully mediated this effect. Also, there is no evidence for differences in users’ perceptions of intelligence between the chatbot and the web platform.

Conversational recommender systems provide users with individually tailored recommendations in a flowing dialogue. These require users to disclose information proactively or reactively for receiving personalized recommendations, which can trigger users' resistance to the platform and to the recommendations. Accordingly, this study examined the extent to which user-initiated and system-initiated recommendations provided by a conversational recommender system influenced users' perceptions of it. The results of an online experiment entail that when recommendations are system-initiated, as compared to user-initiated, users perceive to be in less control and perceive the system as riskier. Furthermore, the results stress that systems that provide user-initiated or system-initiated recommendations do not differ in users' perceptions of anthropomorphism.