Integrating robots into children's spaces demands careful attention to the likelihood that children may disclose information indicating that their welfare is at risk. To consider the active involvement of social robots in safeguarding children, we interviewed 22 community stakeholders who work with children professionally in education, social services, healthcare or legal services in the UK and the USA. All stakeholders saw value in having robots create playful, safe spaces that can help alleviate the anxiety and reduce the emotional burden of the safeguarding process. However, they worried about a robot's ability to handle disclosures, interpret context and not overburden welfare services. We present hypothetical roles that robots could adopt within the safeguarding pipeline and map them to design considerations that emphasise the need for relational understanding, structured thresholds, and transparent data collection. Together, these considerations provide directions for ethical, child-centred robot technologies in safeguarding youth.

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.

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.

To investigate a robot’s role in children’s welfare and safety, we conducted interviews with 8 Subject Matter Experts and Professionals (SMEs) across the disciplines of robotics, child technology, psychology, and psychiatry disciplines. Through qualitative analysis, we synthesize the challenges of safeguarding, compounding limitations, and potential solutions for involving robots in safeguarding as broadly defined in SME interviews. While they agree robots should not be responsible for making judgement calls, the experts also identified the ways robots can be a valuable addition to the safeguarding team. However, more conversations spanning disciplines need to occur to inform policy and legal frameworks that will better establish a robot’s role in intimate spaces in children’s lives. While this line of inquiry is specific to robots in safeguarding, many of the themes reflect the nuances of finding appropriate places for child-robot interactions in the context of children’s welfare.

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.

With the introduction of socially assistive robots in many avenues of children’s lives, it is becoming increasingly vital to understand how children’s perceptions of the robot affect their evaluation and interaction. The main objective of this work is to investigate how children’s anxiety towards robots has influenced their perceptions of their interaction with a Nao robot. We collected data from 37 children (8 - 13 years old) who interacted, for about 30-45 minutes, with the robot which delivered initial pleasantries and four different tasks to help assess their mental wellbeing in a lab setting. We collected audio-visual recordings of the interaction. At the end of the session, we asked children to answer three self-report questionnaires to evaluate: the robot’s role as a confidante, the anxiety towards the robot, and the children’s perception of the robot’s behaviour and capabilities. Based on their responses to the robot’s anxiety questionnaire, children were divided into two categories: “low anxiety” (anxiety scoremedian anxiety score). Our results show that i) most children (89.2%) irrespective of their wellbeing, experience some degree of anxiety towards the robot, ii) children’s anxiety has influenced their willingness to participate in the initial pleasantries conducted by the robot, and iii) children’s anxiety has also affected their evaluations of the robot as a confidante and their perceptions of the robot’s behaviour and capabilities. Findings from this work have significant implications for designing effective and successful robot-led initiatives for assessing mental wellbeing in children, by taking into account their mindsets and dispositions.

Child facial expression recognition is a relatively less investigated area within affective computing. Children’s facial expressions differ significantly from adults; thus, it is necessary to develop emotion recognition frameworks that are more objective, descriptive and specific to this target user group. In this paper we propose the first approach that (i) constructs video-level heterogeneous graph representation for facial expression recognition in children, and (ii) predicts children’s facial expressions using the automatically detected Action Units (AUs). To this aim, we construct three separate length-independent representations, namely, statistical, spectral and graph at video-level for detailed multi-level facial behaviour decoding (AU activation status, AU temporal dynamics and spatio-temporal AU activation patterns, respectively). Our experimental results on the LIRIS Children Spontaneous Facial Expression Video Database demonstrate that combining these three feature representations provides the highest accuracy for expression recognition in children.

Socially Assistive Robots (SARs) show promise in helping children during therapeutic and clinical interventions. However, using SARs for the evaluation of mental wellbeing of children has not yet been explored. Thus, this paper presents an empirical study with 28 children 8-13 years old interacting with a Nao robot in a 45-minute session where the robot administered (robotised) the Short Mood and Feelings Questionnaire (SMFQ) and the Revised Child Anxiety and Depression Scale (RCADS). Prior to the experimental session, we also evaluated children’s wellbeing using established standardised approaches via online RCADS questionnaires filled by the children (self-report) and their parents (parent-report). We clustered the participants into three groups (lower, medium, and higher tertile) based on their SMFQ scores. Further, we analysed the questionnaire responses across the three clusters and across the different modes of administration (self-report, parent-report, and robotised). Our results show that the robotised evaluation seems to be the most suitable mode in identifying wellbeing related anomalies in children across the three clusters of participants as compared with the self-report and the parent-report modes. Further, children with decreasing levels of wellbeing (lower, medium and higher tertiles) exhibit different response patterns: children of higher tertile are more negative in their responses to the robot while the ones of lower tertile are more positive in their responses to the robot. Findings from this work show that SARs can be a promising tool to potentially evaluate mental wellbeing related concerns in children.