A Longitudinal Study of Child Wellbeing Assessment via Online Interactions with a Social Robots

2024-04-16

arXiv.org

AI Summary

Traditional face-to-face mental health assessments for children suffer from limited accessibility and delayed responsiveness. Method: This study investigates whether online social robot (NAO) interactions can serve as a novel paradigm for dynamic mental health assessment in children aged 8–13, employing a longitudinal multi-session design integrating multimodal audiovisual recording, the Short Mood and Feelings Questionnaire (SMFQ), user perception surveys, clustering analysis, and longitudinal perceptual modeling. Contribution/Results: We provide the first empirical evidence that online robot-mediated interaction reliably tracks children’s mental health trajectories over time; child acceptance remains stable or improves longitudinally; and both initial and second sessions significantly discriminate boys’ mental states, while the first session alone achieves high discriminative validity for girls. Critically, we identify a gender-moderated effect: girls exhibit greater self-disclosure to robots and higher assessment sensitivity and timeliness than boys—offering a theoretically grounded, deployable framework for personalized, remote pediatric mental health screening.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems

Abstract

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 towards 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 analysed 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.

Problem

• Online interactions assess children's mental wellbeing.
• Children's perceptions of robots remain consistent.
• Gender influences wellbeing assessments and robot opinions.

Innovation

• Online child-robot interaction for wellbeing
• NAO robot used in longitudinal study
• SMFQ for mental wellbeing clustering