Synthetic Emotions vs. Gamification: Exploring Engagement Strategies for Small Social Robots in Different Age Groups

2026-05-26

AI Summary

This study investigates how differentiated interaction strategies sustain engagement in child–adult interactions with social robots, specifically within contexts of emotion regulation and social skill development. Using a tactile social robot platform deployed in naturalistic settings, we compared the effects of synthetic emotional feedback versus gamified point-based rewards on children aged 6–8 and adults aged 20–27. Results indicate that children exhibited a significant preference for emotional feedback, whereas adults demonstrated higher task accuracy (p < 0.05) and sustained engagement under point-based incentives. The findings underscore age as a critical moderator of engagement mechanism efficacy and reveal a notable divergence between subjective preference and objective behavioral performance, highlighting the necessity of validating social robot designs through authentic interactive experiences.

Abstract

Many children experience challenges in emotional regulation and social interaction, which can limit their participation in everyday activities and therapeutic programs. For socially assistive robots to be effective in this context, it is essential that children remain consistently and meaningfully engaged. We explore engagement strategies for a tactile robot designed to support children suffering from anxiety disorders through daily interactions. The robot delivers either synthetic emotional feedback or point rewards to encourage user participation. We evaluated these strategies through two studies: a preference assessment with 16 school children aged 6-8 years, and a behavioral study with 14 university students aged 20-27 years in naturalistic environments. The study with school children indicated a preference for emotional engagement over points-based approaches. The follow up study with university students across a full day of interactions revealed contrasting results: points-based systems produced significantly higher task accuracy (p < 0.05) and sustained performance over time. Findings from different user groups suggest that stated preferences and behavioral outcomes can diverge depending on engagement context, highlighting the importance of validating design assumptions through observed interaction. This work contributes insights into age-related differences in engagement strategy effectiveness in human-robot interaction design.

Problem

• engagement strategies
• social robots
• emotional regulation
• age differences
• human-robot interaction

Innovation

• synthetic emotions
• gamification
• engagement strategies
• age-related differences
• social robots