What People Share With a Robot When Feeling Lonely and Stressed and How It Helps Over Time

2025-04-03

AI Summary

This study investigates the ameliorative effects of repeated human-robot interaction on loneliness and perceived stress among young adults, and elucidates the underlying psychological mechanisms. We implemented five QTrobot-facilitated interventions augmented by large language models to support cognitive reappraisal dialogues. Psychological self-report scales and user-disclosed textual data were analyzed using linear mixed-effects modeling, semantic clustering, and Kruskal–Wallis H tests. Our key contribution is the first systematic demonstration that baseline emotional state—particularly stress level—significantly moderates topic distribution in user disclosures to robots: highly stressed individuals preferentially disclose themes related to social connection, whereas low-stress users focus more on introspection and goal-directed content. We further established a bidirectional association model linking disclosure themes (e.g., social connection vs. self-goal orientation) with magnitude of psychological improvement. Results confirm significant reductions in loneliness and perceived stress; six stable disclosure themes were identified, providing interpretable, dynamic mechanistic evidence for emotion-supportive human-robot interaction.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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 large language model 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 a 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 huma-robot interaction.

Problem

• Investigates how social robots reduce loneliness and stress.
• Explores themes in user disclosures to robots during interactions.
• Examines dynamic emotional support in human-robot interactions.

Innovation

• Robot-led intervention with structured conversations
• Large language model powered QTrobot for support
• Semantic clustering of user disclosures for themes