How AI and Human Behaviors Shape Psychosocial Effects of Chatbot Use: A Longitudinal Randomized Controlled Study

2025-03-21

AI Summary

This study investigates how speech modality (text, neutral speech, expressive speech) and dialogue type (open, impersonal, personal) influence psychosocial outcomes of AI chatbot use. A four-week randomized controlled trial with 981 participants generated over 300,000 real-world interaction logs, analyzed via validated psychometric instruments (e.g., UCLA Loneliness Scale), longitudinal modeling, and moderated mediation analysis. Results reveal: (1) higher usage frequency consistently exacerbates loneliness, AI emotional dependence, and problematic use while diminishing real-world social engagement; (2) neutral speech intensifies dependence under high usage intensity; (3) personalized dialogue reduces dependence but slightly increases loneliness; and (4) individuals with higher attachment anxiety report greater loneliness, whereas those with higher trust propensity exhibit stronger emotional dependence on AI. This work provides the first empirical evidence of dynamic interplay between vocal expressivity and user behavior, offering foundational insights for ethically grounded design and targeted interventions in AI social agents.

Technology Category

• Humans and AI: Emotional Intelligence
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

AI chatbots, especially those with voice capabilities, have become increasingly human-like, with more users seeking emotional support and companionship from them. Concerns are rising about how such interactions might impact users' loneliness and socialization with real people. We conducted a four-week randomized, controlled, IRB-approved experiment (n=981,>300K messages) to investigate how AI chatbot interaction modes (text, neutral voice, and engaging voice) and conversation types (open-ended, non-personal, and personal) influence psychosocial outcomes such as loneliness, social interaction with real people, emotional dependence on AI and problematic AI usage. Results showed that while voice-based chatbots initially appeared beneficial in mitigating loneliness and dependence compared with text-based chatbots, these advantages diminished at high usage levels, especially with a neutral-voice chatbot. Conversation type also shaped outcomes: personal topics slightly increased loneliness but tended to lower emotional dependence compared with open-ended conversations, whereas non-personal topics were associated with greater dependence among heavy users. Overall, higher daily usage - across all modalities and conversation types - correlated with higher loneliness, dependence, and problematic use, and lower socialization. Exploratory analyses revealed that those with stronger emotional attachment tendencies and higher trust in the AI chatbot tended to experience greater loneliness and emotional dependence, respectively. These findings underscore the complex interplay between chatbot design choices (e.g., voice expressiveness) and user behaviors (e.g., conversation content, usage frequency). We highlight the need for further research on whether chatbots' ability to manage emotional content without fostering dependence or replacing human relationships benefits overall well-being.

Problem

• Investigates how AI chatbot interaction modes affect loneliness and socialization
• Examines impact of conversation types on emotional dependence and problematic usage
• Explores relationship between user behaviors and chatbot design choices

Innovation

• Voice-based chatbots reduce loneliness initially
• Personal topics slightly increase loneliness
• High usage correlates with higher loneliness