How AI and Human Behaviors Shape Psychosocial Effects of Extended Chatbot Use: A Longitudinal Randomized Controlled Study
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Abstract (arXiv)
As people increasingly seek emotional support and companionship from AI chatbots, understanding how such interactions impact mental well-being becomes critical. We conducted a four-week randomized controlled experiment (n=981, >300k messages) to investigate how interaction modes (text, neutral voice, and engaging voice) and conversation types (open-ended, non-personal, and personal) influence four psychosocial outcomes: loneliness, social interaction with real people, emotional dependence on AI, and problematic AI usage. No significant effects were detected from experimental conditions, despite conversation analyses revealing differences in AI and human behavioral patterns across the conditions. Instead, participants who voluntarily used the chatbot more, regardless of assigned condition, showed consistently worse outcomes. Individuals' characteristics, such as higher trust and social attraction towards the AI chatbot, are associated with higher emotional dependence and problematic use. These findings raise deeper questions about how artificial companions may reshape the ways people seek, sustain, and substitute human connections.
Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). First posted on arXiv 21 Mar 2025.
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- 2026Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration8 May 2026
- 2026How Value Induction Reshapes LLM Behaviour8 May 2026
- 2026Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, User Factors, and System Prompts7 May 2026
- 2026Brief chatbot interactions produce lasting changes in human moral values23 Apr 2026
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- 2026Invisible failures in human-AI interactions16 Mar 2026
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- 2026Understanding Cognition-Induced Risks in Agentic AI Systems15 Aug 2026
- 2026ComBodied Agents: a New Paradigm of Human-Centric Agentic AI11 Aug 2026
- 2026Caring Without Feeling: Affective Dynamics as the Control Layer of Human-AI Agent Collaboration8 May 2026
- 2026Seven simple steps for log analysis in AI systemscited by 6
- 2026Invisible failures in human-AI interactionscited by 3
- 2026Disclosure By Design: Identity Transparency as a Behavioural Property of Conversational AI Modelscited by 3
- 2026Understanding Risk and Dependency in AI Chatbot Use from User Discoursecited by 2
- 2026Unilateral Relationship Revision Power in Human-AI Companion Interactioncited by 1
- 2026Brief chatbot interactions produce lasting changes in human moral valuescited by 1
- 2026RealityTest: How People Probe AI Identity and Whether Models Disclose Itcited by 1
- 2026Optimising for Flourishing: Flourishing Metrics and Return on Flourishing as Success Criteria for Artificial Intelligence and Post-AGI Economic Systemscited by 1
- 2026Structure-agnostic Causal Representation Learningcited by 1
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12 most cited of 18 All 18 (JSON)
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- 2026Structure-agnostic Causal Representation Learning1 Oct 2026
- 2026Safety Nudges: User-Facing Interventions for Real-Time AI Risk Awareness22 Sep 2026
- 2026LLMs as Oracles: Reliance on LLMs for Subjective Personal Questions13 Sep 2026
- 2026Optimising for Flourishing: Flourishing Metrics and Return on Flourishing as Success Criteria for Artificial Intelligence and Post-AGI Economic Systems31 Jul 2026
- 2026Alignment Plausibility: A New Standard for Assuring AI in Healthcare8 Jul 2026
- 2026Stumbling Into AI Emotional Dependence: How Routine AI Interactions Reshape Human Connection2 Jun 2026
- 2026VET: A Framework for Analyzing AI Discourse1 Jun 2026
- 2026RealityTest: How People Probe AI Identity and Whether Models Disclose It29 May 2026
- 2026AI Companions as Hyper Attachment and Caregiving Targets15 May 2026
- 2026How Value Induction Reshapes LLM Behaviour8 May 2026
- 2026Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, User Factors, and System Prompts7 May 2026
12 newest of 18 All 18 (JSON)