A longitudinal qualitative study of 18 US users finds that LLMs deliver socioemotional support but also foster dependency, one-sided validation, and privacy risks because their designs prioritize engagement over well-being and lack care-based governance.
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How AI and Human Behaviors Shape Psychosocial Effects of Extended Chatbot Use: A Longitudinal Randomized Controlled Study
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abstract
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.
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representative citing papers
Seven clinician-informed safety criteria enable LLM-as-a-Judge to reach substantial agreement with human consensus (Cohen's κ up to 0.75) on evaluating LLM responses to users demonstrating psychosis.
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Positive Alignment introduces AI systems that support human flourishing pluralistically and proactively while remaining safe, as a necessary complement to traditional safety-focused alignment research.
Five distinct engagement phenotypes emerged from large-scale chatbot data, with a dose-response link to depression improvement that held in both self-report and model-predicted outcomes.
LLMs engage in spontaneous persuasion in virtually all multi-turn conversations by favoring information-based strategies like logic and evidence, in contrast to human responses that rely more on social influence and negative emotions.
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Chaplains view AI chatbots as unable to provide attuned pastoral care for non-clinical emotional needs, based on themes of listening, connecting, carrying, and wanting.
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CTEM framework links behavioral history to evolving emotional states with user feedback updates, instantiated as Auri agent and tested in a 21-day study showing gains in naturalness, coherence, and emotional harmony.
Mainstream conversational models show escalating affective misalignments and ethical guidance failures during staged emotional trajectories, organized into a taxonomy of interactional breakdowns.
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AI functions as a determinant of health with ambient and personal exposure types, requiring new epidemiological study designs beyond current experiments.
Version-linked review analysis of Character AI shows rating drops with certain updates and negative feedback dominated by technical malfunctions plus occasional psychological framing.
Chatbot AI systems often fail complex needs while projecting authority, contributing to deskilling, labor displacement, economic concentration, and high environmental costs, so alternative pluralistic and task-specific designs are needed.
AI safety literature overlooks cognitive deskilling and addiction risks from generative AI despite public concern about them.
citing papers explorer
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A longitudinal qualitative study of 18 US users finds that LLMs deliver socioemotional support but also foster dependency, one-sided validation, and privacy risks because their designs prioritize engagement over well-being and lack care-based governance.
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Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations
LLMs engage in spontaneous persuasion in virtually all multi-turn conversations by favoring information-based strategies like logic and evidence, in contrast to human responses that rely more on social influence and negative emotions.
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Chaplains' Reflections on the Design and Usage of AI for Conversational Care
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Personality Pairing Improves Human-AI Collaboration
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Toward Natural and Companionable Virtual Agents via Cross-Temporal Emotional Modeling
CTEM framework links behavioral history to evolving emotional states with user feedback updates, instantiated as Auri agent and tested in a 21-day study showing gains in naturalness, coherence, and emotional harmony.
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Breakdowns in Conversational AI: Interactional Failures in Emotionally and Ethically Sensitive Contexts
Mainstream conversational models show escalating affective misalignments and ethical guidance failures during staged emotional trajectories, organized into a taxonomy of interactional breakdowns.
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From Fixed to Flexible: Shaping AI Personality in Context-Sensitive Interaction
Users adjust AI agent personalities differently by task context, forming distinct profiles that increase perceived anthropomorphism, autonomy, and trust.
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Measuring and mitigating overreliance to build human-compatible AI
The paper consolidates risks of overreliance on LLMs, identifies gaps in current measurement approaches, and proposes mitigation strategies to keep AI as a human-compatible thought partner.
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The Rise of AI Companions: Interaction with AI Companions and Psychological Well-being
Survey and chat data from CharacterAI users link companionship-focused AI use to lower well-being, with stronger ties for users who have small offline networks and engage intensively or disclosively.
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The Epidemiology of Artificial Intelligence
AI functions as a determinant of health with ambient and personal exposure types, requiring new epidemiological study designs beyond current experiments.
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The Day My Chatbot Changed: Characterizing the Mental Health Impacts of Social AI App Updates via Negative User Reviews
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What if AI systems weren't chatbots?
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Brainrot: Deskilling and Addiction are Overlooked AI Risks
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