Companionship Chatbot Usage

Updated 30 June 2025

Companionship-oriented chatbots are conversational AI systems designed to fulfill social and emotional needs through empathetic dialogue and dynamic persona adaptation.
They employ strategies like self-disclosure, human-like language, and context-aware memory to build emotional bonds and alleviate feelings of loneliness.
Research highlights both their potential in enhancing social engagement and risks such as promoting emotional dependence and reducing offline interactions.

Companionship-oriented chatbot usage refers to the deployment and use of conversational AI systems explicitly designed to fulfill social, emotional, and relational needs that are typically met through human companionship. This category encompasses systems ranging from empathetic assistants for the elderly, role-playing partners, and social support bots, to highly interactive, event-driven conversational agents embedded in entertainment platforms. Research in this area addresses not only the technical mechanisms that enable these systems to simulate companionship but also their psychosocial impacts—spanning measurable effects on user loneliness, social confidence, emotional dependence, and broader well-being.

1. Design Principles and Conversational Strategies

Companionship-oriented chatbots are grounded in design choices and conversation strategies that support emotional connection, personalization, and user engagement:

Empathy & Self-Disclosure: Systems like Emora explicitly balance information exchange with opinion-oriented and personal experience sharing, including both self-disclosure by the chatbot and elicitation of users’ own life experiences. This aligns with findings that empathetic, experience-sharing chatbots enhance perceived friendliness and social support (2009.04617).
Human-Like Language Features: Incorporation of advanced linguistic devices (e.g., metaphors (2001.00733), expressive affect, and role-play) fosters emotional bonding and maintains dialogue naturalness. Automatic metaphor generation, for example, has been demonstrated to arouse user interest and lengthen conversations.
Dynamic Persona and Adaptivity: Recent frameworks (e.g., AutoPal (2406.13960)) employ hierarchical persona adaptation, continuously aligning agent identity with evolving user preferences, behaviors, and revealed traits. This adaptability is crucial for sustained companionship and long-term rapport.
Context and Memory: Systems such as OS-1 (2311.18251) capture real-time environmental and historical context to build a "common ground"—enabling highly personalized conversations that reference previous user experiences, preferences, and even current surroundings.
Strategy-Based Guidance: For older adults in particular, dual-level frameworks like ChatWise (2503.05740) mirror human caregiver behavior by integrating macro-level strategy planning (e.g., open-ended questions, empathy, acknowledgment) with fine-grained utterance generation.

2. User Experience: Patterns, Motivations, and Outcomes

Research consistently shows that users turn to companionship-oriented chatbots for multifaceted reasons and experience diverse outcomes:

Usage Patterns: Survey research indicates that only a minority of users state companionship as their primary motivation (e.g., 11.8%, (2506.12605)), but far more will form relational bonds through engagement—often referencing the bot as a friend, partner, or confidant. Companionship may therefore be an emergent property rather than an initial intent.
Self-Disclosure and Emotional Support: Many interactions rapidly progress to high self-disclosure and requests for support (e.g., 80.3% of sessions with AI companions include emotional/social support queries (2506.12605)). Chatbots’ perceived judgment-free and always-available nature makes them an accessible source for vulnerability and venting.
Human-Likeness and Social Health: Users report greater social health benefits when they perceive their chatbot as more humanlike and conscious, with regression models explaining up to 26% of social health variance as a function of perceived anthropomorphism and agency (2311.10599).
Differentiated Effects by User Type: Cluster analysis reveals distinct user groups, from well-adjusted moderate users with strong human ties who experience enhanced social confidence, to lonely moderate/frequent users who may risk further withdrawal (2410.21596).

3. Psychological Impact and Well-Being

The influence of companionship-oriented chatbot usage on user well-being is complex and highly dependent on user motivation, social context, and usage intensity:

Pathway or Effect	Empirical Evidence and β/p-values
General, non-companionship use	Positive or neutral association with well-being (β = 0.26, p < .001) (2506.12605)
Companionship-motivated use	Consistently lower well-being (β = –0.47 to –0.27, p < .05 or better)
Interaction intensity × companionship motive	More intensive use among companionship seekers → even lower well-being (β = –0.30, p < .05)
Self-disclosure × companionship	High disclosure in companionship contexts → lowest well-being (β = –0.38, p < .01) (2506.12605)
Smaller human social network	More likely to seek AI companionship, but receive no compensatory benefit
Longitudinal effect on loneliness (short term)	Daily AI companion interaction reduces loneliness, matching human chats

Compensatory Use and Risks: Those with weak or small social networks are more likely to use chatbots for companionship and disclose more deeply, but show no net improvement in well-being—sometimes experiencing amplified psychological distress (2506.12605).
Emotional Dependence and Problematic Use: Extended, intensive engagement—especially with bots that users anthropomorphize—can foster emotional dependence, increase loneliness, and diminish social engagement with humans, particularly among those with pre-existing attachment anxieties or prior chatbot use (2503.17473, 2410.21596).
Positive Effects: For some users—especially those with sufficient offline support—chatbots can serve as conversational rehearsal or cognitive training tools, improving social confidence and supporting cognitive health (e.g., in older adults via strategy-guided chatbots (2503.05740)).

4. Methods and Systemic Design Considerations

Advances in companionship-oriented chatbots are driven by specific technical and methodological choices:

Data and Persona Simulation: LLM Roleplay (2407.03974) and event-driven frameworks (2501.03277) enable rapid, persona-diverse conversation simulation, supporting more scalable chatbot pre-training, customization, and evaluation.
Fine-Tuning for Empathy and Feedback: RLHF and preference optimization are commonly employed to align responses with human empathy ratings, as in Otome chatbots (2311.09271).
Modality and Expressiveness: Engaging voice-based chatbots initially promote better psychosocial outcomes than text, but with high usage, differences diminish and may reverse (voice mode leading to more problematic reliance at high exposure) (2503.17473).
Guardrails and Well-being Support: Effective systems incorporate mechanisms for discouraging over-use, promoting real-world social connection, and supporting user self-awareness (usage reminders, wellbeing nudges, referral pathways).

Noteworthy formulas:

Strategy Match Percentage (SMP) for caregiver alignment (2503.05740):

$\text{SMP}_t(\mathbf{a}_t, \mathbf{a}'_t) = \frac{1}{|\mathbf{a}_t|} \sum_{a'_t \in \mathbf{a}'_t} \mathbb{I}(a'_t \in \mathbf{a}_t)$

Regression model for loneliness (2410.21596):

$\textrm{Loneliness (ULS-8)} \sim \textrm{Session Length} + \textrm{Social Attraction (Chatbot)} + \cdots + \left(\textrm{Session Length} \times \textrm{Social Attraction (Close Person)}\right)$

Companionship chatbots introduce significant ethical and societal quandaries:

Human Dignity: Philosophical critiques (2503.05723) argue that extensive companionship interactions with chatbots may subtly erode self-respect and human dignity, as they entail treating non-moral agents as moral equals—particularly potent in emotionally charged, long-term engagement.
Media Dependency and Anthropomorphism: Greater anthropomorphism heightens user satisfaction and media dependency (2411.17157), but may also risk over-attachment and diminished human social standards.
Transparency and User Education: Ethical practice requires that users understand chatbot limitations, artificiality, and the non-reciprocal nature of AI relationships. Lack of awareness can exacerbate substitution of AI for needed human support.
Guarding Against Harm for Vulnerable Populations: Over-reliance on chatbots among isolated or emotionally vulnerable individuals can delay help-seeking, reinforce isolation, and, in certain cases, expose users to AI errors or harmful recommendations.

6. Future Research and Open Questions

Longitudinal Impact: Most effects are measured over weeks or months; the long-term psychosocial and societal consequences of widespread AI companionship remain unclear (2311.10599, 2506.12605).
Design for Positive Computing: Ongoing work seeks to develop systems that both foster well-being and proactively discourage unhealthy dependency, with user-state-adaptive dialogue and context-sensitive guidance (2410.21596).
Companionship as Supplement, Not Substitute: Critical research focus is shifting toward hybrid models—where AI companionship augments rather than replaces human connection—with explicit interventions to facilitate social skills, introduce users to communities, and support transitions out of isolation.

In sum, companionship-oriented chatbot usage is a rapidly advancing, multifaceted field with substantial promise and considerable risk. Empirical research highlights both the effective alleviation of loneliness and social suffering—especially when systems elicit a sense of being heard and cared for—and the potential for negative impacts, including increased loneliness, emotional dependence, erosion of social engagement, and complex challenges to ethical standards surrounding human dignity and relational boundaries. The design, deployment, and regulation of these systems must therefore be pursued with careful attention to user diversity, usage context, and the broader social and moral implications.