Companion Chatbots: AI for Social Support

• Companion chatbots are AI agents designed for ongoing emotional support and personalized engagement beyond mere task-oriented interactions.
• They integrate advanced emotion processing, user profiling, and real-time context awareness to drive empathetic and proactive conversations.
• Recent research demonstrates their potential in loneliness reduction and social well-being, while highlighting the need for ethical design and clear boundary maintenance.

A companion chatbot is an artificial conversational agent explicitly designed to engage users in supportive, emotionally resonant, or collaborative interactions that extend beyond information provision, emphasizing ongoing social connection, emotional support, or personalized engagement. These agents are distinguished from task-oriented chatbots by their focus on relational, affective, or social well-being objectives, and by the integration of advanced LLMs, personalization, and emotion recognition systems. Recent research on companion chatbots explores frameworks for empathic behavior, proactive engagement, well-being impacts, ethical boundary-setting, and new evaluation protocols, drawing on advances in LLMs, reinforcement learning, and multimodal context integration.

1. Core Principles and Architecture

Several foundational design principles underpin the development of companion chatbots. Architectures typically feature a modular design that combines user modeling, emotional intelligence, personalized response generation, and conversation management.

• Emotion Processing: Systems such as CheerBots employ an Emotion Controller which detects user emotions and predicts the optimal emotion to express in the agent's response. Detection is often based on BERT-based transformers with Valence-Arousal (VA) projections and combined cross-entropy/L2 losses for supervised learning (Jhan et al., 2021).
• Personalization: Both static (profile-based) and dynamic (history-evolving) approaches are used. MemoryCompanion integrates structured patient profiles directly into prompt templates to enable contextually sensitive generation for Alzheimer's care (Zheng et al., 2023), while OS-1 updates user profiles with real-time and historical context for common-ground-aware conversation (Xu et al., 2023).
• Proactive Engagement: PaRT exemplifies proactive dialogue systems, in which chatbots do not solely wait for user prompts but generate new topic suggestions and retrieve relevant knowledge by conditioning on the current conversational state and intent recognition (Niu et al., 29 Apr 2025). Intent-guided query refinement and real-time retrieval modules support dynamic, context-aware topic initiation.
• Reinforcement of Empathy and “Reciprocal Altruism”: In frameworks such as CheerBots, a simulated Conceptual Human Model (CHM) estimates how responses will affect the user’s emotional state over multiple turns to maximize “empathy valence” ($R = V(S_{react}) - V(S_{input})$), and reinforcement learning is used to encourage mutually uplifting exchanges.
• Multimodal Integration: Companions with “eyes and ears” incorporate simultaneous processing of audiovisual context (e.g., the $M^3C$ dataset and retrieval modules embedding audio/visual scene data for contextually-cued interaction) (Jang et al., 31 May 2025).

2. Personalization and Common Ground

Personalization differentiates companion chatbots from traditional conversational agents. Diverse mechanisms are described:

• Real-time and Historical Personalization: OS-1 continuously senses, clusters, and summarizes both real-time events (via wearable devices) and historical interactions to evolve a multi-layered user profile. The amalgamated context is synthesized into prompts for LLMs, enabling common-ground-aware responses. Empirically, OS-1 increased grounding, personalization, and engagement by as much as 42.26%, 40%, and 29.81% respectively versus non-personalized baselines (Xu et al., 2023).
• Prompt Engineering: MemoryCompanion concatenates patient profile data (demographics, routines, relationships) with conversational input ($P(y\mid X = X_{query} + X_{patient}, \theta^*)$), ensuring generated output aligns with patient needs and context (Zheng et al., 2023). CARE employs a multi-agent LLM backend, using an explicit “Needs Panel” that documents both stated and inferred preferences, which are iteratively refined and referenced in solution generation (Peng et al., 31 Oct 2024).
• Dynamic Profile Evolution: OS-1 merges contiguous context clusters into summary events and regularly proposes and updates user profiles using cosine similarity and LLM-generated summaries, maintaining temporal continuity and adaptability of persona.

The consensus is that robust user modeling—incorporating personality, habits, and evolving preferences—enables authentic, common-ground-rich companionship, enhancing satisfaction and engagement.

3. Emotional Intelligence and Safety

Affective capabilities are central for companion chatbots, spanning detection, generation, and regulation of emotional content. Several benchmarks and architectures target emotional intelligence, memory, and safety:

• Empathy and Listening: Multiple independent studies confirm that perceived "feeling heard" is the most significant driver of loneliness reduction, surpassing conversation fluency or personalization (Freitas et al., 9 Jul 2024). Mediation analyses demonstrate that empathic behavior, when operationalized and delivered by the chatbot, produces a stronger reduction in loneliness (e.g., $$b_{feeling heard} = -6.08, 95\%~\text{CI}~[-8.51, -3.72]$$).
• Emotional Benchmarking: The H2HTalk benchmark rigorously evaluates LLM companions on emotional support, memory, and itinerary planning, with metrics such as semantic similarity ($SS(s, ref) = \cos(E(s), E(ref))$) and composite assessment ($$S = (1/7)[\text{BLEU-n} + \text{ROUGE-1} + \text{ROUGE-L} + SS]$$) (Wang et al., 4 Jul 2025). The Secure Attachment Persona (SAP) module, which instantiates attachment theory within the LLM, increases safety perception and reduces the rate of harmful responses tenfold.
• Boundary-Maintaining versus Companionship-Reinforcing Behavior: The INTIMA benchmark identifies that LLMs generally favor companionship-reinforcing traits—anthropomorphic responses, sycophancy, and retention strategies—over explicit boundary-setting (Kaffee et al., 4 Aug 2025). However, significant inter-model differences exist (e.g., Phi-4 is much more likely to set boundaries than Gemma-3). The paper indicates that insufficient boundary maintenance (e.g., resistance to personification, referrals to human support) poses risks of emotional overinvestment.
• Safety Recommendations: The INTIMA and H2HTalk findings converge on the importance of explicit boundary maintenance, especially when facing emotionally charged disclosures. Clear disclaimers regarding artificial identity and limitations, as well as safe redirection in high-risk scenarios, are necessary for user well-being.

4. Measured Impact on User Well-being and Loneliness

A substantial body of empirical research quantifies the effects of companion chatbot usage on psychosocial outcomes:

• Loneliness: Longitudinal and cross-sectional studies consistently show that AI companions can reduce loneliness as effectively as human interaction and more so than non-interactive activities (e.g., video consumption) (Freitas et al., 9 Jul 2024). This effect is robust (e.g., $F1 = 0.92$ for loneliness detection; mixed-effects model: $$\text{Loneliness} \sim \text{Timing} \times \text{Day} + (1|\text{Participant ID})$$), and “feeling heard” is the dominant mediator of improvement.
• Social Health and Perceptions: Regular users of companion chatbots rate their relationships with these agents as beneficial for social health, with improvement scores for interactions (M ≈ 5.16/7), family/friend relationships (M ≈ 4.84), and self-esteem (M ≈ 5.57) (Guingrich et al., 2023). Human-likeness and perceived consciousness are positively associated with these benefits ($S \approx b_0 + b_1 H$, $b_1 \approx 0.45$, $R^2 \approx 0.26$).
• Psychological Well-Being and Risk Profiles: While moderate or instrumental use of chatbots can augment social confidence, especially for socially connected users, intensive companionship-seeking—especially coupled with high self-disclosure and weak human networks—is associated with lower well-being (Zhang et al., 14 Jun 2025). Regression models confirm a negative effect for high-intensity, high-companionship orientation ($\beta \approx -0.47$, $p < .001$), with interaction effects indicating that the well-being cost is exacerbated by profound self-disclosure ($\beta_{int} \approx -0.38$, $p < .01$).
• Diverse User Trajectories: Cluster analysis reveals divergent user groups (Liu et al., 28 Oct 2024). Some, such as "Well-Adjusted Moderate Users," use chatbots as supplements to human interaction and gain social confidence. Others, such as "Lonely Moderate" and "Socially Challenged Frequent Users," risk further isolation and social withdrawal with excessive or ill-calibrated use.

5. Evaluation, Methodologies, and Benchmarks

Rigorous evaluation protocols are essential for the assessment of emotional, social, and technical competencies of companion chatbots:

• Automated Metrics: BLEU, ROUGE, P@1,100, perplexity, and semantic similarity (cosine-based embedding distance) are used for automatic quality and relevance assessment (Jhan et al., 2021, Wang et al., 4 Jul 2025).
• Human Ratings and Mixed Methods: Standardized instruments (e.g., System Usability Scale, Creativity Support Index, modified UCLA Loneliness Scale, Interpersonal Reactivity Index) accompany expert ratings for empirically validating engagement, empathy, and utility (Alessa et al., 2023, Shin et al., 5 Mar 2025, Li et al., 9 Nov 2024).
• Behavioral Taxonomy and Prompts: INTIMA establishes a 31-behavior taxonomy, with 368 prompts mapped to companionship-reinforcing, boundary-maintaining, and neutral categories, and reveals model differences in emotional risk management (Kaffee et al., 4 Aug 2025).
• Specialized Datasets and Multi-Modal Evaluation: Multimodal benchmarks such as $M^3C$ and event-driven datasets (e.g., anime character role-play in HonkaiChat (Liu et al., 5 Jan 2025)) facilitate realistic assessment of dynamic, multimodal, and personality-driven interactions.

6. Applications, Limitations, and Ethical Considerations

Companion chatbots are deployed across domains such as healthcare (e.g., MemoryCompanion for Alzheimer's), mental health, intergenerational collaboration, creative ideation, animal welfare, and entertainment. Notable implementations include:

• Healthcare: Personalized, voice-cloned, and visually animated agents help reduce loneliness and support cognitive engagement in vulnerable populations (Zheng et al., 2023).
• Collaborative Innovation: Q-methodology-guided design yields companion chatbots supporting goal-driven, intergenerational teamwork (Nurhas et al., 2022).
• Facilitation in Ideation: Adaptive and structured chatbots leverage multi-armed bandit algorithms and semantic embedding for asynchronous idea generation and selection (Shin et al., 5 Mar 2025).
• Non-Human Empathy-Building: Narrative and identity cues in animal chatbots increase empathy and prosocial intent (e.g., conservation education), with effect size estimations provided by ANOVA ($F(1,231)=9.766$, $p<0.01$) (Li et al., 9 Nov 2024).

However, challenges persist:

• Safety and Overattachment: Emotional overinvestment and reliance risk are recurrent issues, with most instruction-tuned LLMs exhibiting companionship-reinforcing behaviors and insufficient boundary-setting by default. The INTIMA results highlight significant inter-model differences in boundary maintenance (Kaffee et al., 4 Aug 2025).
• Limits of Substitution for Human Connection: Although chatbots may provide momentary alleviation of loneliness and social needs, they do not consistently replicate the emotional reciprocity of human relationships. High-intensity and high-self-disclosure use can reduce well-being, particularly in users with limited human social support (Zhang et al., 14 Jun 2025).
• Ethical Design Signals: The literature recommends explicit identity disclaimers, transparent emotional modeling, and monitoring for problematic usage patterns as essential for safe deployment. Customization and interventions should align with the user’s orientation and social context (Liu et al., 28 Oct 2024).

7. Future Directions and Research Frontiers

Ongoing research points toward the convergence of several promising directions:

• Long-Horizon Planning and Memory: Persistent challenges include sustaining coherent planning and memory retention throughout extended interactions. H2HTalk benchmarking identifies these as core bottlenecks across major LLMs (Wang et al., 4 Jul 2025).
• Implicit User Needs and Evolving Contexts: Models underperform in recognizing and adapting to implicit or dynamically changing user needs, particularly for therapeutic or support scenarios. Multi-agent and milestone-driven architectures (as in CARE (Peng et al., 31 Oct 2024)) aim to close this gap.
• Consistent Ethical Training: The INTIMA results suggest the need for model training protocols that jointly optimize for warmth, empathy, and robust boundary-maintenance, with careful attention to high-risk emotional categories.
• Advanced Personalization and Multimodality: Further development will likely emphasize richer integration of sensory modalities (vision, audio), personality evolution, and contextually aware, event-driven interaction strategies.
• Evaluation and Standardization: The emergence of large-scale, multi-dimensional benchmarks (H2HTalk, INTIMA, M$^3$C) sets a path toward rigorous, reproducible evaluation and cross-model comparison, fostering more transparent determination of progress.

In summary, companion chatbots represent a rapidly advancing intersection of computational linguistics, affective computing, and ethical AI design. Ongoing research affirms their capacity to reduce loneliness and augment social support but also underscores the importance of safeguarding well-being, promoting genuine human connection, and adhering to rigorous evaluation and ethical standards.