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Do chatbot relationships lose their appeal as novelty wears off?

Explores whether the positive social dynamics observed in one-time chatbot studies persist or fade through repeated interactions. Critical for designing systems intended for sustained engagement over weeks or months.

Synthesis note · 2026-02-22 · sourced from Psychology Chatbots Conversation

Evidence from longitudinal studies with the chatbot Mitsuku shows that social processes related to relationship formation decreased throughout interactions, likely due to a novelty effect wearing off. This is a critical knowledge gap: one-shot interaction studies dominate conversational agent research, and their findings may not hold across multiple interactions.

Chatbots are not only designed for short-term purposes but often for medium- and longer-term interactions. Health coaching, therapeutic support, daily functioning screening — these all require sustained engagement over weeks or months. If the social processes that drive initial engagement decay, the design challenge shifts from "how to make a good first impression" to "how to sustain engagement through the novelty decay."

The implication: researchers and designers who extrapolate from one-shot studies to longitudinal products are making an empirically unsupported leap. The positive findings from single-session experiments — increased self-disclosure, anthropomorphism, trust — may be novelty-dependent rather than stable properties of the interaction.

This creates a design requirement: chatbots intended for repeated use need engagement mechanisms that go beyond initial social impression. Personalization is one approach (since Does chatbot personalization build trust or expose privacy risks?), but it comes with its own dual-edged dynamics.

Personalization as counterforce: A longitudinal study on personalized vs non-personalized conversational agents provides evidence that personalization can counteract novelty decay. Each additional interaction means the agent learns more about the user AND the user expects more from the agent — creating a dynamic tension. Personalization effects on perceived anthropomorphism and trust are positive, but they coexist with increased perceived privacy risks. The CASA framework itself needs updating: "the capabilities of the agents and the overall experience of users with technology have evolved since CASA was first proposed." Agents are now more accessible (smartphones, messaging platforms), more data-rich, and more personalized — meaning the novelty-decay dynamics documented with Mitsuku may operate differently with modern agents that genuinely adapt over time. The question becomes whether personalization creates genuine relationship deepening or merely delays the novelty decay curve.

Inquiring lines that read this note 83

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What mechanisms preserve shared understanding in evolving conversations? What drives appropriate trust calibration in personalized AI systems? How do neighboring agents influence whether others cooperate or collude? How does AI-generated content undermine authentic engagement on social platforms? What design and behavioral factors drive false consciousness attribution to AI? How can AI chatbots provide therapeutic benefit without causing harm? What prevents conversational agents from taking initiative in dialogue? Can local safety checks guarantee system-level behavioral safety? When should work require human-AI partnership versus full automation? Why do people disclose to AI systems despite their artificial nature? What factors drive AI persuasiveness and how can it be mitigated? What determines appropriate intervention timing and manner for AI agents? Should AI communication design follow human conversation norms or develop distinct machine-specific principles? How can conversational agents maintain consistent personas across multi-turn dialogue? How well do AI systems understand human social norms? Does warmth and empathy training systematically degrade model reliability? How do prompting refinements mask underlying biases and model frequency patterns? How can reward models capture diverse human preferences without excluding minority populations? How should agents manage memory granularity to improve long-term performance? What trajectory-level metrics beyond task success best evaluate agent performance? Why do locally safe actions create system-level safety gaps? Can real-time computational alliance measurement improve therapy outcomes?

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Original note title

novelty effects in chatbot relationships decay predictably over repeated interactions — social processes related to relationship formation decrease