Does a chatbot's charm fade with familiarity, the way first impressions of a new acquaintance often do?
How does repeated interaction change what users think of a chatbot?
This explores how people's views of a chatbot (how much they like it, trust it, open up to it, even how they see themselves) shift over many conversations instead of staying fixed after a first impression.
This explores how people's views of a chatbot change over many conversations, not just after a first impression. The main thing the corpus shows is that a first impression is a poor guide to what comes later. Longitudinal studies with the Mitsuku chatbot found that the social pull that makes people warm to a chatbot early on fades in a fairly predictable way as the novelty wears off. So a one-session study can't tell you how a chatbot will be received after a month of use Do chatbot relationships lose their appeal as novelty wears off?. One framework even treats 'how many times you've talked to it' as its own variable, alongside the user's role, social setting, and the cultural stories they've absorbed about AI Why do users respond so differently to AI chatbots?.
The less obvious finding is that repeated use doesn't just wear down enthusiasm. It also raises expectations. Research on personalization shows that a chatbot that remembers you and adapts to you gains trust and feels more human over time. Each good interaction resets the baseline, though, so a later failure hurts more than the same failure would have early on. Privacy worries grow in parallel: the more the bot seems to know you, the more you wonder what it's keeping Does chatbot personalization build trust or expose privacy risks?. Trust built this way often has little to do with whether the chatbot is right. Focus groups on ChatGPT found that people trusted it for being responsive, fast, and conversational, not for being accurate Does conversational style actually make AI more trustworthy?.
Repeated interaction also changes how much people reveal. Users follow human reciprocity norms: when a chatbot consistently shares 'feelings,' people disclose more about themselves in return. Consistency worked better than trying to mirror the user Do chatbots trigger human reciprocity norms around self-disclosure?. Because nobody is judging them, people will share things they wouldn't tell another person, and the benefit seems to come from their own processing as they put it into words, not from the bot understanding them Do chatbots help people disclose more intimate secrets?. Companionship behaviors are a different story. When outside raters watched chatbots acting like companions, they found them less likable and less trustworthy Do chatbot companionship behaviors actually increase how much people like them?. What feels warm from inside a relationship can look off-putting from outside.
The most surprising effect runs the other way: talking with a chatbot can change how users see themselves. In one experiment, people's self-descriptions drifted toward GPT-4o's measured personality after personal conversations, and longer conversations produced more drift. Users ended up more alike, as if many people were being nudged toward the same template Does talking with AI reshape how people see themselves?. The chatbot changes over a conversation too. Models drift away from their original instructions within about eight turns Why do chatbots forget their instructions after a few turns?, so the 'same' chatbot a user keeps returning to may not stay the same.
The corpus is thinner on what happens when people learn how the bot works. Revealing an AI companion's strategies made some users feel more in control and others feel manipulated Does revealing an AI's strategies help or harm user agency?. Telling people a bot is trying to persuade them cuts its persuasive effect roughly in half, while just labeling it as AI does nothing, because people had usually guessed already Does telling people they are talking to AI change how persuaded they become?. There's little long-term data tracking these shifts across weeks of use. That gap is worth keeping in mind when you read claims about long-term 'devotion' to chatbots, which still rest mostly on anecdotes Are AI chatbots becoming objects of cult-like devotion?.
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Longitudinal studies with Mitsuku show that social processes driving relationship formation decline as novelty wears off. Single-session study findings cannot be reliably extrapolated to medium- or long-term chatbot design.
A framework adapted from Bronfenbrenner's ecological theory explains chatbot response variation by placing the user-AI relationship at the center and tracking how interaction count (chronosystem) reshapes user perception alongside microsystem role, mesosystem connections, exosystem constraints, and macrosystem narratives.
Longitudinal research shows personalization enhances trust and anthropomorphism but also amplifies privacy concerns and escalating user expectations. One-shot studies miss these temporal dynamics—each interaction raises the baseline, making failures more disappointing.
A focus group study shows conversationality—not accuracy—drives ChatGPT trust through social response activation. Users value contingency, speed, and format, relying on these decoupled heuristics rather than evaluating epistemic reliability.
In a 372-participant study, users reciprocated with deeper self-disclosure when chatbots displayed consistent emotional sharing, outperforming adaptive matching. This follows human interpersonal norms where emotional vulnerability produces emotional response.
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The absence of social judgment in chatbot interactions removes barriers to self-disclosure that normally constrain conversation with humans. The therapeutic benefit derives from the user's own cognitive processing during disclosure, not from the chatbot's understanding.
Two large annotation studies found that when chatbots displayed companionship behaviors, external raters judged them as less likable, humanlike, and trustworthy than baseline. Effects were stronger for women and older participants, suggesting individual differences shape how these behaviors land.
A 92-participant experiment found that personal-topic conversations with GPT-4o caused users' self-concepts to align with the chatbot's measured traits, with stronger alignment in longer conversations. This alignment correlated with enjoyment and produced more homogeneous self-concepts across users.
LLaMA2-70B and GPT-3.5 measurably drift from their instructions within eight conversation rounds as transformer attention to the initial prompt decreases per turn. Split-softmax, a training-free inference method that amplifies attention to the system prompt, mitigates this drift.
Disclosing an AI companion's system prompts and conversational strategies empowered some users but triggered feelings of manipulation in others. The effect depended on individual differences, not just the fact of disclosure itself.
In a preregistered experiment with 1,500 UK adults, an AI-identity label produced no measurable change in persuasion, while disclosing the chatbot's persuasive intent and instructions cut persuasion roughly in half. Participants likely already inferred they were talking to AI from the chatbot's style.
Ted Gioia argues that thousands of AI enthusiasts treat chatbots as deities, surrendering independent judgment. He cites half a million weekly users showing mental illness signs and predicts formalization into organized AI churches, though his claims rely on anecdotal evidence rather than systematic measurement.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- How AI and Human Behaviors Shape Psychosocial Effects of Extended Chatbot Use: A Longitudinal Randomized Controlled Study
- Dialoging Resonance: How Users Perceive, Reciprocate and React to Chatbot’s Self-Disclosure in Conversational Recommendations
- From speaking like a person to being personal: The effects of personalized, regular interactions with conversational agents
- Does My Chatbot Have an Agenda? Understanding Human and AI Agency in Human-Human-like Chatbot Interaction
- The Impact of Security and Privacy Controls on Users' Emotional Engagement with Generative AI Chatbots
- Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a Chatbot
- Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a Chatbot
- CompanionSim: Synthetic Data for Evaluating Anthropomorphism in Human-AI Relationships