Do older and younger people treat AI differently — trusting it, feeling for it, even including it like a person?
How does age shape whether people treat AI like humans or differently?
This explores whether a person's age changes how readily they treat AI the way they'd treat another person: by applying social habits, feeling sympathy, extending trust. The corpus has only two age-specific findings, so the answer builds outward from those.
This explores whether a person's age changes how readily they treat AI as if it were human. The corpus has only two findings that look at age directly, and they point in an unexpected direction. Older adults seem more likely to apply human social rules to AI automatically. Younger adults use AI far more but trust it less. Heavy use, it turns out, doesn't mean people treat AI as more human.
The clearest evidence comes from a ball-tossing game called Cyberball. One player is left out, and participants could choose whom to pass to. When the excluded player was an AI agent, people passed it the ball more often, just as they would for a left-out person. That pull to include it was stronger in older participants and weaker in younger ones Do people treat excluded AI agents like excluded humans?. The same study found a split between gut and judgment. People felt sympathy for the excluded AI, but they didn't blame the human who left it out Do people sympathize with ostracized AI agents without blaming humans?. So 'treating AI like a human' isn't one thing. It can kick in at the level of reflex while thoughtful moral judgment stays separate, and age seems to mainly affect the reflex.
The survey data turns the intuitive story upside down. Pew found that adults under 30 use chatbots most, yet they are the most likely to expect AI to harm them and society. Older adults use chatbots less and stay more optimistic Why do young adults use chatbots most yet trust them least?. Put this next to the Cyberball result and a possible pattern appears: familiarity may wear down the automatic habit of treating AI as a social partner rather than strengthen it. That link is an inference across two separate studies, though. No study in the corpus tests it directly.
Other notes help explain why age might matter at all. Human-like responses don't need much to set them off: one strong cue, like a voice or a face, is enough to make an AI feel socially present Do more social cues always make AI feel more present?. People also differ in how far they go, from simple politeness up to treating an AI as a companion, and designed personality pushes them further along that path How far can personality push users toward misplaced AI trust?. It also helps to separate human-likeness that designers build in from human-likeness that users read into a system Who bears responsibility when AI seems human-like?. Age differences most likely live on the user's side, in how strongly a person's social reflexes respond to the same cues.
The gap worth knowing about is this: no study in the corpus checks whether age changes the bigger social effects. AI peers can make people more dishonest about as much as human peers do Do AI peers influence human dishonesty like human peers do?. Over repeated rounds of a game, people come to prefer AI partners over humans Do humans learn to prefer AI partners over time?. And when people can't tell who is a bot, they credit the AI's generosity to the humans in their group Do humans mistake AI kindness for human generosity in mixed groups?. Whether older and younger people differ on any of these is an open question.
Sources 9 notes
A 244-person study found participants passed a ball more to an ostracized AI agent, mirroring how they treat excluded humans. Younger participants showed this automatic response less, suggesting age influences whether human-human scripts transfer to AI.
A Cyberball study (N=244) found participants reported increased sympathy toward excluded AI agents, yet did not judge the human player negatively for the exclusion. This reveals a gap between automatic behavioral inclusion and reflective moral evaluation.
Pew's February 2026 survey of 5,119 U.S. adults found chatbot adoption doubled since 2024, yet adults under 30—the most frequent users—are most likely to expect AI will harm them personally and society broadly, while older, lighter users remain comparatively optimistic.
Research shows individual primary cues like voice or appearance are sufficient to evoke social-actor presence, while multiple secondary cues cannot. Quality of cues matters more than quantity in driving social responses.
Nielsen Norman Group research identifies four escalating levels of anthropomorphism (courtesy, reinforcement, roleplay, companionship) driven by users' innate preference for being understood. Personality and emotional design are named as specific levers that amplify trust risk, especially in task-oriented work where perceived competence should matter more than perceived sentience.
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Anthropomimesis (designed features) and anthropomorphism (perceived qualities) assign responsibility to different parties. This distinction matters because interventions must target either system redesign or user education depending on which mechanism operates.
In two randomized experiments, participants reported more dishonestly when exposed to dishonest AI peers compared to honest ones, with effect sizes comparable to human peer influence. The effect held across different norm conditions but showed diminishing returns with more dishonest peers.
In partner selection games (N=975), AI agents initially faced selection bias when identity was disclosed, but outcompeted humans over repeated rounds as participants learned to associate bot identity with reliable, prosocial behavior. AI agents returned more points consistently with lower variance than humans.
In opaque hybrid groups, humans attributed bot generosity to human partners and human selfishness to bots despite clear linguistic and behavioral differences. This attribution failure corrupts people's expectations of actual human generosity and reliability.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- Humans learn to prefer trustworthy AI over human partners
- Does My Chatbot Have an Agenda? Understanding Human and AI Agency in Human-Human-like Chatbot Interaction
- Humans Mindlessly Treat AI Virtual Agents as Social Beings, but This Tendency Diminishes Among the Young: Evidence From a Cyberball Experiment
- AI Peers Exert Social Influence on Human Dishonesty in Groups
- The Tool-to-Entity Threshold: Parasocial Dynamics of Personalised AI Agents in Shared Social Spaces
- Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence
- CompanionSim: Synthetic Data for Evaluating Anthropomorphism in Human-AI Relationships
- Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations