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Published on: December 6, 2024
Age and generational differences in anthropomorphism and trust in large language models
Michelle Cohn1,2, Mahima Pushkarna3, Mark Díaz4
1Department of Linguistics, University of California, Davis, Davis, CA, United States.
Frontiers in Cognition
|August 4, 2026
Summary
Younger adults anthropomorphize large language models (LLMs) less than older generations. This study explored how age impacts perceptions of AI social agency and trust in LLMs.
Area of Science:
- Human-Computer Interaction
- Artificial Intelligence Ethics
- Cognitive Psychology
Background:
- Large language models (LLMs) are becoming central to information seeking.
- Understanding user perceptions of LLMs as social agents is crucial.
- Age-related differences in technology adoption and social cognition are well-documented.
Purpose of the Study:
- To investigate how individuals anthropomorphize and trust a pseudo-LLM.
- To examine age and generational differences in these perceptions.
- To explore the impact of anthropomorphic cues on user experience.
Main Methods:
- Survey of 1,485 English-speaking US adults across four generations (Gen Z, Millennials, Gen X, Baby Boomers).
- Experimental manipulation of anthropomorphic cues (text vs. text+speech, first-person vs. third-person framing).
- Analysis of anthropomorphism, trust, and accuracy ratings, supplemented by qualitative data.
Main Results:
- Gen Z exhibited lower anthropomorphism, trust, and perceived accuracy compared to older generations.
- Anthropomorphic voice cues enhanced perceived human-likeness and accuracy across all age groups.
- Qualitative data revealed distinct conceptualizations of the LLM based on age and generation.
Conclusions:
- Age and generational factors significantly influence how individuals attribute social agency and mind to artificial systems.
- LLM design incorporating anthropomorphic cues can enhance user experience uniformly.
- Further research is needed to understand the long-term implications of these perceptions.
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