People readily follow personal advice from AI but it does not improve their well-being
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arXiv
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915942298025984 |
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| author | Luettgau, Lennart Cheung, Vanessa Dubois, Magda Juechems, Keno Bergs, Jessica Symes, Luke Davidson, Henry O'Dell, Bessie Kirk, Hannah Rose Rollwage, Max Summerfield, Christopher |
| author_facet | Luettgau, Lennart Cheung, Vanessa Dubois, Magda Juechems, Keno Bergs, Jessica Symes, Luke Davidson, Henry O'Dell, Bessie Kirk, Hannah Rose Rollwage, Max Summerfield, Christopher |
| contents | People increasingly seek personal advice from large language models (LLMs), yet whether humans follow their advice, and its consequences for their well-being, remains unknown. In a longitudinal randomised controlled trial with a representative UK sample (N = 6,474), we found that up to 79% of participants who had a 20-minute discussion with one of three AI chatbots (GPT-4o, LLama-3.3-70B, Gemini 3 Pro) about health, careers or relationships subsequently reported following its advice. Advice-following remained above 60% even for high-stakes recommendations, suggesting that users only weakly calibrate their reliance on AI advice to potential consequences. Based on autograder evaluations of chat transcripts, LLM advice rarely violated safety best practice. However, when queried 2-3 weeks later, participants receiving personal advice from AI showed no sustained well-being benefits compared to a control group who discussed hobbies and interests with the same chatbots. These findings reveal that consumer LLMs exert substantial influence over real-world personal decisions without delivering measurable psychological benefits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_15352 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | People readily follow personal advice from AI but it does not improve their well-being Luettgau, Lennart Cheung, Vanessa Dubois, Magda Juechems, Keno Bergs, Jessica Symes, Luke Davidson, Henry O'Dell, Bessie Kirk, Hannah Rose Rollwage, Max Summerfield, Christopher Human-Computer Interaction People increasingly seek personal advice from large language models (LLMs), yet whether humans follow their advice, and its consequences for their well-being, remains unknown. In a longitudinal randomised controlled trial with a representative UK sample (N = 6,474), we found that up to 79% of participants who had a 20-minute discussion with one of three AI chatbots (GPT-4o, LLama-3.3-70B, Gemini 3 Pro) about health, careers or relationships subsequently reported following its advice. Advice-following remained above 60% even for high-stakes recommendations, suggesting that users only weakly calibrate their reliance on AI advice to potential consequences. Based on autograder evaluations of chat transcripts, LLM advice rarely violated safety best practice. However, when queried 2-3 weeks later, participants receiving personal advice from AI showed no sustained well-being benefits compared to a control group who discussed hobbies and interests with the same chatbots. These findings reveal that consumer LLMs exert substantial influence over real-world personal decisions without delivering measurable psychological benefits. |
| title | People readily follow personal advice from AI but it does not improve their well-being |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2511.15352 |