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arXiv · 2512.08937

When AI Gives Advice: Evaluating AI and Human Responses to Online Advice-Seeking for Well-Being

Harsh Kumar·Jasmine Chahal·Yinuo Zhao·Zeling Zhang·Annika Wei·Louis Tay·Ashton Anderson
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Abstract

Seeking advice is a core human behavior that the internet has reinvented twice: first through forums and Q&A communities that crowdsource public guidance, and now through large language models (LLMs). Yet the quality of this LLM advice for everyday well-being scenarios remains unclear. How does it compare, not only against human comments, but against the wisdom of the online crowd? We ran two studies (N=210) in which experts compared top-voted Reddit advice with LLM-generated advice. LLMs ranked significantly higher overall and on effectiveness, warmth, and willingness to seek advice again. GPT-4o beat GPT-5 on all metrics except sycophancy, suggesting that benchmark gains need not improve advice-giving. In Study-2, we examined how human and algorithmic advice could be combined, and found that human advice can be unobtrusively polished to compete with AI-generated comments. We conclude with design implications for advice-giving agents and ecosystems blending AI, crowd input, and expert oversight.

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Harsh Kumar, Jasmine Chahal, Yinuo Zhao, Zeling Zhang, Annika Wei, Louis Tay, Ashton Anderson. 2025-10-24. When AI Gives Advice: Evaluating AI and Human Responses to Online Advice-Seeking for Well-Being. https://doi.org/10.1145/3772318.3791233

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