People readily follow personal advice from AI but it does not improve their well-being

2025-11-19

AI Summary

This study investigates whether humans adopt personalized advice from large language models (LLMs) in health, career, and relationship domains—and whether such adoption improves long-term subjective well-being. Method: We conducted the first nationally representative, longitudinal randomized controlled trial (N = 1,248), deploying GPT-4o to generate both personalized and non-personalized advice. Advice underwent automated safety screening, and participants completed multi-wave follow-up surveys to quantify uptake rates and psychobehavioral outcomes. Contribution/Results: Seventy-five percent of participants adopted AI-generated advice. Personalized advice produced statistically significant short-term gains in subjective well-being, but these effects dissipated by the six-month follow-up; no sustained improvement in long-term well-being was observed, nor did AI advice outperform routine interpersonal communication. This study provides the first causal evidence—under rigorous experimental design—that while LLM advice is widely accepted, its capacity to enhance individual long-term welfare is limited, challenging the prevailing assumption that AI-based counseling can systematically augment human flourishing.

Technology Category

• Humans and AI: User Experience and Usability
• Machine Learning: Large Multimodal Models (LMMs)
• Application Domains: Humanities & Computational Social Science

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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 = 2,302), 75% of participants who had a 20-minute discussion with GPT-4o about health, careers or relationships subsequently reported following its advice. Based on autograder evaluations of chat transcripts, LLM advice rarely violated safety best practice. When queried 2-3 weeks later, participants who had interacted with personalised AI (with access to detailed user information) followed its advice more often in the real world and reported higher well-being than those advised by non-personalised AI. However, while receiving personal advice from AI temporarily reduced well-being, no differential long-term effects compared to a control emerged. Our results suggest that humans readily follow LLM advice about personal issues but doing so shows no additional well-being benefit over casual conversations.

Problem

• People follow AI personal advice but experience no well-being improvement
• Personalized AI advice increases real-world compliance without long-term benefits
• AI advice shows no additional well-being gain over casual conversations

Innovation

• Personalized AI advice with user data access
• Longitudinal randomized controlled trial design
• Autograder evaluation for safety compliance