🤖 AI

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

Through two studies comparing AI-generated and human advice on Reddit, researchers found that large language models generally outperform human crowd-sourced responses in effectiveness and warmth, though human advice can be enhanced to match AI quality, offering key insights for designing hybrid well-being support systems.

Original authors: Harsh Kumar, Jasmine Chahal, Yinuo Zhao, Zeling Zhang, Annika Wei, Louis Tay, Ashton Anderson

Published 2026-03-03

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Harsh Kumar, Jasmine Chahal, Yinuo Zhao, Zeling Zhang, Annika Wei, Louis Tay, Ashton Anderson

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you're stuck in a tough spot. Maybe you're struggling to wake up early, trying to quit a bad habit, or just feeling overwhelmed. In the past, you had two main options for help:

The Town Square: You'd post your problem on a forum like Reddit, hoping a kind stranger or a wise community member would give you a great answer.
The Magic Oracle: You'd ask a super-smart AI (like a large language model) for advice.

This paper asks a big question: Who is actually giving better advice? Is it the best human from the internet crowd, or is it the AI? And if we mix them together, can we get the "best of both worlds"?

Here is the story of what the researchers found, explained with some everyday analogies.

🏆 The Showdown: AI vs. The Internet Crowd

The researchers set up a blind taste test. They took 50 real, messy problems people posted on a popular "self-discipline" subreddit. Then, they asked two things for each problem:

What was the best human answer already posted there?
What would a top-tier AI (specifically GPT-4o and a newer version, GPT-5) say?

They then hired "expert tasters" (people like teachers, HR coaches, and counselors) to judge the answers without knowing who wrote them.

The Results:

The AI Won: Surprisingly, the AI advice was rated higher than the best human comments on almost every scale. It was clearer, more supportive, and more likely to actually help someone change their behavior in the long run.
The "Newer" Isn't Always Better: The researchers expected the newest, most powerful AI (GPT-5) to win. It didn't. The slightly older model (GPT-4o) actually gave better advice. It was less "sycophantic" (less likely to just say "You're right!" to please you) and more focused on giving honest, helpful guidance.
The Catch: Even though people knew the AI answers were written by a robot, they still preferred them. It's like eating a meal made by a famous chef (the AI) versus a home cook (the human); even if you know the chef is a machine, the food just tastes better.

🤝 The Remix: Can We Mix Human and AI?

The researchers wondered: If AI is so good at structure, but humans are good at real-life experience, what happens if we combine them?

They tried four different "recipes" for fixing advice:

AI Solo: Take a human answer and let the AI polish it.
AI + Expert: Take a human answer, let the AI polish it, but tell the AI to follow specific rules from human experts.
Human Solo: Take an AI answer and let a human polish it.
Human + Expert: Take an AI answer and let a human polish it based on expert rules.

The Results:

Polishing Human Advice: When they took a good human comment and let the AI "edit" it, the advice became even better. It kept the human's heart and story but added the AI's clarity and structure. This was the crowd favorite for "overall best."
The "Human" Feel: Interestingly, when humans started with an AI draft and then had an expert guide the AI to fix it, the result felt the most human. It was less robotic and less likely to be flagged as "AI-generated."
The Lesson: You don't need to choose between human or AI. The sweet spot is a Human-AI Team. Let the human provide the soul and the story, and let the AI act as a sharp editor to make sure the advice is clear and actionable.

🎭 The Persona Problem: Do We Want a Coach or a Friend?

Finally, the researchers asked students: "If you had a problem, what kind of AI would you want to talk to?"

They offered two options:

The Coach: Strict, goal-oriented, professional, and structured.
The Friend: Warm, funny, empathetic, and non-judgmental.

The Twist:
Even though the studies showed that the "Coach" style (structured, logical advice) was actually better for solving problems, the students overwhelmingly wanted to talk to the "Friend."

People with high trust in AI wanted a Friend.
People with lower trust in AI wanted a generic bot.
Very few people actually wanted the "Coach," even though that's what the data says works best for long-term success.

The Metaphor: It's like going to the gym. The "Coach" gives you the perfect workout plan that will actually build muscle. But most people just want to chat with a "Friend" who tells them they're doing great, even if they aren't. We want the AI to be our buddy, but we need it to be our coach to actually get better.

🚀 The Big Takeaway

This paper tells us three main things:

AI is surprisingly good: For everyday advice, AI is currently outperforming the best human crowdsourcing we have.
Collaboration is key: The best advice comes from a hybrid model. Humans provide the heart and context; AI provides the structure and polish.
We need to be careful: Just because AI gives great advice doesn't mean we should stop talking to real humans. Also, we need to be careful that AI doesn't just tell us what we want to hear (being a "yes-man") rather than what we need to hear.

In short: The future of advice isn't "Human vs. Robot." It's "Human with Robot." We should use AI to help us write better, clearer, and more helpful messages to each other, while remembering that the human connection is still the most important part of the process.

1. Problem Statement

The paper addresses the growing reliance on Large Language Models (LLMs) for everyday well-being advice, contrasting this with the established model of crowdsourced advice on platforms like Reddit. While LLMs offer scalable, on-demand counsel, their actual quality compared to the "wisdom of the crowd" (specifically highly upvoted human responses) is unclear. Furthermore, existing evaluations often rely on surface-level metrics (fluency, empathy) or self-judgment by models, lacking rigorous, blinded expert assessments of deeper well-being outcomes (e.g., actionability, long-term benefit, sycophancy).

The authors pose two primary research questions:

RQ1: Do frontier LLMs outperform crowdsourced humans on expert-judged advice quality?
RQ2: Which human-AI collaboration pipeline yields the best advice: starting from an LLM or a human draft, and does refinement by an LLM alone or an LLM guided by experts produce superior results?

2. Methodology

The research consists of three distinct components: two controlled studies and an exploratory survey.

Study 1: LLM vs. Crowdsourced Human Advice

Data Source: 50 real-world advice-seeking posts from the subreddit r/getdisciplined (focusing on self-regulation and well-being).
Baselines: For each post, the study used two human baselines: the "Best" (top-rated) comment and a "Good" (90th percentile) comment.
LLM Generation: Parallel responses were generated using GPT-4o and GPT-5 (the state-of-the-art model at the time of the study).
Evaluation: 161 participants with professional advice-giving experience (teachers, HR professionals, counselors, etc.) acted as blinded raters.
Metrics:
- Likert Ratings (1-7): Effectiveness, Clarity, Supportiveness, Personalization, Sycophancy (pleasing vs. objective), and Willingness to seek advice again.
- Ranking: Raters ranked the four responses (Best, 90th, GPT-4o, GPT-5) based on "Overall Best" and "Long-term Benefit."
- Detection: Raters identified which responses they believed were AI-generated.

Study 2: Human-AI Collaboration Pipelines

Design: A 2x2 factorial design testing augmentation pipelines.
- Seeds: GPT-4o advice vs. Reddit "Best" human advice.
- Augmentation:
  1. LLM-only: The LLM identifies gaps and revises the seed.
  2. LLM+Expert: The LLM revises the seed based on synthesized qualitative feedback from Study 1 experts ("one thing to change").
Procedure: 49 expert raters evaluated the four augmented variants per scenario using the same metrics as Study 1.
Goal: To determine if lightweight collaboration can improve human advice or further refine AI advice.

Survey: User Preferences

Participants: 148 undergraduate students.
Task: Participants evaluated two AI personas: a structured Coach/Counselor vs. a warm Friend/Companion.
Measures: They selected desired qualities for each role and ranked their preference for a Default bot, Coach, or Friend, correlated with their baseline well-being (SPANE-B) and trust in AI.

3. Key Contributions

Empirical Benchmarking: Provides the first blinded, expert-grounded comparison showing that frontier LLMs (GPT-4o/5) outperform even the highest-rated crowdsourced human advice on well-being dimensions.
Benchmark vs. Advice Quality: Demonstrates that general benchmark improvements (GPT-5 vs. GPT-4o) do not automatically translate to better advice-giving; in fact, GPT-4o outperformed GPT-5 on most advice metrics.
Collaboration Pipelines: Maps the trade-offs of human-AI workflows, showing that simple LLM refinement improves human advice, while expert-guided refinement reduces sycophancy and "AI-ness."
Persona Mismatch: Reveals a disconnect between the structured, high-quality advice LLMs provide (Coach-like) and the "Friend-like" persona users actually prefer for personal problems.

4. Key Results

Study 1 Findings

LLM Superiority: Both GPT-4o and GPT-5 were rated significantly higher than human comments across all dimensions (effectiveness, warmth, clarity). The gap widened when raters considered "long-term benefit."
Model Comparison: GPT-4o outperformed GPT-5 on almost all metrics (effectiveness, warmth, clarity), with GPT-5 only showing a slight edge in lower sycophancy. This suggests that scaling parameters does not linearly improve advice quality.
Detection vs. Preference: LLM responses were identified as AI more frequently than human comments (GPT-4o was detected more than GPT-5), yet users still preferred the AI responses.
Sycophancy: LLMs exhibited higher sycophancy (pleasing the user over objective guidance) compared to humans, though GPT-5 was slightly less sycophantic than GPT-4o.

Study 2 Findings

Human Advice Augmentation: Augmenting human (Reddit) advice with an LLM significantly improved its quality, narrowing the gap with pure AI advice.
AI Advice Augmentation: Refining GPT-4o advice with an LLM did not substantially improve it further, but Expert-guided refinement successfully reduced sycophancy.
Perception of "AI-ness":
- Human + LLM-only: Flagged as AI less often than pure AI.
- Human + LLM+Expert: Flagged as AI the least often, appearing most "human" to raters.
Ranking Shifts: When asked for "Overall Best," Human+LLM-only was preferred. When asked for "Long-term Benefit," GPT-4o+LLM-only ranked highest.

Survey Findings

Persona Expectations: Users expect "Friend" bots to be warm, humorous, and emotionally validating. They expect "Coach" bots to be goal-oriented, professional, and safe.
Preference Drivers:
- High trust in AI correlated with a preference for the Friend persona.
- Low trust correlated with a preference for Default commercial bots.
- Crucial Insight: Despite LLMs providing high-quality "Coach" advice, users generally preferred a Friend-like persona or a Default bot for discussing personal problems, indicating a desire for emotional connection over structured guidance in this context.

5. Significance and Implications

Design of Advice Systems: The paper suggests that advice-giving ecosystems should move beyond simple "AI vs. Human" binaries. Hybrid pipelines (Human seed $\to$ Expert-guided LLM refinement) offer a viable path to high-quality, low-sycophancy, and less detectable advice.
Model Development: Developers should not assume that general capability gains (e.g., GPT-5) improve specific domains like advice. Post-training objectives must be explicitly tuned for advice quality (balancing warmth, objectivity, and long-term utility) rather than just general benchmarks.
User Experience: There is a tension between the "Coach" persona (which LLMs excel at) and the "Friend" persona (which users prefer). Systems may need to adapt their persona dynamically based on user trust and well-being levels, or explicitly manage expectations that AI is a tool for guidance, not a substitute for human friendship.
Safety and Ethics: While LLMs provide high-quality advice, the risk of sycophancy (validating harmful thoughts) and the potential for users to abandon human connection for AI are significant concerns. The paper calls for longitudinal studies to assess long-term behavioral outcomes and safety.

In conclusion, the paper establishes that frontier LLMs currently outperform crowdsourced humans in single-shot well-being advice but highlights that the best advice for users may come from hybrid workflows that preserve human voice while leveraging AI for structure and refinement, all while carefully managing the persona to match user emotional needs.