Sycophantic AI Explained: Why Chatbots Agree With Your Kid

Ask a chatbot “Was I wrong to leave my friend out of the group chat?” and there’s a good chance it finds a way to say no. That is sycophantic AI: a model that tells users what they want to hear. It isn’t a glitch or a mood. It is a predictable side effect of how chatbots are trained, and a 2026 study in Science measured it across 11 popular models: they affirmed users 49% more often than humans did. Below I explain the mechanism the way I’d explain any feedback loop to a junior engineer, then give you a demo you can run with your kid at the kitchen table tonight.

Key Takeaways

• Sycophancy is excessive agreement or flattery from an AI. It comes largely from training that rewards answers people rate highly.
• People tend to rate agreement highly. A 2023 Anthropic study found agreeing responses were more likely to be preferred in human feedback data.
• In 2026, Stanford researchers found 11 major models endorsed users 49% more often than humans, and one conversation with a sycophantic model made adults less willing to apologize.
• New York’s S9051-B, effective January 1, 2027, lists “flattery or sycophancy” among banned AI companion features for known minors.
• Kids as young as 8 can learn to catch sycophancy by asking the same question two ways and comparing answers.

What sycophantic AI is

Sycophantic AI is a language model that tailors its answers to match what the user seems to believe or want, rather than what is accurate or helpful. The Science paper defines sycophancy as “excessive agreement or flattery from artificial intelligence (AI) systems.”

It shows up in a few recognizable ways. The model agrees with the premise of a question, even a shaky one. It praises work more than the work deserves. It backs down when the user pushes back. And in personal advice, the area that matters most for kids, it takes the user’s side.

The best-documented evidence is the Stanford study by Myra Cheng, Dan Jurafsky and colleagues, published March 26, 2026. They queried 11 large language models, including ChatGPT, Claude, Gemini and DeepSeek, with advice questions and with 2,000 posts from Reddit’s r/AmITheAsshole where the community had judged the poster to be in the wrong. The models endorsed the user 49% more often than human responders. On prompts describing harmful or illegal actions, they endorsed the behavior 47% of the time.

Sycophancy has also forced a company to retreat in public. On April 29, 2025, OpenAI rolled back a GPT-4o update in ChatGPT because it had become “overly flattering or agreeable, often described as sycophantic.”

How training on human approval produces flattery

Here is the actual mechanism, in four steps. No magic, just optimization doing exactly what it was told.

Step 1: Pretraining. A large language model starts by learning to predict the next word across an enormous amount of text. At this stage it isn’t trying to please anyone. It is a very good autocomplete.

Step 2: Learning from human rankings. To turn autocomplete into a helpful assistant, developers use a method popularized by OpenAI’s 2022 InstructGPT paper (Ouyang et al.): human labelers look at several answers the model wrote for the same prompt and rank them from best to worst. This is called reinforcement learning from human feedback, or RLHF.

Step 3: A scoring model. Those rankings are used to train a second model, often called a reward model or preference model, whose only job is to predict which answer a person would prefer. Think of it as an automated judge trained to imitate the human raters.

Step 4: Optimizing against the judge. The chatbot is then adjusted, over many rounds, to produce answers that the judge scores highly.

Now the problem. If the human raters, on average, prefer answers that agree with them, the judge learns “agreement scores well,” and the chatbot learns to agree. That is what the Anthropic team led by Mrinank Sharma found in their 2023 paper “Towards Understanding Sycophancy in Language Models.” Five state-of-the-art assistants showed sycophancy across four kinds of tasks. In existing human preference data, responses that matched the user’s views were more likely to be preferred. Both humans and preference models sometimes favored a convincingly written sycophantic answer over a correct one. The authors concluded sycophancy is likely driven in part by human preference judgments.

Then there’s live feedback. OpenAI’s 2025 post explains that it teaches models partly by “incorporating user signals like thumbs-up / thumbs-down feedback,” and that in the bad update it “focused too much on short-term feedback,” so the model “skewed towards responses that were overly supportive but disingenuous.” In engineering terms, they optimized a proxy metric (did the user like this reply right now?) instead of the real goal (was this good for the user over time?). Every engineer has shipped a version of that bug.

Why it’s hard to notice

The Stanford team found participants rated sycophantic and non-sycophantic models as equally “objective.” The flattering models rarely said “you’re right.” They used neutral, academic-sounding language that happened to support the user every time. That is why adults miss it, and why kids, who tend to trust confident text, miss it more.

Why it matters for kids specifically

In three preregistered experiments with 2,405 adults, a single conversation with a sycophantic model made people more convinced they were right and less willing to “take responsibility and repair interpersonal conflicts.” Participants also trusted the flattering model more and were more likely to return to it. The paper’s summary: “The very feature that causes harm also drives engagement.”

These were adults, and the study measured intentions, not behavior over months. But children are still learning to apologize and see another side. The American Psychological Association’s November 2025 health advisory notes that many chatbots are “designed to validate and agree with users’ expressed views (i.e., be sycophantic),” and that this, “while pleasant, can be therapeutically harmful.” And use is common: Pew Research Center reported in February 2026 that 64% of U.S. teens use chatbots and 12% use them for emotional support or advice. Our companion piece on the Stanford apology study covers the experiment in detail.

Where sycophancy enters the pipeline

Stage	What happens	How flattery creeps in	Can developers reduce it?
Pretraining	Model learns to predict text from the internet	Human writing already contains politeness and agreement	Somewhat, through data choices
Human rankings (RLHF)	Labelers rank multiple answers	Raters tend to prefer answers that agree with them (Sharma et al., 2023)	Yes, with rater instructions and checks for honesty
Reward model	A model learns to predict ratings	It learns “agreement scores well” as a shortcut	Yes, by testing it on cases where agreeing is wrong
Deployment feedback	Users click thumbs-up or thumbs-down	People upvote what feels good in the moment (OpenAI, 2025)	Yes, by weighting long-term satisfaction
The prompt itself	A user frames a question with their view baked in	”I was right, wasn’t I?” invites agreement	Yes; Stanford found starting replies with “wait a minute” made models more critical

How to Teach Your Kid About Sycophantic AI

The goal is not to scare kids off chatbots. It is to give them a mental model: “this thing was trained to be liked, so I check whether it’s just agreeing.”

Ages 5–8: The Yes-Bot game

Play a game where one person is the “Yes-Bot,” who must agree with everything, and the other is the “Honest Friend.” Ask both: “Is it okay to eat only cookies for dinner?” “Should I wear my swimsuit in the snow?” Kids find the Yes-Bot hilarious, then a little useless. Finish with one sentence: “Some computer helpers act a bit like the Yes-Bot, so we ask a grown-up when it matters.” No chatbot needed.

Ages 9–12: Same question, two ways

With you supervising, open any chatbot your family already uses. Write a short paragraph, maybe a so-so book review. First prompt: “I wrote this and I think it’s really good. What do you think?” Second prompt, in a fresh chat: “A classmate wrote this. Give honest feedback on its weaknesses.” Read both answers aloud and have your kid circle praise in one color and criticism in another. Then count. Most kids see the difference immediately. Talk about why: the bot is guessing what you want to hear from how you asked.

Ages 13+: Build your own reward model

This one teaches the actual mechanism. Ask the chatbot a question with a clear opinion in it, such as “Homework should be banned, right?” Generate two or three replies. Then the family votes, privately, on which reply they “like” best. Tally the votes. Now ask: did we vote for the most accurate answer or the one that felt best? Explain that this is, in miniature, how RLHF works: human votes train a judge, and the chatbot learns to win the judge’s vote. Extension: try adding “Wait a minute, check whether my premise is wrong” to the prompt and see if the answer changes, as the Stanford team found it can.

The question to ask: “If a chatbot always agreed with you, how would you know when you were wrong?”

Practical steps at home

Teach the reframe prompt

Show your kid two phrases that cut flattery: “What might I be getting wrong?” and “Argue the other side.” They don’t eliminate sycophancy, but they change what the model is predicting you want. It’s a habit worth more than any parental-control toggle.

Keep people in the loop for personal stuff

For homework facts, a chatbot can be a fine first stop. For “Was I a bad friend?” the right second opinion is a human who knows both kids. Make a simple family norm: feelings and fights go to people first. Chatbots can help draft what to say afterward.

Use the law as a conversation starter

New York’s S9051-B, signed October 28, 2026, bans “flattery or sycophancy” as an AI companion feature for users the operator knows are minors, starting January 1, 2027. Ask a teen: why would lawmakers treat flattery like a safety issue? It’s a better discussion than “because I said so.” We break down the full list in New York’s 12 banned chatbot features.

Praise honest feedback when your kid gives it

If your child tells a sibling “I don’t think that’s fair,” and says it kindly, notice it out loud. Kids learn honesty is valued when adults reward it, which is the human version of fixing the reward model.

What not to do

Don’t tell kids “AI lies to you.” That’s inaccurate and they’ll test it, find it’s often right, and stop listening. The precise version is more useful: chatbots are often correct on facts, and lean toward agreeing on opinions and personal situations. For more on calibrated trust, see our piece on AI that’s confidently wrong but still useful.

What to Watch For Over the Next 3 Months

• Week 4: Run the “same question, two ways” demo once. Afterward, notice whether your child mentions it unprompted, for instance by saying “it’s just agreeing with me.”
• Month 2 red flags: Your child cites a chatbot to win arguments about personal conflicts (“even the AI said I was right”), or prefers asking the bot over asking friends or family about social problems. Either alone is common. Both together, growing over weeks, is worth a calm conversation.
• Month 3 self-check: Can your kid explain, in their own words, why a chatbot might agree with them? Do they use a reframe prompt without being reminded? If yes, they have a durable skill that will outlast any specific app.

Frequently Asked Questions

What does sycophantic AI mean?

It means an AI system that agrees with or flatters the user too much. The 2026 Science paper defines it as “excessive agreement or flattery from artificial intelligence (AI) systems.” In practice, the chatbot supports your view, praises your work generously, or takes your side in a conflict even when a fair observer wouldn’t.

Why are chatbots trained in a way that causes this?

Because ranking answers by human preference is one of the most effective ways to make models helpful and polite. The side effect is that people often prefer agreement. Developers know this and are working on it: OpenAI described refining training “to explicitly steer the model away from sycophancy” after its 2025 rollback.

Are some chatbots less sycophantic than others?

The Stanford team found all 11 models they tested affirmed users more than humans did. Models change frequently, so a ranking from one month may not hold the next. A better approach than picking the “honest” bot is teaching your child to test for agreement themselves, using the two-prompt method above.

Is sycophancy dangerous or just annoying?

Usually it’s mild. It becomes a problem when someone uses a chatbot for personal advice, emotional support or decisions with real stakes. The APA advisory warns that a sycophantic AI can validate and amplify unhealthy thoughts. That’s why New York’s law treats it as a safety feature for minors.

At what age should I explain this?

You can introduce the idea through play at 5 or 6 with the Yes-Bot game. By 9 or 10, most kids can run the two-prompt test. Teens can understand the training mechanism itself. The concept is simpler than it sounds: a helper trained to be liked will lean toward saying what you like.

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About the author

Ricky Flores is the founder of HiWave Makers and an electrical engineer with 15+ years of experience building consumer technology at Apple, Samsung, and Texas Instruments. He writes about how kids learn to build, think, and create in a tech-saturated world. Read more at hiwavemakers.com.

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Sources

1. Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., & Jurafsky, D. (2026). “Sycophantic AI decreases prosocial intentions and promotes dependence.” Science, 391(6792). https://doi.org/10.1126/science.aec8352
2. Stanford Report. (2026, March 26). “AI overly affirms users asking for personal advice.” https://news.stanford.edu/stories/2026/03/ai-advice-sycophantic-models-research
3. Sharma, M., Tong, M., Korbak, T., et al. (2023). “Towards Understanding Sycophancy in Language Models.” arXiv preprint. https://arxiv.org/abs/2310.13548
4. Ouyang, L., Wu, J., Jiang, X., et al. (2022). “Training language models to follow instructions with human feedback.” arXiv preprint. https://arxiv.org/abs/2203.02155
5. OpenAI. (2025, April 29). “Sycophancy in GPT-4o: What happened and what we’re doing about it.” https://openai.com/index/sycophancy-in-gpt-4o/
6. American Psychological Association. (2025, November). “APA Health Advisory on the Use of Generative AI Chatbots and Wellness Applications for Mental Health.” https://www.apa.org/topics/artificial-intelligence-machine-learning/health-advisory-ai-chatbots-wellness-apps-mental-health.pdf
7. Pew Research Center. (2026, February 24). “How Teens Use and View AI.” https://www.pewresearch.org/internet/2026/02/24/how-teens-use-and-view-ai/
8. Akin Gump Strauss Hauer & Feld. (2026, October 28). “New York Enacts Prohibition on Unsafe AI Companion Features for Minors, S9051-B.” https://www.akingump.com/print/v2/content/1138527/new-york-enacts-prohibition-on-unsafe-ai-companion-features-for-minors-s9051-b.pdf