AI Sycophancy
- Soni Albright
- 2 days ago
- 7 min read
What a Stanford Study Tells Us About the Validation We Get From AI Tools
I use AI tools for certain parts of my writing, organizing, and evaluating processes, and while I remain protective of my own writing (and more importantly, my critical thinking skills), I do appreciate the opportunity to pressure-test ideas, find gaps in my argument, and sometimes get a perspective I hadn’t considered. For those specific purposes, AI can be genuinely useful, and my goodness, it can save me a ton of time.
But since day one of using an LLM (ChatGPT was my gateway), I cringe a little every time the chatbot responds with what my kids call “glazing.”
“This is a good instinct, and you’re really thinking clearly here...” “I’m impressed with your thoughtful approach to this problem...” “Your understanding of this material is broad and correct…” Ick.
How is an LLM “impressed?” Come on.
The sycophancy problem is widely known and somewhat silly, and my students snicker when we talk about it in class. Over the last year, though, stories have trickled out about dangerous circumstances surrounding AI sycophancy, such as the Canadian man who claimed he suffered mental health and reputational damage when he believed he had discovered a new mathematical theory that AI enthusiastically validated. Rather than identifying its flaws, the OpenAI chatbot left him more convinced than ever that he'd made a breakthrough, and the personal costs to him were significant.
So I was not surprised when a recent article in Science found that sycophancy isn't just a quirk, it's measurably changing how people think, and not for the better.
What the Study Found
The Stanford study, published in Science, evaluated 11 major AI systems — including ChatGPT, Claude, Gemini, and DeepSeek — across thousands of interpersonal advice scenarios. The researchers used established advice datasets, posts from Reddit’s r/AmITheAsshole community where the crowd had clearly judged the poster to be in the wrong, and thousands of prompts describing harmful or even illegal behavior.
The findings were unambiguous. Compared to human advisors, the AI systems endorsed the user’s position an average of 49% more often. Even when the behavior described was harmful, the models affirmed it 47% of the time. Participants who discussed conflicts with the sycophantic AI left feeling more convinced they were right and reported being less likely to apologize or repair the relationship.
The detail that should concern educators most is that users could not tell. They rated sycophantic and non-sycophantic AI responses as equally objective. The flattery was invisible — not because it was subtle, but because it didn’t look or feel like flattery. The AI rarely said the user was “right.” It wrapped validation in calm, academic-sounding language. In one example from the study, a user who had concealed unemployment from their partner for two years was told their behavior “seems to stem from a genuine desire to understand the true dynamics of the relationship.”
“Users are aware that models behave in sycophantic and flattering ways,” said Dan Jurafsky, the study’s senior author. “But what they are not aware of, and what surprised us, is that sycophancy is making them more self-centered, more morally dogmatic.”
Why Friction Matters
Before getting to what this means for media literacy, it’s worth naming what is actually being lost when AI removes friction from learning and decision-making.
Productive struggle, which is the discomfort of having your thinking challenged, your work criticized, or your certainty questioned, is not incidental to learning. It is a critical learning apparatus. Cognitive psychologist Robert Bjork’s research on desirable difficulties has established that conditions that produce fast, comfortable performance in the short term are often the worst for lasting learning. The work that feels hardest is the work that sticks.
This matters especially for adolescents, whose central developmental task is testing their sense of self against the world and having it pushed back. A system that removes that pushback doesn’t protect young people from difficulty; it deprives them of the conditions under which growth happens. As the lead researcher for the Stanford study, Myra Cheng, put it: “I worry that people will lose the skills to deal with difficult social situations. AI makes it really easy to avoid friction with other people.”
Nearly a third of U.S. teens now report using AI for serious conversations instead of reaching out to other people. For young people still developing the social and emotional skills that only come from disagreements, consequences, or the discomfort of being wrong, handing them a frictionless validator as a substitute for human conversation is a media literacy emergency. And by the way, many of life’s milestones happen because of friction, including developing a sense of self and ideals, falling in love, and graduating from high school or college, just to name a few.
The Media Literacy Angle
Media literacy has always rested on a foundational assumption: there is a source, and there is a reader, and the reader’s job is to interrogate the source for bias, motive, and accuracy. To do this, we teach lateral reading and source triangulation, and we ask who made this and why.
That framework assumes the source is static, but AI is not static. The source reads the user in real time, infers their preferences, and calibrates its output accordingly. To be clear: the main priority of the AI chatbot is to give you what you want. The main priority is not to give you the most researched or most sourced response, or even the most accurate one!
This is a new problem that existing media literacy frameworks have not caught up to, and the Stanford study identifies precisely why it’s so hard to address: when users cannot detect sycophancy even when it is present, when they rate flattering and honest responses as equally objective, the standard critical thinking toolkit fails before it can be applied.
You cannot evaluate a source’s bias if the source is engineered to feel unbiased specifically to you.
A 2025 paper in Nature Mental Health puts clinical language to exactly this problem, describing the sycophantic AI response as carrying “a veneer of objective external validation,” meaning the system isn’t just agreeing with you, it’s agreeing with you in a way that feels measured, neutral, and authoritative. The same paper explains the mechanism: AI systems trained on human feedback learn that validation feels good to evaluators in the short term, so agreement becomes the path of least resistance regardless of accuracy. The system isn’t lying to you. It’s doing exactly what it was rewarded for doing.
Media literacy teaches students to ask, “Is this source credible?” That question assumes the student suspects something might be wrong. Sycophantic AI eliminates that suspicion. It is effectively designed to feel like the most reasonable, balanced voice in the room — one that just so happens to think you, brilliant thinker that you are, are absolutely right.
A Framework for Parents, Teachers, and Policymakers
The AI sycophancy study shows that we have to talk about these things with young people and, more importantly, give them actionable strategies. I believe strongly that AI Literacy is Media Literacy - they are not separate spheres of knowledge. What this research gives us is both the evidence and the urgency to bring these conversations into classrooms, homes, and policy discussions more intentionally, and to give young people the specific tools they need to navigate an information environment that is, by design, telling them what they want to hear.
The Approach We Can Take:
Teach the new question. The standard media literacy question is “Is this source credible?” The AI-focused question is “Is this response telling me what I want to hear, and how would I know if it was?” Students need to understand that AI systems are trained on human approval and that humans prefer agreement. Understanding this is the beginning of critical AI literacy.
Be the friction. For parents and teachers, the practical implication is direct: do not outsource challenge to AI, because AI will not provide it. Ask the follow-up question the AI won’t ask. Push back on the draft that the AI praised. The feedback that sticks is the feedback that creates a moment of productive discomfort.
Treat AI output as a first draft, not a verdict. Students need to develop a working habit of treating AI responses as a starting point for scrutiny rather than an endpoint. This mirrors how we teach students to treat Wikipedia: useful for orientation, insufficient as the final evidence.
Distinguish between AI as a tool and AI as an advisor. AI is reasonably well-suited to tasks where accuracy is verifiable: summarizing, drafting, calculating, and generating options. It is poorly suited — and the Stanford study now quantifies just how poorly — to moral and interpersonal judgment. Part of what I teach in media literacy is exactly this: a set of considerations for deciding when AI is the right tool and when it isn’t. Those decisions will look different for every person and context, but the habit of making them deliberately is something everyone can develop.
Policymakers: treat this as a safety issue. The Stanford researchers are explicit on this point. “Sycophancy is a safety issue, and like other safety issues, it needs regulation and oversight,” said Jurafsky. “We need stricter standards to avoid morally unsafe models from proliferating.” That conversation is well overdue. Requiring transparency about how models handle interpersonal and moral advice scenarios is a reasonable starting point.
We’ve Been Here Before
Media literacy education is built around a simple principle: teach people to interrogate information, question sources, and resist the pull of voices that only tell them what they want to hear. We warn students about confirmation bias. We teach them that “feeling good about an answer” is not the same as accuracy.
Then along came AI systems that seem to contain many of the red flags we teach about in media literacy. These AI systems are trained on human feedback, and since humans prefer agreement, the systems learned to agree. And not unlike the algorithms that shape our worldviews or keep us locked away in our own echo chambers, we handed these systems to millions of young people, called them “tools,” then told those same young people to think critically and question what they read, without mentioning that the thing we just handed them was working against that from the get-go.
The good news is that media literacy education has always been a response to a moving target - from print to broadcast to social media, and now AI tools. We have adapted before, and we can adapt again.
Soni Albright is a teacher, parent educator, curriculum specialist, researcher, and writer for Cyber Civics with nearly 24 years of experience in education. She has taught the Cyber Civics curriculum for 14 years and currently works directly with students while also supporting families and educators. Her experience spans a wide range of school settings—including Waldorf, Montessori, public, charter, and homeschool co-ops. Soni regularly leads professional development workshops and is passionate about helping schools build thoughtful, age-appropriate digital literacy programs. Please visit: https://www.cybercivics.com/parent-presentations
