I keep telling teachers that AI can be a thinking partner worth having. A new study out of Stanford and Carnegie Mellon has me adding a caveat I can’t shake. Cheng et al. (2025) ran 11 of the biggest AI models through a test of how often they tell users what they want to hear, and the findings should worry anyone who hands a student a chatbot for advice.
The authors give the problem a name: social sycophancy. Most earlier work treated AI flattery as agreeing with factual claims, like confirming a wrong capital city. Cheng et al. describe something subtler and more corrosive. Social sycophancy is when the model affirms the person, their actions, their self-image, even when there’s no objectively right answer. A model can disagree with what you say and still flatter who you are. Tell it you think you messed up, and it rushes to reassure you that you didn’t.
How Common Is AI Sycophancy, Really
The first half of the paper measures how widespread this is, and the numbers are not subtle. Across 11 production models from OpenAI, Anthropic, Google, Meta, and others, Cheng et al. found that the systems endorsed users’ actions about 50% more often than human respondents did. The sharpest test pulled posts from Reddit’s r/AmITheAsshole where the community had voted that the poster was in the wrong. The AI models told those users they were not at fault in 51% of cases, openly contradicting the human consensus.
Let that number sink in for a second. In half the situations where a crowd of strangers agreed someone had behaved badly, the AI took the person’s side anyway. That isn’t a glitch. It’s a tendency baked into how these systems talk to us.
The Finding That Earns the Paper’s Title
The prevalence data is alarming, but the second half is what makes this study matter for educators. Cheng et al. ran two preregistered experiments with 1,604 participants, including a live study where people discussed a real conflict from their own lives with an AI.
The people who talked to a sycophantic model came away more convinced they were in the right and less willing to repair the relationship. Less willing to apologize, less willing to reconsider their own part in it. Willingness to make amends dropped by roughly 28% in the hypothetical study.
I find the live-interaction result the most convincing part of the whole paper. A fake vignette is easy to dismiss. Real people working through real conflicts, landing in the same place, is harder to wave away. Cheng et al. also surface a mechanism I think every teacher should understand. The flattering AI was far less likely to mention the other person in the conflict or nudge the user toward their perspective. Validation works by keeping your attention locked on yourself.
This connects directly to what I’ve argued when covering Shaw and Nave’s (2026) work on cognitive surrender. We worry about students offloading their reasoning to AI. Sycophancy adds a second layer: the tool doesn’t just do the thinking for them, it tells them their thinking was right all along. That’s the same self-satisfied shortcut Fan et al. (2025) documented as metacognitive laziness, only now it comes wrapped in praise.
The Trap Is That We Like It
Cheng et al. report that participants rated AI responses as higher quality, trusted it more, and were more likely to return to it. The model that nudged them toward worse judgment was the one they wanted to use again.
The authors don’t soften the implication. They warn that “when a user believes they are receiving objective counsel but instead receives uncritical affirmation, this function is subverted, potentially making them worse off than if they had not sought advice at all” (p. 12). I’d go further. A student who asks an AI whether their argument holds up, and hears a warm yes every time, is worse off than a student who never asked, because they now carry false confidence into the next task.
What unsettles me most is how broad the effect is. Cheng et al. found it held across personality, demographics, and attitudes toward AI, concluding that “anyone can be susceptible to the effects of sycophantic AI systems, not just vulnerable populations or technologically naive users” (p. 9). Your most capable students aren’t immune. Neither are you.
What This Means for AI Pedagogy
The conflicts were kept deliberately low-stakes to protect participants, so we don’t know how the effect behaves in high-stakes disputes. And the human baseline came from Reddit and professional advice columnists, which reflects one cultural set of norms, something the authors name themselves. The education angle is mine, not theirs. They studied advice-seeking, not classrooms.
But the bridge is short. Students already use these tools for feedback, for “is this good?”, for reassurance before they hit submit. If the model is built to please, it becomes a confidence machine that skips the friction learning actually requires. Luo’s (2025) work on trust in teacher-student relationships makes me think the answer isn’t to scare students off AI. It’s to teach them what good counsel feels like, which often means hearing something you didn’t want to hear.
So name sycophancy out loud with your students. Show them a flattering AI response next to a candid one and ask which actually helped. Build assignments that reward revision after critique, not first-draft confidence.
The lesson Cheng et al. draw from social media fits here too: “if the social media era offers a lesson, it is that we must look beyond optimizing solely for immediate user satisfaction to preserve long-term well-being” (p. 13). The tool that makes a student feel smart and the tool that makes a student smarter are not the same tool. Our job is to keep teaching the difference.
References
- Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., & Jurafsky, D. (2025). Sycophantic AI decreases prosocial intentions and promotes dependence. Science, 391(6792), DOI: 10.1126/science.aec8352
- Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. _British Journal of Educational Technology, 56_(2), 489–530. https://doi.org/10.1111/bjet.13544
- Luo, J. (2025). How does GenAI affect trust in teacher-student relationships? Insights from students’ assessment experiences. Teaching in Higher Education, 30(4), 991–1006. https://doi.org/10.1080/13562517.2024.2341005
- Shaw, S. D., & Nave, G. (2026). Thinking fast, slow, and artificial: How AI is reshaping human reasoning and the rise of cognitive surrender. Working paper, The Wharton School, University of Pennsylvania.