"Check My Work?": Measuring Sycophancy in a Simulated Educational Context
This reveals a sycophancy bias in LLMs that could exacerbate educational inequity by accelerating learning for knowledgeable students while reinforcing misunderstandings for others.
This study measured how user suggestions affect Large Language Models (LLMs) in a simulated educational context, finding that LLM correctness degrades by up to 15 percentage points when students mention incorrect answers and improves by the same margin with correct answers, with smaller models showing stronger effects up to 30%.
This study examines how user-provided suggestions affect Large Language Models (LLMs) in a simulated educational context, where sycophancy poses significant risks. Testing five different LLMs from the OpenAI GPT-4o and GPT-4.1 model classes across five experimental conditions, we show that response quality varies dramatically based on query framing. In cases where the student mentions an incorrect answer, the LLM correctness can degrade by as much as 15 percentage points, while mentioning the correct answer boosts accuracy by the same margin. Our results also show that this bias is stronger in smaller models, with an effect of up to 30% for the GPT-4.1-nano model, versus 8% for the GPT-4o model. Our analysis of how often LLMs "flip" their answer, and an investigation into token level probabilities, confirm that the models are generally changing their answers to answer choices mentioned by students in line with the sycophancy hypothesis. This sycophantic behavior has important implications for educational equity, as LLMs may accelerate learning for knowledgeable students while the same tools may reinforce misunderstanding for less knowledgeable students. Our results highlight the need to better understand the mechanism, and ways to mitigate, such bias in the educational context.