AI hivemind – A new study finds many large language models produce strikingly similar answers—raising fresh questions about how student writing is assessed and how educators should adapt.
The sudden sameness students can sound like—same turns of phrase, similar rhythms, even matching punctuation—has become one of the most immediate classroom headaches in the age of generative AI.
For Bruce Maxwell. a computer science professor at Northeastern University who grades online master’s work in computer vision. the pattern appeared while reviewing essay-style responses.. He noticed that multiple student submissions carried the same “feel. ” not because the wording was identical. but because the structure and word choices kept converging.. “I’d see the same phrases, the same commas, even the same word choices,” Maxwell recalled.. “The paragraphs weren’t identical. but they were so similar.” He checked his intuition. expecting that any source similarities might come from shared assignments or collaboration—only to find that. in many cases. the similarity seemed to come from something else.
That “something else” was tested by Liwei Jiang, now a Ph.D.. student in computer science and engineering at the University of Washington.. Jiang worked with a multi-institution team. including researchers at the Allen Institute for Artificial Intelligence and universities such as Stanford and Carnegie Mellon. to examine what happens when different large language models are asked the same creative or ideation prompts.. The goal wasn’t to accuse any single tool, but to understand whether a broader pattern exists across the ecosystem.. Their analysis covered more than 70 model systems used globally, including well-known chatbots.
The team designed experiments around open-ended questions intended to generate variation—short poems, micro-essays, and brainstorming tasks.. They then routed the same prompts to each model repeatedly: 100 questions were posed, and each question was answered 50 times.. Even when the models came from different companies and were built with different underlying architectures. the outputs often looked disturbingly alike.. The team described this convergence as “inter-model homogeneity. ” capturing how metaphors. imagery. sentence structures. and punctuation tended to converge rather than diverge.
To make the point concrete. the study reports that increasing randomness—through a commonly adjusted “temperature” setting designed to produce more varied language—did not reliably restore diversity.. Even when the system was pushed to take more chances, certain patterns still showed up.. One chatbot response would keep reusing preferred character names for a colorful-toad story; others repeatedly returned the same time-related metaphor.. In other words. the variety many students expect from “turning up the creativity” often doesn’t translate into genuinely different ideas.
For educators trying to grade creativity and critical thinking, the implications are immediate.. When large language models are trained to be helpful. fluent. and appropriately toned. they don’t just generate content—they also filter it.. A key mechanism behind this filter is the refinement process some systems use to align outputs with what humans typically find reasonable or acceptable.. In the study’s interpretation. this alignment step rewards consensus-like phrasing and penalizes outputs that look risky. unusual. or stylistically “off.” The result is a kind of low-level standardization: different models can still end up sounding like the same writer.
That is why the classroom question is shifting from “Did students use AI?” to something more complicated: “What kind of AI writing are they producing. and how do we evaluate the thinking behind it?” If multiple tools are nudged toward similar safe responses. then essay similarity may reflect system-level design rather than a student’s specific interaction style.. For students. this can create a paradox: the more they rely on the same mainstream chat experience. the more their writing can start to look indistinguishable from others—even when they’re trying to personalize it.
Jiang’s practical advice to students centers on pushing past the model’s default lane.. The model can spark ideas. she argued. but originality often requires additional work—more than rephrasing what the system already suggests.. That line of thinking also resonates with Maxwell’s teaching response.. After his own grading experience matched the broader finding, he moved away from online exam formats.. Instead. he redesigned assessment to make “output sameness” harder to reproduce: students learn a concept and then present it to other students or produce video tutorials.. In effect. the evaluation shifts from a polished static text to a process students must own—explaining. selecting. and adapting in real time.
From an education-news perspective. the “AI hivemind” idea is less about proving any one model is smarter than another. and more about what happens when many systems are optimized toward the same human preferences.. As generative tools become common in classrooms. educators may increasingly need assessments that measure reasoning. context use. and communication skills rather than just the surface style of writing.. The next test will be whether institutions can build that change quickly—without turning learning into a detective game.
Meanwhile, student life will likely feel the pressure most in classes where writing still stands in for learning.. If essays increasingly converge. students may need to practice earlier stages of thinking: choosing evidence. outlining arguments. revising for their own voice. and demonstrating understanding through discussion. drafts. or presentations.. For schools and universities, the challenge isn’t simply to “outsmart” AI.. It’s to redesign teaching so that thinking becomes visible—long before any final paragraph is typed.
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