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AI feedback boosts preprint revisions and LLM tool adoption

A large-scale randomized field experiment involving over 31,000 arXiv preprints and 45,000 researchers demonstrated that AI-generated feedback significantly increases manuscript revisions. Authors receiving AI feedback were 12.55% more likely to revise their work and subsequently increased their use of LLM tools in future papers. The positive effects were most pronounced for authors from non-English-dominant regions, less established manuscripts, and early-career researchers, suggesting AI can democratize access to scientific critique. AI

影响 AI feedback can democratize scientific critique, boosting productivity and equity for researchers globally.

排序理由 The cluster contains an academic paper detailing a large-scale randomized field experiment on the impact of AI feedback on scientific research. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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  1. arXiv cs.AI TIER_1 English(EN) · Binglu Wang, Weixin Liang, Jiahui Xue, Yuhui Zhang, Hancheng Cao, Dashun Wang, Yian Yin ·

    Human-AI Collaboration in Science at Scale: A Global Large-scale Randomized Field Experiment

    arXiv:2605.24180v1 Announce Type: cross Abstract: Collaboration is the defining mode of modern science, yet its core mechanism -- feedback -- remains hard to observe, difficult to scale, and unequally distributed. Here we test whether large language models (LLMs) can contribute t…