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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

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

RANK_REASON 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]

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COVERAGE [1]

  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…