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AI enhances scientific research through hybrid collaboration and feedback

Recent research explores the integration of AI, particularly large language models (LLMs), into scientific processes. One study analyzed trends in AI terminology within clinical trial registrations, noting an increase in terms like chatbots and GPTs, and found that hybrid human-AI approaches show promise for screening but require clearer reporting standards. Another paper investigated human-AI collaboration for estimating scientific replicability, demonstrating that hybrid prediction markets often outperform AI-only or human-only methods. Furthermore, a large-scale experiment showed that LLM-generated feedback on arXiv preprints significantly increased manuscript revisions and subsequent use of AI tools by authors, especially benefiting those from non-English-dominant regions. AI

IMPACT AI is proving to be a valuable tool in scientific research, improving efficiency in data analysis, enhancing replicability assessments, and democratizing access to feedback for researchers globally.

RANK_REASON The cluster consists of multiple academic papers detailing research into AI applications and human-AI collaboration in scientific contexts.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

AI enhances scientific research through hybrid collaboration and feedback

COVERAGE [4]

  1. arXiv cs.AI TIER_1 English(EN) · Sandra Woolley, Tim Collins, Khalid Khattak, Illia Chernomorets, Ariane Arevalo, Chris Richardson ·

    Trends in AI and Human-AI Interaction in Clinical Trials -- A Hybrid Human-AI Exploration

    arXiv:2605.29096v1 Announce Type: new Abstract: This paper examines records retrieved from the ClinicalTrials.gov registry to characterize temporal trends in AI terminology and the geographical distribution of AI trials. The work also reports on an exploratory hybrid human-AI app…

  2. arXiv cs.AI TIER_1 English(EN) · Tatiana Chakravorti, Robert Fraleigh, Timothy Fritton, Christopher Griffin, Vaibhav Singh, Sai Koneru, C. Lee Giles, David Pennock, Anthony Kwasnica, Sarah Rajtmajer ·

    Human-AI Collaboration for Estimating Scientific Replicability

    arXiv:2605.27394v1 Announce Type: cross Abstract: Determining whether published scientific findings can successfully be replicated is a long-standing challenge in the empirical sciences. Existing approaches for replicability assessment typically rely either on human judgment, i.e…

  3. 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…

  4. r/Anthropic TIER_1 English(EN) · /u/Jessgitalong ·

    Get More from Technology: AI as a Human Collaboration

    <table> <tr><td> <a href="https://www.reddit.com/r/Anthropic/comments/1tpdkzn/get_more_from_technology_ai_as_a_human/"> <img alt="Get More from Technology: AI as a Human Collaboration" src="https://external-preview.redd.it/sb5QnYQ2YkQTtfrbwKdIciUESSjVA7wjYEzV06BU5z8.jpeg?width=32…