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AI4Research: a survey of artificial intelligence for scientific research

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Chen, Qiguang, Yang, Mingda, Qin, Libo, Liu, Jinhao, Yan, Zheng, Guan, Jiannan, Peng, Dengyun, Ji, Yiyan +8 more·2026·Science China Information Sciences
DOI 10.1007/s11432-025-5000-y
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Abstract

Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs) such as OpenAI-o1 and DeepSeek-R1, have demonstrated remarkable capabilities in complex domains such as logical reasoning and experimental coding. Motivated by these advancements, numerous studies have explored the application of AI in the innovation process, particularly in the context of scientific research. These AI technologies primarily aim to develop systems that can autonomously conduct research processes across a wide range of scientific disciplines. Despite these significant strides, a comprehensive survey on AI for research (AI4Research) remains absent, which hampers our understanding and impedes further development in this field. To address this gap, we present a comprehensive survey and offer a unified perspective on AI4Research. Specifically, the main contributions of our work are as follows. (1) Systematic taxonomy: We first introduce a systematic taxonomy to classify five mainstream tasks in AI4Research. (2) New frontiers: Then, we identify key research gaps and highlight promising future directions, focusing on the rigor and scalability of automated experiments, as well as the societal impact. (3) Abundant applications and resources: Finally, we compile a wealth of resources, including relevant multidisciplinary applications, data corpora, and tools. We hope our work will provide the research community with quick access to these resources and stimulate innovative breakthroughs in AI4Research. All data and resources are publicly available at https://github.com/LightChen233/Awesome-AI4Research .

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  • The rapid advancement of AI4Research has fundamentally transformed scholarly communication, spawning interest in automating and enhancing peer review.

    “The rapid advancement of AI4Research has fundamentally transformed the landscape of scholarly communication, spawning a surge of interest in automating and enhancing the peer review process (Chen et al., 2025).”

    From Can AI Be a Good Peer Reviewer? A Survey of Peer Review Process, Evaluation, and the Future (2026) · cites this paper as Chen et al., 2025

    Sections 2.1.5 and 7 describe AI4Research as automating and enhancing peer review and report that researchers are exploring AI for that purpose. They do not establish that AI4Research has fundamentally transformed scholarly communication or that such a transformation caused a surge of interest; Section 7 instead points to delays, time demands, and growing workloads as motivations. (LLM, conf=0.85)

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Authors
Chen, Q., Yang, M., Qin, L., Liu, J., Yan, Z., Guan, J., Peng, D., Ji, Y., Li, H., Hu, M., Zhang, Y., Liang, Y., Zhou, Y., Wang, J., Chen, Z., & Che, W.
Affiliations
  • Harbin Institute of Technology (CN)
  • Central South University (CN)
  • University of Hong Kong (HK)
  • University of Illinois Urbana-Champaign (US)
  • Princeton University (US)
  • Fudan University (CN)
  • Chinese University of Hong Kong (HK)
  • ByteDance (China) (CN)
Corresponding author
  • Qin, Libo
  • Che, Wanxiang
ORCID
Venue
Science China Information Sciences
Publisher
Springer Science and Business Media LLC
Published
2026-09-28
Volume
69
Issue
10
ISSN
  • 1869-1919 (ISSN-L)
  • 1674-733X
DOI
10.1007/s11432-025-5000-y
OpenAlex
W4417183323
Cited by
4
References
2
Citations by year
2026: 4
FWCI
13.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Science China Information Sciences (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • National Natural Science Foundation of China (62236004, 62476073, 62306342)
  • Young Scientists Fund
  • Fundamental Research Funds for the Central Universities
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Social Sciences › Decision Sciences › Information Systems and Management › Research, Science, and Academia
Keywords
large language models, autonomous research, automated experimentation, scientific research automation, research gaps

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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