AI4Research: A Survey of Artificial Intelligence for Scientific Research
- Published
- Source
- arXiv
- Paper number
- 067
- Field
- Scientific AI / Survey
- arXiv ID
- 2507.01903
Key points
- Despite rapid progress in large language models and their scientific innovation applications, there has been no comprehensive and systematic survey that specifically covers the full cycle of scientific research, AI4Research.
- Existing surveys usually focus on narrower subareas such as AI for scientific discovery, which has fragmented understanding of AI's broader impact on academic workflows.
- The lack of an integrated perspective hinders systematic understanding and slows further progress in using AI across all stages of scientific research.
- The authors developed a taxonomy that categorizes AI applications into five major tasks across the research cycle: scientific understanding, academic surveying, scientific discovery, academic writing, and academic peer review.
- They formally defined AI4Research and distinguished it from the narrower AI4Science, while providing a rigorous component-level framework for understanding AI's role at each stage.
- For each component, they conducted an extensive literature review to synthesize current methods, pioneering work, and empirical summaries, and they also organized practical resources such as tools, datasets, and benchmarks.
Paper links
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