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This paper presents a systematic review of how artificial intelligence (AI) contributes to accelerating and improving the research process. Research using AI is divided into three major categories: hypothesis formation, hypothesis testing, and paper publication. Hypothesis formation involves knowledge synthesis and hypothesis generation; hypothesis testing involves scientific claim verification, theorem proof, and experimental verification; and paper publication involves paper writing and peer review. The paper presents current challenges and future research directions in each domain, and provides a comprehensive overview of existing benchmarks and tools that support AI integration in various fields. The related materials are made publicly available through the GitHub repository ( https://github.com/zkzhou126/AI-for-Research) .
Takeaways, Limitations
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Takeaways:
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Provides a systematic review of how AI can accelerate and enhance research.
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Presenting AI utilization strategies for each stage of hypothesis setting, verification, and paper publication.
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Provides a comprehensive overview of AI-based research tools and benchmarks.
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Providing introductory materials on AI-based research for beginners.
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Providing publicly accessible resources.
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Limitations:
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Further analysis is needed on the practical utility and generalizability of the AI-based tools and benchmarks presented in the paper.
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Lack of sufficient discussion of the ethical implications and bias issues of AI.
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Possibility of bias in research review of specific research fields.
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The presentation of future research directions is somewhat comprehensive and lacks specific research topics.
