Unlocking the Potential of AI Researchers in Scientific Discovery: What Is Missing?
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914051675652096 |
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| author | Yu, Hengjie Liu, Shuya Yang, Haiyun Yan, Yuping Qu, Maozhen Jin, Yaochu |
| author_facet | Yu, Hengjie Liu, Shuya Yang, Haiyun Yan, Yuping Qu, Maozhen Jin, Yaochu |
| contents | The potential of AI researchers in scientific discovery remains largely untapped. Over the past decade, AI for Science (AI4Science) publications in 145 Nature Index journals have increased fifteen-fold, yet they still account for less than 3% of the total publications. Drawing upon the Diffusion of Innovation theory, we project AI4Science's share of total publications to rise from 2.72% in 2024 to approximately 20% by 2050. Achieving this shift requires fully harnessing the potential of AI researchers, as nearly 95% of AI-driven research in these journals is led by experimental scientists. To facilitate this, we propose structured workflows and strategic interventions to position AI researchers at the forefront of scientific discovery. Specifically, we identify three critical pathways: equipping experimental scientists with accessible AI tools to amplify the impact of AI researchers, bridging cognitive and methodological gaps to enable more direct involvement in scientific discovery, and proactively fostering a thriving AI-driven scientific ecosystem. By addressing these challenges, we aim to empower AI researchers as key drivers of future scientific breakthroughs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_05822 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unlocking the Potential of AI Researchers in Scientific Discovery: What Is Missing? Yu, Hengjie Liu, Shuya Yang, Haiyun Yan, Yuping Qu, Maozhen Jin, Yaochu Computers and Society Emerging Technologies Human-Computer Interaction I.2.1; I.2.4; J.2; J.3; C.3 The potential of AI researchers in scientific discovery remains largely untapped. Over the past decade, AI for Science (AI4Science) publications in 145 Nature Index journals have increased fifteen-fold, yet they still account for less than 3% of the total publications. Drawing upon the Diffusion of Innovation theory, we project AI4Science's share of total publications to rise from 2.72% in 2024 to approximately 20% by 2050. Achieving this shift requires fully harnessing the potential of AI researchers, as nearly 95% of AI-driven research in these journals is led by experimental scientists. To facilitate this, we propose structured workflows and strategic interventions to position AI researchers at the forefront of scientific discovery. Specifically, we identify three critical pathways: equipping experimental scientists with accessible AI tools to amplify the impact of AI researchers, bridging cognitive and methodological gaps to enable more direct involvement in scientific discovery, and proactively fostering a thriving AI-driven scientific ecosystem. By addressing these challenges, we aim to empower AI researchers as key drivers of future scientific breakthroughs. |
| title | Unlocking the Potential of AI Researchers in Scientific Discovery: What Is Missing? |
| topic | Computers and Society Emerging Technologies Human-Computer Interaction I.2.1; I.2.4; J.2; J.3; C.3 |
| url | https://arxiv.org/abs/2503.05822 |