2026科学智能（上）_科研人员_AI_应用现状调查报告_17页_3mb

报告摘要

AI for Science: Summary of the State of AI Use Among Researchers

Core Content

This report, published in 2026, presents an in-depth analysis of AI adoption and usage among researchers globally, based on a survey conducted by Springer Nature, Fudan University, and the Shanghai Academy of AI for Science. It explores how AI is integrated into the research workflow, the role of different AI tools, and the funding and trust dynamics surrounding their use.

Main Findings

1. AI Adoption and Penetration

AI is most frequently used for information- and text-intensive tasks, such as literature search and discovery and improving or editing papers, and least used for judgment-based, collaborative, or administrative tasks.
Younger researchers tend to use AI more frequently than their senior colleagues, especially for content discovery and paper editing.
The use of AI is uneven across the world, with East Asian countries (particularly China, Japan, and South Korea) showing significantly higher rates of AI use compared to other regions.
Institutional funding for AI tools is rare, with most researchers using free or self-funded tools.

2. AI Tools in the Research Workflow

General-purpose large language models (LLMs) dominate AI usage, accounting for 75.9% of all mentions.
ChatGPT is the most commonly used tool, followed by Gemini, Claude, and DeepSeek.
DeepSeek is used much more frequently in China (48.8%) than in other countries (usually below 10%).
Microsoft Copilot is the only tool with predominant institutional funding (48.5%), likely due to its integration with enterprise software.
Free access is most common for DeepSeek (93.9%), while personal funding is widespread, especially in hospitals, self-employed, and academia.

3. Funding for AI Tools

Institutional funding accounts for only 11.1% of AI use, while free tools and personal funding make up 45.4% and 41.3%, respectively.
Personal funding is the most common, suggesting that researchers perceive high value in AI tools, even if they are not institutionally supported.
Unclear funding sources account for 2.2% of responses, indicating a lack of transparency in how access is financed.

4. Heavy AI Users for Content Discovery

Approximately 5.7% of researchers rely primarily or exclusively on AI for content discovery, a group referred to as 'heavy users'.
East Asian countries have twice the rate of heavy users compared to the global average.
Younger researchers are more likely to be heavy users than senior researchers.
Personal payment is more strongly associated with heavy use than institutional funding.

5. AI Satisfaction and Concerns

65.2% of researchers are satisfied or very satisfied with AI tools for content discovery.
Satisfaction is driven by AI’s ability to identify relevant research and provide supporting references.
Major concerns include accuracy and hallucination, loss of critical thinking, and research integrity.
Productivity gains and literature discovery are the most frequently cited hopes for AI in research.

Key Insights

AI is seen as a supportive tool, not a replacement for human judgment.
Trust remains limited due to concerns about accuracy and data security.
Regional differences are significant, with East Asia leading in AI adoption.
Institutional support is still underdeveloped, and access to advanced tools depends largely on personal funding.
General-purpose models are the primary entry point for AI use, while specialized tools are still relevant in specific contexts.

Conclusion

The report highlights a growing reliance on AI in research, particularly in literature search and paper editing, with East Asian researchers being the most active. While AI is widely used and perceived as beneficial, trust issues persist, especially regarding accuracy and integrity. The funding landscape is dominated by free and self-funded tools, with institutional support still playing a limited role. The findings suggest a gradual shift toward the use of general-purpose AI models, but specialized tools will continue to have a complementary role in research workflows.