Abstract: The AI-driven new paradigm of scientific research is becoming a crucial driver for China to achieve technological competitive advantage and emerge as a leading science and technology powerhouse. With the development of the new generation of AI technology, the growing importance of AI in driving technological advancements and fostering interdisciplinary collaboration is gaining increasing attention. Many countries around the world have emphasized the importance of AI in promoting scientific exploration and have strengthened policy deployment and guidance across various domains, including establishing specialized research institutions, providing financial support, fostering talent development, leveraging data resources, innovating in technology, and collaborating internationally. The new paradigm of scientific research driven by artificial general intelligence has catalyzed transformations in research organizational patterns through the empowerment of large-scale AI models. The AI-driven new paradigm of scientific research represents a transformation of traditional research methods and processes, creating a knowledge-centered system with diversified stakeholder participation and a symbiotic collaboration between humans and machines. While the significance of adopting AI-driven research paradigms is widely acknowledged, existing studies have not thoroughly explored their theoretical foundations from epistemological and methodological perspectives. This paper investigates the AI-driven new research paradigm from a dual perspective of ‘Knowledge Evolution Theory’ and ‘Paradigm Theory’, systematically elucidating the main characteristics of the new research paradigm, and building a research framework for the AI-driven scientific paradigm. Knowledge evolution theory focuses on the specific mechanisms, evolutionary laws and influencing factors of knowledge growth and evolution. Paradigm Theory research on a collection of rule systems universally adopted by the scientific community to ensure efficient and orderly operation of research activities. Building on this framework, the paper conducts a multi-case analysis of the exploratory application of the AI-driven research paradigm. The study reveals that the AI-driven research paradigm encompasses elements such as research tools, research organization models, diverse application scenarios, and governance of research applications. Specifically, AI research tools optimize traditional research processes through substitution effects, enhancement effects, and autonomous effects. The AI-empowered research platform organization model is transforming the infrastructure, organizational structure, collaboration mechanisms, and talent composition of existing research paradigms. Multidisciplinary research bottlenecks provide training and iterative application scenarios for large models. Simultaneously, governance of AI research applications under the new paradigm needs to be refined based on the technological, content, and social attributes of AI. The study outlines the policy implications for developing a new AI-driven paradigm in scientific research based on its findings. First, it is essential to establish a policy framework for the "AI-driven new paradigm of scientific research". Second, there is a need to enhance database infrastructure, advance computing capabilities, and foster the development of basic models for artificial general intelligence. Concurrently, efforts should focus on enhancing decentralized large-scale model platforms and other novel scientific research models that align with the integration of artificial general intelligence applications. Finally, attention must be focused on the governance of AI applications in scientific research. This study offers theoretical support and empirical insights for the coordinated formulation and implementation of the ‘AI-driven new paradigm of scientific research’ policy.

摘要： 人工智能驱动的科研新范式正在成为我国形成科技竞争优势、实现科技强国的关键驱动力。人工智能驱动的科研新范式是改变传统的科研方法和过程，形成以知识为中心，多元化主体参与、人机协同共生的科研规则体系的集合。文章从“知识进化论”和“范式理论”的双元视角来探究人工智能驱动的科研新范式，从认识论和方法论层面系统阐述了科研新范式的主要特征，构建了通用人工智能驱动科研新范式的研究框架。在此基础上对人工智能驱动科研新范式的探索应用进行多案例分析。研究表明人工智能驱动的科研新范式主要包括科研工具、科研组织模式、多元应用场景以及科研应用治理等要素。具体来看，人工智能科研工具通过替代效应、增强效应和自主效应优化传统科研过程；人工智能赋能的科研平台组织模式正变革已有科研范式的基础设施、组织结构、协作机制与人才结构；多学科领域面临的科研瓶颈为大模型提供了训练迭代的应用场景；同时，要基于人工智能的技术属性、内容属性与社会属性完善新范式下人工智能科研应用治理。研究为我国统筹制定和实施“人工智能驱动的科研新范式”政策提供理论支撑和经验借鉴。