智库理论与实践2026，Vol.11Issue(4)：25-35,11.DOI:10.3724/j.issn.2096-1634.20260506.0003
AI4S赋能信息资源管理:演进、方法与实践
AI4S-Enabled Information Resource Management:Evolution,Methods,and Practices
摘要
Abstract
[Purpose/Significance]Artificial Intelligence for Science(AI4S)is driving the transformation of scientific research from the traditional hypothesis-driven paradigm toward a new paradigm jointly driven by data and models.Against this backdrop,it is necessary to systematically examine the evolutionary trajectory,method-ological system,practical scenarios,and governance challenges of AI4S-enabled information resource manage-ment.[Method/Process]AI4S-enabled information resource management has made significant progress at both the methodological and practical levels.Large language models are reshaping scientific productivity in a wide range of scenarios,including retrieval-augmented generation,multi-agent collaboration,multimodal semantic alignment,knowledge graph integration,scientific literature and intelligence acquisition,peer review,scholarly publishing,and cross-lingual scientific communication.However,issues such as model hallucination and knowl-edge-base contamination,algorithmic manipulation and systemic bias,copyright disputes over AIGC,data monop-olies,and information cocoons have also created new tensions in academic ethics and research governance.[Result/Conclusion]Accordingly,the discipline of information resource management should proactively shift from being a supporting discipline for literature services to becoming an infrastructure discipline for scientific innovation.By building open and trustworthy scientific data foundations,improving academic norms for human-AI collaboration,and strengthening algorithmic regulation and content provenance mechanisms,information resource management should assume new responsibilities in the AI era as a provider of high-quality data,a governor of the knowledge lifecycle,and a guardian of academic ethics.关键词
AI for Science/科研范式/信息资源管理/大语言模型Key words
AI for science/research paradigm/information resource management/large language model分类
社会科学引用本文复制引用
黄水清,许乾坤,王东波,王林旭..AI4S赋能信息资源管理:演进、方法与实践[J].智库理论与实践,2026,11(4):25-35,11.基金项目
本文系国家社会科学基金重大项目"先秦诸子典籍知识库建设及词典编纂"(项目编号:22&ZD262)研究成果之一. （项目编号:22&ZD262）
