[目的/意义]在“AI for Science”逐渐被视为科学研究“第五范式”的背景下，人工智能（AI）正对信息资源管理（IRM）领域的研究过程与学科范畴带来深远影响。然而，目前尚缺乏从学科全局出发，基于系统性理论工具对AI在IRM领域的应用与影响进行的综合性考察。文章基于I-model理论，探索AI赋能IRM研究的关键要素、作用机理及未来挑战，旨在为学科研究与实践提供新思路。[方法/过程]通过文献调研与典型案例分析，总结人工智能驱动信息资源管理（AI4IRM）的核心内容与关键路径；从I-model理论的“信息—技术—人员—组织/社会”四个维度，解析AI在信息检索、知识发现与管理、数据处理与分析等具体场景中的创新应用；对面向未来的发展趋势和潜在风险进行全面审视。[结果/结论]AI在提升信息分析精度、决策支持效率以及知识服务能力方面展现出巨大潜力，但在大数据质量、算法偏见、隐私与伦理规范等层面仍存在现实挑战。I-model理论有助于理解AI驱动下IRM领域的动态关联与复杂性，为进一步推进跨学科交叉研究与科学决策提供了理论支撑和实践参考。

[Purpose/significance] As“AI for Science”is increasingly viewed as the“fifth paradigm”of scientific re⁃search, Artificial Intelligence (AI) has begun to exert a profound impact on both the research processes and disciplinary boundaries of Information Resources Management (IRM). However, a comprehensive examination of AI’s applications and influences in IRM, from a holistic disciplinary perspective using systematic theoretical tools, remains lacking.Drawing upon the I-model theory, this paper investigates the key elements, underlying mechanisms, and future chal⁃lenges of AI-empowered IRM research, aiming to offer fresh perspectives for both academic inquiry and practice. [Method/process] Employing a literature review and representative case analyses, this study summarizes the core com⁃ponents and key pathways of AI-driven Information Resources Management (AI4IRM). It then utilizes the four dimen⁃sions of the I-model—information, technology, people, and organization/society—to analyze AI’s innovative applica⁃
tions in tasks such as information retrieval, knowledge discovery and management, as well as data processing and analy⁃sis. The paper also provides a comprehensive review of forthcoming trends and potential risks. [Result/conclusion]The findings reveal that AI holds substantial promise in enhancing information analysis accuracy, decision-support effi⁃ciency, and knowledge service capabilities. Nevertheless, significant challenges persist, particularly regarding data quality in large-scale datasets, algorithmic bias, privacy concerns, and ethical regulations. By illuminating the dynamic relationships and complexities inherent in AI-driven IRM, the I-model theory furnishes theoretical and practical foun⁃dations for advancing interdisciplinary research and informed decision-making in this evolving field.