Chen Xiaoyu, Wang Yun, Wang Chunyue
[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.
