| 温芳芳,段月漪,赵悦名.AI与信息资源管理学的融合图景与演进路径[J].数字图书馆论坛,2025,21(11):52~61 |
| AI与信息资源管理学的融合图景与演进路径 |
| Integration Landscape and Evolution Path of AI and Information Resource Management |
| 投稿时间：2025-08-18 |
| DOI：10.3772/j.issn.1673-2286.2025.11.006 |
| 中文关键词: 人工智能；信息资源管理；AI4S |
| 英文关键词: Artificial Intelligence; Information Resource Management; AI4S |
| 基金项目:本研究得到河南省高等学校哲学社会科学创新团队支持计划“专利数据分析与科技创新管理”（编号：2024-CXTD-13）、河南省高等教育教学改革研究与实践项目（研究生教育类）“学科交叉融合背景下高校研究生培养机制变革与模式创新”（编号：2023SJGLX184Y）资助。 |
| 作者 | 单位 | | 温芳芳 | 河南科技大学图书馆 | | 段月漪 | 河南科技大学商学院 | | 赵悦名 | 河南科技大学商学院 |
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| 摘要点击次数: 5 |
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| 中文摘要: |
| AI4S浪潮下AI赋能信息资源管理（IRM）学科正引发研究范式的系统性变革，描绘IRM的AI研究图景并梳理AI与IRM融合的演进脉络，有助于增强学界对学科研究范式变革的系统性认识和把握。本研究以2015—2024年IRM学科20种CSSCI期刊上发表的AI主题论文为样本数据，考察AI词汇在IRM研究中的分布结构及热度变化，基于BERT模型的主题分析揭示AI与IRM融合的研究场景与演进脉络。研究发现，AI已广泛融入IRM，引发了研究方法的变革和研究内容的拓展。AI与IRM的融合场景呈现多元化特征，但在该学科的核心区域更能实现持久深度的融合，其中技术迭代驱动与服务场景重塑是两大核心融合主线。与此同时，IRM学科并非单纯的技术使用者，而是通过其独特的专业优势成为了AI知识生态体系的构建者、优化者和赋能者。IRM研究图景整体呈现出AI4IRM与IRM4AI双向驱动的特征。 |
| 英文摘要: |
| Under the AI4S wave, AI empowerment in the discipline of Information Resource Management (IRM) is triggering a systematic transformation of its research paradigm. Mapping the AI research landscape of IRM and sorting out the evolutionary context of the integration between AI and IRM are conducive to enhancing the academic community’s systematic understanding and grasp of the disciplinary research paradigm transformation. This study takes AI-related papers published in 20 CSSCI journals of the IRM discipline from 2015 to 2024 as sample data, examines the distribution structure and popularity dynamics of AI-related terminology in IRM research, and reveals the research scenarios and evolutionary contexts of the integration between AI and IRM through topic analysis based on the BERT model. The findings indicate that AI has been extensively integrated into IRM, triggering innovations in research methods and expansions in research content. The integration scenarios of AI and IRM exhibit diversified characteristics, yet more enduring and in-depth integration is achieved in the core domains of the discipline, and technology iteration-driven development and service scenario restructuring constitute the two core integration threads. Meanwhile, the IRM discipline is not merely a passive adopter of technology; instead, leveraging its unique professional strengths, it has emerged as a builder, optimizer, and enabler of the AI knowledge ecosystem. The overall research landscape of IRM presents the characteristic of bidirectional driving between AI4IRM and IRM4AI. |
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