人工智能赋能地方高校内部审计实施路径研究——以J学院为例

Research on the Implementation Path of Internal Audit in Local Universities Empowered by Artificial Intelligence—Taking J College as an Example

摘要: 高校内部审计普遍存在业务数据分散割裂、审计作业效率低、风险识别精准度不足等问题。人工智能技术具有强大的数据整合和智能分析能力，可重构高校内部审计工作模式，有效破解传统内审工作的发展瓶颈。本文以地方高校J学院为研究案例，梳理其内部审计工作现状，剖析其存在的数据壁垒突出、专属审计平台缺位、AI技术应用空白、复合型审计人才紧缺、配套保障制度不完善等核心问题。基于人工智能审计数据层、算法层、应用层的协同运行逻辑，构建阶梯式智能审计建设体系。从平台搭建、数据治理、模型开发、人才培育、机制完善五个维度，提出适配地方高校发展实际的人工智能赋能内部审计实施路径。研究结果为地方高校内部审计数字化转型提供实践参考与路径借鉴。

Abstract: Internal audit in universities generally faces problems such as fragmented business data, low audit efficiency, and inaccurate risk identification. With powerful capabilities of data integration and intelligent analysis, artificial intelligence (AI) technology can reshape the working mode of university internal audit and effectively break the development bottlenecks of traditional internal audit. Taking J College, a local university, as the research case, this paper sorts out the current situation of its internal audit work and analyzes the core problems including prominent data barriers, the absence of dedicated audit platforms, blank application of AI technology, shortage of compound audit talents, and imperfect supporting guarantee systems. Based on the collaborative operation logic of the data layer, algorithm layer and application layer of AI-enabled audit, this paper constructs a stepped intelligent audit construction system. From five dimensions of platform construction, data governance, model development, talent cultivation and mechanism improvement, it proposes the implementation path of AI-enabled internal audit adapted to the actual development of local universities. The research findings provide practical references and path insights for the digital transformation of internal audit in local universities.