Mechanisms and Pathways for Enhancing Internal Audit Effectiveness in Universities through Artificial Intelligence
As artificial intelligence (AI) becomes increasingly embedded in university governance and auditing, a central concern in the intelligent transformation of internal auditing is how to leverage its technological advantages to improve audit effectiveness. This study examines the process through which AI enables internal auditing in universities, analyzes the mechanism by which audit effectiveness is generated, and develops an evaluation system comprising five dimensions and 17 indicators. The analytic hierarchy process (AHP) and fuzzy comprehensive evaluation are then applied to assess a case university. The results indicate that the university achieves an overall rating of good in AI-enabled internal audit effectiveness. Although it has established a certain level of digital infrastructure and capability for converting technological inputs into audit outcomes, further improvement is required in technological innovation, the in-depth application of intelligent tools, and data governance. Based on the weaknesses identified by the evaluation, this study proposes targeted improvement pathways. The findings provide a reference for universities seeking to advance intelligent internal auditing and enhance the effectiveness of AI applications.