Abstract
In the midst of the global wave of digitalization in higher education, data-intelligence-driven university governance has become an important lever for modernizing university governance systems and governance capacity. Currently, in the process of promoting data-intelligence-driven university governance, Chinese universities still face problems and challenges such as incomplete basic databases, data silos, lack of data literacy among data decision-makers and users, ineffective application of generative artificial intelligence, poor data quality, and the absence of evaluation mechanisms for data-driven governance. To address these issues, the article further identifies the constituent elements of the endogenous dynamics in data-intelligence-driven university governance, and then deduces the generation mechanism and evolution path of these endogenous dynamics, thereby establishing a logical framework for data-intelligence-driven university governance. The effectiveness of the endogenous dynamics is evaluated from four dimensions—governance efficiency, decision quality, subject satisfaction, and innovative development—thereby proposing optimization paths and policy recommendations to stimulate and strengthen these endogenous dynamics.
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