Artificial Intelligence Drives the Governance Reform of Graduate Education: Theoretical Logic, Practical Dilemma and Innovation Breakthrough

Abstract

 In the digital age, artificial intelligence has deeply integrated into the governance of graduate education, providing new impetus for optimizing training models. However, in practice, challenges such as data governance barriers, technical ethical risks, and insufficient adaptability of stakeholders are encountered. For instance, data silos hinder precise training, and technological dependence undermines educational value. This study, grounded in constructivist learning theory, technology acceptance model, collaborative governance theory, and risk society theory, constructs an analytical framework and proposes innovative pathways: driving scenario innovation through technology integration, building adaptive platforms and data middleware; reconstructing a multi-stakeholder collaborative governance model to strengthen digital literacy and ethical norms; creating a data-driven governance loop. Future efforts should balance technological empowerment with humanistic care, promoting the transition of governance towards "intelligent coexistence," thereby facilitating high-quality educational development.