Universities worldwide are rapidly advancing artificial intelligence (AI) to reconcile the perennial tension between educational quality, scalability, and personalization.This paper analyzes 13 studies and case analyses, focusing on AI applications in higher education: (1) adaptive learning systems, (2) real-time instructional tools, and (3) automated assessment feedback. These technologies improve course completion, reduce instructor preparation time, and enhance student writing.Nonetheless, they also introduce fresh risks related to academic integrity, data privacy, shifting teacherstudent roles, and equity in resource distribution. This paper proposes a two-tier, triadic governance framework to address the intertwined challenges of educational ethics and quality, user adaptation, and the digital divide posed by generative AI. Universities should adopt honor codes, AI evaluation guidelines, secure data sandboxes, and AI-literacy curricula. Governments and industry must provide regulatory standards, resources, and multilingual large-language-model infrastructure. AI can significantly drive equity and excellence in higher education when internal self-regulation aligns with external policies.
Sichen Wang (Wed,) studied this question.
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