AI-Driven Education Quality Improvement in Private Universities

Abstract

Against the dual tides of higher education massification and digitalization, private higher education institutions have emerged as a key driver of expansion in China’s higher education system. However, they have long been under pressure in terms of quality assurance due to three core challenges: intense competition for student enrollment, resource constraints, and faculty turnover. The Outline for Building a Powerful Education Nation (2024–2035) and the National Education Digitalization Strategy explicitly identify educational digitalization as a critical breakthrough for developing a powerful education nation, while emphasizing the application of emerging technologies such as AI in education. This provides both a policy window and technical leverage for private higher education institutions to address their structural dilemmas. Focusing on the role of AI technology in enhancing teaching quality at private higher education institutions across scenarios including personalized teaching, intelligent assessment and virtual experiments, this paper proposes a “trinity” quality improvement pathway integrating “differentiated instruction, affective feedback, and precision evaluation.” It further offers actionable strategies for private higher education institutions to achieve high-quality development through the application of AI.