Opportunities, Challenges and Strategies for AI-Assisted Improvement of Quality Assurance in Higher Education: A Case Study of Lingnan University in Hong Kong, China

Abstract

This study focuses on Lingnan University (LU) in Hong Kong, China, deeply analyzing its quality assurance situation. Through the analysis of the 2019 audit report of LU by the Quality Assurance Council (QAC) under the University Grants Committee of Hong Kong, China, the strengths and limitations of the university in governance, programme quality assurance, programme delivery, and student participation and support services are revealed. The AI-assisted solutions discussed in this study are innovative but face limitations in feasibility and practicality. Therefore, alternative approaches that integrate AI and human expertise are proposed, such as developing a hybrid data analysis platform, establishing an External Advisory Board, formulating an AI-assisted e-learning strategic plan, and creating a dual-layered feedback system. These solutions aim to address issues like weak key performance indicators, insufficient external engagement, underdeveloped e-learning, and imperfect feedback mechanisms. Meanwhile, the study emphasizes the need to pay attention to ethical issues in AI applications, such as responsibility definition, data privacy, and over-reliance. By balancing AI and human decision-making, LU is expected to improve its quality assurance processes, enhance educational quality, and adapt to the digital transformation trend in higher education.