AI-assisted assessment of higher education quality: A visual analytical approach
Chun Yu He, Y. Huang, Haolun Lan, Jing He, Xiaoliang Fan, Degan Zhang, Juncong Lin, Minghong Liao, Cheng Wang · Visual Informatics · 2026
The reputation of universities has drawn increasing attention in recent years, especially with the emergence of various rankings. However, despite advances in big data technologies that facilitate data collection and analysis, accurately defining and balancing factors related to university reputation and educational quality remains complex and tedious. Moreover, current educational assessment methods exhibit notable differences and controversies. In this paper, we present Iva , a human-in-the-loop I ntelligent V isual A ssessment system for higher education quality. This system utilizes large language models to analyze extensive multi-modal educational data, with visualization techniques incorporated to enable multi-scale exploration and interaction. Our extensive evaluations, including a carefully-designed user study and expert interviews, demonstrate the system’s potential value and provide insights for future improvements.