Yingying Huang, Renfang Wang, Haoliang Hu, Mengying Ding, Hong Qiu
College of Big Data and Software Engineering, Zhejiang Wanli University;College of Foreign Languages, Zhejiang Wanli University
Abstract：
As artificial intelligence deeply empowers various industries, cultivating high- level talent in Intelligent
Science and Technology (IST) has become a critical challenge. To clarify the talent cultivation approaches for
postgraduate programs in IST at Chinese universities, this study adopts data analysis methods. It systematically
reviews 27 peer- reviewed articles, conducts an K-means clustering based analysis of the publicly available IST
postgraduate curricula from 9 Chinese universities, and extracts the core perspectives of these universities on
talent cultivation in this field. Specifically, Chinese universities have reached a consensus on four core dimensions
for high-level talent cultivation in IST: curriculum system construction, teaching method innovation, practical
teaching mechanism development, and industry-academia-research integration. Curriculum clustering analysis
shows that IST curricula should focus on three core categories: mathematics-related courses, AI professional
basic courses, and interdisciplinary application courses. Further analysis reveals strong internal cohesion in
professional basic courses due to clear knowledge logic and relevance, while interdisciplinary application courses
suffer from fragmentation due to broad coverage and insufficient connection. All universities recognize the value
of practical modules for talent competence, but the lack of a unified standardized evaluation framework hinders
the measurement and optimization of practical effects. Future research should track graduate trajectories to
inform the optimization of interdisciplinary application courses, while simultaneously developing unified
assessment instruments to validate curriculum effectiveness.
Key Words：
intelligent science and technology; postgraduate education; interdisciplinary pedagogy; industry –academia integration; K-means clustering
