Xie, D.; Chen, F. Q.; Jiang, B. Y.; Xiao, Y. H.; Liu, L. N.; Dai, J. F. Reflections on the teaching reform of polymer chemistry: towards intelligent and data-driven approaches. Polym. Bull. (in Chinese), 2025, 38(5), 837–843.
Xie, D.; Chen, F. Q.; Jiang, B. Y.; Xiao, Y. H.; Liu, L. N.; Dai, J. F. Reflections on the teaching reform of polymer chemistry: towards intelligent and data-driven approaches. Polym. Bull. (in Chinese), 2025, 38(5), 837–843. DOI: 10.14028/j.cnki.1003-3726.2025.24.315.
With the development and deeper application of artificial intelligence (AI) technology
there is an increasing demand to explore AI-supported teaching and professional development. The in-depth implementation of AI-enabled education has become an important direction in the digital and intelligent transformation of education. As a core course in polymer materials and engineering
polymer chemistry can benefit from the use of knowledge graphs to teach intelligently and digitally. This approach addresses shortcomings of traditional teaching models
such as insufficient interactive teaching
a disconnect between theory and practice
and a limited variety of teaching methods and evaluation systems. This holds significant value for promoting the reform of polymer chemistry teaching and further enhancing the quality of talent cultivation.
Tao, L. ; Byrnes, J. ; Varshney, V. ; Li, Y . Machine learning strategies for the structure-property relationship of copolymers . iScience , 2022 , 25 ( 7 ), 104585 .
Kang, J. W. ; Choi, K. ; Jo, W. H. ; Hsu, S. L . Structure-property relationships of polyimides: a molecular simulation approach . Polymer , 1998 , 39 ( 26 ), 7079 – 7087 .
Cencer, M. M. ; Moore, J. S. ; Assary, R. S . Machine learning for polymeric materials: an introduction . Polym. Int. , 2022 , 71 ( 5 ), 537 – 542 .