University Chemistry ›› 2025, Vol. 40 ›› Issue (3): 277-284.doi: 10.12461/PKU.DXHX202412104

Special Issue:

• Study and Reform of Chemical Education • Previous Articles     Next Articles

AI-Driven Biochemical Teaching Research: Predicting the Functional Effects of Gene Mutations

Ying Zhang, Fang Ge, Zhimin Luo   

  1. Jiangsu Key Laboratory of Smart Biomaterials and Theranostic Technology, State Key Laboratory of Flexible Electronics (LoFE), College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), School of Chemistry and Life Sciences, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
  • Received:2024-12-13 Accepted:2025-02-24 Published:2025-03-19
  • Contact: Fang Ge, Zhimin Luo E-mail:gfang0616@njupt.edu.cn;iamzmluo@njupt.edu.cn

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Abstract: Guided by the principle of “integrating science and education, collaboratively cultivating talent”, this paper explores the integration of Artificial Intelligence (AI) with biochemistry teaching and research. It examines the application of AI technology in the reform of biochemistry education, specifically through the development of a case study in biomedical engineering that predicts the functional effects of gene mutations using AI. The paper discusses the design and implementation of this AI-driven teaching case, focusing on the case’s background, curriculum design, teaching strategies, and evaluation of its impact. This approach aims to cultivate interdisciplinary thinking in students, enhancing their ability to integrate knowledge across fields.

Key words: Biochemistry, Case teaching, Gene mutation, Functional effect, Artificial intelligence