Exploration and Practice of Artificial Intelligence Empowering Case-based Teaching in Biochemistry and Molecular Biology
10.16476/j.pibb.2025.0224
- VernacularTitle:人工智能赋能生物化学与分子生物学案例教学的探索与实践
- Author:
Ying-Lu HU
1
;
Yi-Chen LIN
1
;
Jun-Ming GUO
1
;
Xiao-Dan MENG
1
Author Information
1. School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo 315211, China
- Publication Type:Journal Article
- Keywords:
artificial intelligence;
case-based learning;
biochemistry and molecular biology;
teaching efficiency;
research innovation capability
- From:
Progress in Biochemistry and Biophysics
2025;52(8):2173-2184
- CountryChina
- Language:Chinese
-
Abstract:
In recent years, the deep integration of artificial intelligence (AI) into medical education has created new opportunities for teaching Biochemistry and Molecular Biology, while also offering innovative solutions to the pedagogical challenges associated with protein structure and function. Focusing on the case of anaplastic lymphoma kinase (ALK) gene mutations in non-small-cell lung cancer (NSCLC), this study integrates AI into case-based learning (CBL) to develop an AI-CBL hybrid teaching model. This model features an intelligent case-generation system that dynamically constructs ALK mutation scenarios using real-world clinical data, closely linking molecular biology concepts with clinical applications. It incorporates AI-powered protein structure prediction tools to accurately visualize the three-dimensional structures of both wild-type and mutant ALK proteins, dynamically simulating functional abnormalities resulting from conformational changes. Additionally, a virtual simulation platform replicates the ALK gene detection workflow, bridging theoretical knowledge with practical skills. As a result, a multidimensional teaching system is established—driven by clinical cases and integrating molecular structural analysis with experimental validation. Teaching outcomes indicate that the three-dimensional visualization, dynamic interactivity, and intelligent analytical capabilities provided by AI significantly enhance students’ understanding of molecular mechanisms, classroom engagement, and capacity for innovative research. This model establishes a coherent training pathway linking “fundamental theory-scientific research thinking-clinical practice”, offering an effective approach to addressing teaching challenges and advancing the intelligent transformation of medical education.
