Research on optimization of the scholarship of teaching and learning in clinical medical colleges using artificial intelligence

Deng Hongmei; Zhang Hong; Liu Wenli; Gao Jing; Zhu Jing

doi:10.3760/cma.j.cn116021-20250728-02210

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Research on optimization of the scholarship of teaching and learning in clinical medical colleges using artificial intelligence

Digital Intelligence MedicalEducation | 更新时间：2026-02-13

- Research on optimization of the scholarship of teaching and learning in clinical medical colleges using artificial intelligence
- Chinese Journal of Medical Education Research Vol. 25, Issue 1, Pages: 37-43(2026)
- 作者机构：
  
  重庆医科大学儿科学院教务处，重庆 401122
- 作者简介：
  
  Zhu Jing, Email: 1686598427@qq.com
- 基金信息：
  
  Chongqing Higher Education Teaching Reform Research Project(233154)
- DOI：10.3760/cma.j.cn116021-20250728-02210
  CLC： R-05
- Received：28 July 2025，
  
  Published：20 January 2026
- 稿件说明：
移动端阅览
邓红梅, 张虹, 刘文莉, 等. 运用AI探索临床医学院教学学术制度的优化研究[J]. 中华医学教育探索杂志, 2026, 25(1): 37-43.

Deng Hongmei, Zhang Hong, Liu Wenli, et al. Research on optimization of the scholarship of teaching and learning in clinical medical colleges using artificial intelligence[J]. Chinese Journal of Medical Education Research, 2026, 25(1): 37-43.
邓红梅, 张虹, 刘文莉, 等. 运用AI探索临床医学院教学学术制度的优化研究[J]. 中华医学教育探索杂志, 2026, 25(1): 37-43. DOI： 10.3760/cma.j.cn116021-20250728-02210.

Deng Hongmei, Zhang Hong, Liu Wenli, et al. Research on optimization of the scholarship of teaching and learning in clinical medical colleges using artificial intelligence[J]. Chinese Journal of Medical Education Research, 2026, 25(1): 37-43. DOI： 10.3760/cma.j.cn116021-20250728-02210.

摘要

目的

在数字化、智能化驱动医学教育高质量发展的背景下，以教学学术理念为指导，探索利用人工智能（artificial intelligence，AI）技术系统优化临床医学院教学管理制度的路径，为构建面向卓越医学人才培养的教学管理新模式提供实践样本。

方法

以修订《儿科学院教学管理规章制度（2022版）》为例，依托DeepSeek与Kimi等AI工具，采用教务处AI初筛—教学秘书预处理—教研室主任审核—学术委员会终审的技术路线，对制度文本进行智能对比与深度剖析，并基于戴明循环、目标管理和权变理论等形成《儿科学院教学管理规章制度（2025版）》。

结果

经AI辅助优化后的制度体系实现了3项突破：①将“教学研究”确立为制度核心，建立教学学术成果与职称晋升的强关联机制，促进教师发展；②以学生成长为中心，本制度重构了“六体系PDCA[计划（plan）、执行（do）、检查（check）、调整（action）]循环”“六层级管理网格”及“六化专业支撑框架”，构建了一套精密协作、相互赋能的三维教学管理模式；③推进数字化管理平台建设，实现质控执行监测、动态更新和反馈改进的智能闭环。

结论

AI辅助的制度重构不仅显著提升了教学管理的科学化、精细化、智能化水平，更为临床医学院在落实教学学术制度的进程中提供了可复制、可推广的“儿科方案”。

Abstract

Objective

In the context of digitalization and intelligence driving the high-quality development of medical education

and guided by the concept of scholarship of teaching and learning

this study aims to explore the path of systematically optimizing the teaching management system in clinical medical colleges using artificial intelligence (AI) technology

and provide a practical model for constructing a new teaching management paradigm oriented towards cultivating outstanding medical talents.

Methods

In a case study

AI tools such as DeepSeek and Kimi were used in the revision of the Pediatric College Teaching Management Regulations (2022 Edition). A technical workflow was implemented

involving preliminary AI screening by the Academic Affairs Office

pre-processing by teaching secretaries

review by department heads

and final approval by the Academic Committee. This process facilitated intelligent comparison and in-depth analysis of the regulation text. Based on the Deming cycle (plan-do-check-act

PDCA)

objective management

and contingency theory

the Pediatric College Teaching Management Regulations (2025 Edition) was formulated.

Results

The AI-assisted optimization achieved three institutional breakthroughs. ①Teaching research was established as the institutional core and a strong linkage between teaching academic achievements and professional title promotion was created to foster faculty development. ②Centered on student growth

the system restructured the six-system PDCA cycle

six-level management grid

and six-element professional support framework

constructing a precisely coordinated and mutually empowering three-dimensional teaching management model. ③The development of a digital management platform was advanced to achieve an intelligent closed loop for monitoring quality control implementation as well as enabling dynamic updates and feedback-driven improvement.

Conclusions

AI-assisted institutional restructuring not only significantly enhanced the scientific rigor

sophistication

and intelligence of teaching management but also provided a replicable and scalable pediatric solution for clinical medical colleges in the process of implementing scholarship of teaching and learning systems.

关键词

Keywords

references

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