中国实用口腔科杂志 ›› 2026, Vol. 19 ›› Issue (5): 596-599.DOI: 10.19538/j.kq.2026.05.011

• 口腔医学教育研究专栏 • 上一篇    下一篇

人工智能赋能基于知识图谱的口腔正畸学智慧课程建设与成效

李振霞,周林曦,经    典,郑小雯,刘    超,赵    宁,房    兵,夏伦果   

  1. 上海交通大学医学院附属第九人民医院口腔正畸科,上海交通大学口腔医学院,国家口腔医学中心,口腔疾病国家临床医学研究中心,上海市口腔医学重点实验室,上海市口腔医学研究所,上海 200011
  • 出版日期:2026-09-30 发布日期:2026-09-30
  • 通讯作者: 夏伦果,房兵
  • 基金资助:
    国家自然科学基金(82571126);上海交通大学医学院本科核心课程AI+课程项目(DGD-2024-15);上海交通大学医学院附属第九人民医院临床教学激励计划团队项目(JXTD-2025-8);上海交通大学医学院本科荣誉课程项目(DGD-2026-5)

  • Online:2026-09-30 Published:2026-09-30

摘要: 针对口腔正畸学本科教学中理论知识庞杂抽象、与实践脱节、学时有限、缺乏个性化教学等突出困境,上海交通大学口腔医学院正畸教研室构建了人工智能(artificial intelligence,AI)赋能基于知识图谱的口腔正畸学智慧课程。其以错𬌗畸形九大重点病种为主线重塑课程内容,构建了包含能力层、问题层、知识层和资源层的四维知识图谱,实现知识可视化与结构关联。在此基础上,其开发了AI智能学伴、个性化学习路径推荐、虚拟仿真实操平台、智慧问诊智能体及智能助教等模块,实现实时答疑、精准测练、自适应学习、模拟问诊及全流程教师减负。教学应用采用线上线下混合式模式,结合翻转课堂与多维过程性智能评估。初步应用显示,学生的学习兴趣、教学满意度及病例综合分析成绩均明显提高,并在创新创业项目和论文发表等方面取得明显进步。文章介绍的AI赋能基于知识图谱的智慧课程建设,能够有效破解口腔正畸学教学难点,可促进学生临床思维、实践能力和创新素养的全面发展,为医学教育改革提供了可复用的实践范式。

关键词: 智慧课程, 知识图谱, 口腔正畸学, 教学改革

Abstract: To address the prominent challenges in undergraduate orthodontics education,such as the fragmented and abstract nature of theoretical knowledge,disconnection from clinical practice,limited class hours,and lack of personalized instruction,the Department of Orthodontics,College of Stomatology,Shanghai Jiao Tong University has developed an artificial intelligence(AI)-empowered smart course of orthodontics based on a knowledge graph. Centered around nine major types of malocclusion,the course content has been restructured to construct a four-dimensional knowledge graph comprising a competence layer,a problem layer,a knowledge layer,and a resource layer,enabling knowledge visualization and structured associations. On this basis,several modules were developed,including an AI intelligent learning companion,personalized learning path recommendation,virtual simulation practice platform,smart diagnostic inquiry agent,and intelligent teaching assistant,enabling real-time question-answering,precision assessment and practice,adaptive learning,simulated clinical interviews,and full-process workload reduction for instructors. The teaching model adopts a blended online-offline approach,combined with flipped classrooms and multi-dimensional process-based intelligent assessment. Preliminary applications show significant improvement in student learning interest,teaching satisfaction,and comprehensive case analysis performance,as well as notable progress in innovation and entrepreneurship projects,and paper publications. This construction of AI-empowered knowledge graph-based smart course effectively resolves the teaching difficulties in orthodontics,promotes the comprehensive development of students' clinical reasoning,practical abilities,and innovative competencies,and provides a replicable practical paradigm for medical education reform.

Key words: smart course, knowledge graph, orthodontics, teaching reform

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