<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">ETR</journal-id><journal-title-group><journal-title>Educational Theory and Research</journal-title></journal-title-group><issn>2995-3448</issn><eissn>2995-3456</eissn><publisher><publisher-name>Art and Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/ETR.2026170025</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>知识图谱与人工智能融合驱动的高职本科护理《正常人体结构》课程重构与实践研究</title><url>https://artdesignp.com/journal/ETR/4/17/10.61369/ETR.2026170025</url><author>傅晓艳,王文倩,邵寒雨,潘意,蒋进展</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>4</volume><issue>17</issue><history><date date-type="pub"><published-time>2026-04-24</published-time></date></history><abstract>随着&amp;ldquo;健康中国&amp;rdquo;战略的深入推进与医疗技术的高速迭代，社会对高层次、复合型护理人才的需求日益凸显。高职本科护理教育作为应用型护理人才培养的关键环节，其课程教学的系统性改革势在必行。《正常人体结构》作为护理专业的基础核心课程，其教学质量直接影响学生后续临床课程的学习成效。然而，传统教学模式长期面临知识体系碎片化、内容抽象难懂、理论与护理实践脱节等现实困境。为破解上述难题，本研究深度融合知识图谱与人工智能（AI）技术，构建了面向高职本科护理专业的《正常人体结构》智慧课程新范式。文章系统阐述了课程构建的理论基础，创新设计了&amp;ldquo;知识图谱构建&amp;mdash;AI 技术赋能&amp;mdash;教学模式重构&amp;mdash;评价机制改革&amp;rdquo;四位一体的建设路径，并详细介绍了教学实践过程。通过对比实验与问卷调查，研究初步验证：该模式能够显著提升学生的知识结构化水平、空间思维能力、自主学习能力以及临床关联能力，为高职本科护理专业基础课程的数字化、智能化转型提供了可复制、可推广的实践范式。</abstract><keywords>知识图谱,人工智能,高职本科,护理教育,正常人体结构,课程重构,智慧教学</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] 国务院. 国务院关于印发国家职业教育改革实施方案的通知[Z]. 2019.[2] 姜安丽. 护理学本科专业课程体系与教学内容改革研究[J]. 中华护理教育，2010,7(5): 193-196.[3] Khot, Z., Quinlan, K., Norman, G. R., &amp;amp; Wainman, B. (2019). The relative effectiveness of computer-based and traditional resources for education in anatomy: A systematic review and meta-analysis. Anatomical Sciences Education, 12(2), 174-186. https://doi.org/10.1002/ase.1803[4] 王萍，陈丽，郑勤华. 教育知识图谱的概念内涵与应用框架[J]. 中国电化教育，2019(7): 89-96.[5] 黄荣怀，刘德建，徐晶晶，等. 人工智能如何助力教育发展？&amp;mdash;&amp;mdash; 《北京共识》解读与思考[J]. 现代远程教育研究，2020, 32(1): 3-11.[6] Piaget, J. (1970). Genetic epistemology. Columbia University Press.[7] Sweller, J. (2011). Cognitive load theory. Psychology of learning and motivation, 55, 37-76. https://doi.org/10.1016/B978-0-12-387691-1.00002-8[8] Lave, J., &amp;amp; Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.[9] Eppler, M. J., &amp;amp; Burkhard, R. A. (2004). Knowledge visualization. Universit&amp;agrave; della Svizzera italiana.[10] 李青，赵蔚，姜强. 基于知识图谱的自适应学习路径推荐研究[J]. 电化教育研究，2017, 38(11): 82-88.[11] Corbett, A. T., &amp;amp; Anderson, J. R. (1995). Knowledge tracing: Modeling the acquisition of procedural knowledge. User Modeling and User-Adapted Interaction, 4(4), 253-278. https://doi.org/10.1007/BF01099821[12] 王陇德. 正常人体结构[M]. 北京：人民卫生出版社，2018.[13] Devlin, J., Chang, M. W., Lee, K., &amp;amp; Toutanova, K. (2019). BERT: Pretraining of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171-4186. https://doi.org/10.18653/v1/N19-1423</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
