目的 探讨基于人工智能的线上学习平台在医学检验技术专业学生实习教学中的应用效果。方法 选取2022年6月至2024年6月在中山大学附属第一医院实习的医学检验技术专业42名学生作为研究对象,以简单随机法将其分为A组和B组,每组21名学生。本研究采用混合方法研究设计。定量研究部分采用交叉设计,A组先利用线上学习平台进行血细胞形态学学习,再进行显微镜学习;B组则相反。通过前测、平台学习后测和显微镜学习后测评估学习效果。采用配对t检验和独立样本t检验对数据进行统计分析。定性研究部分根据定量研究结果和观察情况对实习学生和指导教师进行访谈,并进行主题分析。结果 A组学生前测成绩为(60.10±10.44)分,B组学生前测成绩为(61.71±10.45)分,其差异无统计学意义(P=0.618)。A组学生线上平台学习后成绩[(83.71±7.75)分]高于前测成绩,显微镜学习后成绩[(93.52±4.29)分]高于线上平台学习后成绩;B组学生显微镜学习后成绩[(75.24±10.76)分]高于前测成绩,线上平台学习后成绩[(91.14±4.80)分]高于显微镜学习后成绩;A组学生线上平台与显微镜学习相结合方式的测试成绩高于B组学生显微镜学习后成绩:以上差异均具有统计学意义(均P<0.001)。实习学生认为线上平台学习资源丰富、学习高效且方便,满足自主学习需求。指导教师对该平台给予高度评价,认为显著提升了教学效率。结论 基于人工智能的线上学习平台可以提供方便且高效的学习环境,能够提升血细胞形态的学习效率,可以作为血细胞形态学习的有效辅助手段。
李俊勋
,
张帆
,
李润钊
,
程静
,
谭红霞
,
陈培松
,
黄彬
,
欧阳涓
,
吕万革
. 基于人工智能的线上学习平台在医学检验技术专业血细胞形态学实习教学中的应用[J]. 中华医学教育杂志, 2025
, 45(3)
: 204
-209
.
DOI: 10.3760/cma.j.cn115259-20241218-01329
Objective To explore the application of an artificial intelligence (AI)-based online learning platform in the clinical internship of medical laboratory technology students. Methods A total of 42 undergraduate medical laboratory technology students interning at the First Affiliated Hospital of Sun Yat-sen University from June 2022 to June 2024 were selected and randomly divided into two groups, A and B, with 21 students in each group. This study employed a mixed-methods research design. The quantitative research section adopted a crossover design, where Group A first learned hematological cell morphology using the online platform followed by microscope-based learning, and Group B followed the reverse order. Pre-tests, post-tests after online platform learning, and post-tests after microscope-based learning were conducted to assess learning outcomes. Paired t-tests and independent-sample t-tests were used for statistical analysis of the data. The qualitative research section involved interviews with the intern students and supervising teachers based on the quantitative research results and observations, followed by thematic analysis. Results The pre-test scores were (60.10±10.44) for Group A and (61.71±10.45) for Group B, with no statistically significant difference (P=0.618). Group A′s post-test scores after online platform learning [(83.71±7.75)] were higher than their pre-test scores, and their post-test scores after microscope-based learning [(93.52±4.29)] were higher than those after online platform learning; Group B′s post-test scores after microscope-based learning [(75.24±10.76)] were higher than their pre-test scores, and their post-test scores after online platform learning [(91.14±4.80)] were higher than those after microscope-based learning; Group A′s test scores after combining online platform and microscope-based learning were higher than Group B′s post-test scores after microscope-based learning; all the above differences were statistically significant (all P<0.001). The intern students believed that the online platform offered abundant learning resources, efficient and convenient learning, and met their self-directed learning needs. The supervising teachers highly evaluated the platform, considering it significantly improved teaching efficiency. Conclusions The AI-based online learning platform provides a convenient and efficient learning environment, enhances the learning efficiency of hematological cell morphology, and can serve as an effective auxiliary tool for learning hematological cell morphology.
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