Application of an artificial intelligence-based online learning platform in the internship teaching of hematological cell morphology for medical laboratory technology students

doi:10.3760/cma.j.cn115259-20241218-01329

Abstract

Abstract: 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.

Key words: Blood cells, Morphology, Artificial intelligence, Online learning platform, Medical laboratory technology, Mixed methods research

CLC Number:

R-05

Li Junxun, Zhang Fan, Li Runzhao, Cheng Jing, Tan Hongxia, Chen Peisong, Huang Bin, Ouyang Juan, Lyu Wange. Application of an artificial intelligence-based online learning platform in the internship teaching of hematological cell morphology for medical laboratory technology students[J]. Chinese Journal of Medical Education, 2025, 45(3): 204-209.

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