Related Experiment Video
Updated: Aug 5, 2025
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
Published on: October 27, 2023
Artificial Intelligence-based online platform assists blood cell morphology learning: A mixed-methods sequential
Junxun Li1, Juan Ouyang1, Juan Liu2
1Department of Laboratory Science, First Affiliated Hospital of Sun Yatsen University, Guangzhou, China.
Background:
The study aimed to evaluate the effectiveness of learning blood cell morphology by learning on our Artificial intelligence (AI)-based online platform.
Methods:
Our study is based on mixed-methods sequential explanatory design and crossover design. Thirty-one third-year medical students were randomly divided into two groups. The two groups had platform learning and microscopy learning in diferent sequences with pretests and posttests, respectively. Students were interviewed, and the records were coded and analyzed by NVivo 12.0.
Results:
For both groups, test scores increased significantly after online-platform learning. Feasibility was the most mentioned advantage of the platform. The AI system could inspire the students to compare the similarities and differences between cells and help them understand the cells better. Students had positive perspectives on the online-learning platform.
Conclusion:
The AI-based online platform could assist medical students in blood cell morphology learning. The AI system could function as a more knowledgeable other (MKO) and guide the students through their zone of proximal development (ZPD) to achieve mastery. It could be an effective and beneficial complement to microscopy learning. Students had very positive perspectives on the AI-based online learning platform. It should be integrated into the course and curriculum to facilitate the students.[Box: see text].
