TITLE:
Impact of Artificial Intelligence Combined with Task-Driven Learning Methods on Teaching Effectiveness for Medical Interns: A Study in Respiratory Medicine
AUTHORS:
Cheng Chen, Liang Li
KEYWORDS:
Artificial Intelligence, Task-Driven Learning, Medical Internship, Respiratory Medicine, Teaching Effectiveness, Educational Innovation
JOURNAL NAME:
Intelligent Information Management,
Vol.17 No.6,
November
25,
2025
ABSTRACT: Objective: This study aimed to evaluate the effectiveness of an artificial intelligence (AI)-enhanced task-driven learning approach in respiratory medicine internship training compared to traditional teaching methods. Methods: A retrospective study was conducted involving 53 medical interns, divided into a control group (n = 27) receiving conventional teaching and an experimental group (n = 26) undergoing AI-enhanced task-driven instruction. The intervention integrated AI tools for clinical simulation with structured task-driven learning activities. Outcomes were assessed through standardized examinations (theoretical knowledge, practical skills, and case analysis) and comprehensive surveys (teaching satisfaction and self-evaluated competency). Results: The experimental group demonstrated statistically superior performance across all assessment domains compared to controls. Significantly higher scores were observed in theoretical knowledge (29.58 ± 3.38 vs. 27.11 ± 3.99, P = 0.02), practical skills (32.23 ± 3.13 vs. 26.56 ± 2.45, P Conclusion: The integration of AI tools with task-driven learning methodologies significantly enhances respiratory medicine internship training outcomes, improving both objective performance metrics and subjective learning experiences. This innovative approach offers a promising educational model for clinical training, though technical and ethical considerations require continued attention.