Impact of Artificial Intelligence Combined with Task-Driven Learning Methods on Teaching Effectiveness for Medical Interns: A Study in Respiratory Medicine

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 < 0.001), and case analysis (14.85 ± 1.85 vs. 10.96 ± 1.95, P < 0.001). Satisfaction surveys revealed significantly more favorable responses from the experimental group (P < 0.001), with a higher proportion of “very satisfied” ratings. Self-assessment results indicated markedly improved perceived competency among experimental group participants (P = 0.02). 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.