THE APPLICATION AND EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE-BASED INTERACTIVE LEARNING SYSTEMS IN TEACHING EPIDEMIOLOGY: A RANDOMIZED CONTROLLED TRIAL
Keywords:
Artificial Intelligence (AI), epidemiology education, Intelligent Tutoring Systems (ITS), adaptive learning, personalized education, machine learning, medical education, student performance, engagement.Abstract
Objective: To evaluate the effectiveness of an Artificial Intelligence-based interactive learning system (AI-ILS) compared to traditional didactic instruction in enhancing theoretical knowledge, critical thinking, and student engagement in an undergraduate epidemiology course. Methods: A randomized controlled trial was conducted with 200 third-year medical students. Participants were randomly assigned to the Control Group (n=100), receiving standard lectures and static case studies, or the Experimental Group (n=100), which used an AI-ILS ("Epi-Tutor"). The AI-ILS utilized natural language processing (NLP) to engage students in Socratic dialogue and adaptive algorithms to tailor case complexity based on real-time performance. The intervention lasted 10 weeks. Outcomes were measured using pre- and post-intervention standardized tests (knowledge), a case-based reasoning assessment (critical thinking), and the User Engagement Scale (UES) survey. Results: The Experimental Group achieved significantly higher post-intervention knowledge scores (Mean: 89.2 ± 4.5) compared to the Control Group (Mean: 78.4 ± 6.1; p<0.001). In the critical thinking assessment, AI-ILS users demonstrated superior performance in "hypothesis generation" and "bias identification" (p<0.01). Engagement metrics revealed that students in the Experimental Group spent 40% more time on task voluntarily. The AI system successfully identified knowledge gaps in biostatistics for 35% of students and automatically provided remedial modules, which correlated with improved final scores. Conclusion: AI-based interactive learning systems significantly outperform traditional instructional methods in teaching complex epidemiological concepts. By providing personalized, adaptive, and interactive content, AI tools foster deeper conceptual understanding and higher engagement. Integrating such systems into medical curricula is a viable strategy to enhance the competency of future public health professionals.
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