Explainable Artificial Intelligence (XAI) in Medical Education: A Multi-Modal Framework for Enhancing Human-AI Collaboration

The study utilized a two-phase sequential explanatory design with mixed methodologies. In Phase 1 (Technical Development), the CerViD-MultiModal model was developed and validated using neuroimaging data from 100 Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects to classify early vs. late mild cognitive impairment via fornix morphometry features. In Phase 2 (Educational Intervention), a randomized controlled trial was conducted with 120 third-year medical students enrolled in the clinical neuroscience rotation at the University of Liberia. Participants were randomized into two equal groups (n=60 per group): Control Group: Completed a 45-minute traditional lecture module using static text and bar charts. XAI-Enhanced Group: Completed an interactive 45-minute module featuring SHAP summary charts, LIME patient-specific explanations, and interactive force graphs. Post-intervention electronic assessments evaluated four primary outcomes: AI Literacy Score (0-100 scale), System Usability Scale (SUS, 0-100 scale), perceived cognitive workload using the NASA Task Load Index (NASA-TLX, 0-100 scale), and Confidence in AI Interpretation (1-5 scale)