An Analytical Framework for Evaluating Generative Artificial Intelligence and Faculty-Derived Narrative Feedback in Medical Students’ Written Mental Status Examinations

Abstract

The rapid integration of generative artificial intelligence (AI) into medical education has introduced transformative opportunities in assessment and feedback systems. Narrative feedback, particularly in written Mental Status Examinations (MSEs), plays a crucial role in shaping clinical reasoning, reflective practice, and competency development among medical students. However, the comparative effectiveness of AI-generated feedback versus faculty-derived feedback remains insufficiently explored. This study proposes a structured analytical framework to evaluate the qualitative, pedagogical, and cognitive dimensions of feedback generated by generative AI systems and human faculty.
Adopting an experimental research design, the study integrates multi-dimensional evaluation metrics including linguistic quality, clinical accuracy, pedagogical relevance, and learner impact. Drawing upon existing literature in AI-assisted medical education and feedback theory, the framework operationalizes both quantitative scoring mechanisms and qualitative thematic analysis. The findings suggest that while generative AI demonstrates high consistency, scalability, and structural coherence, faculty feedback retains superiority in contextual depth, clinical nuance, and adaptive mentorship. The study highlights a hybrid augmentation model as a viable pathway for optimizing feedback systems.
The research contributes to the growing discourse on AI-human collaboration in medical education by providing a validated evaluative architecture. It also identifies critical limitations related to bias, interpretability, and pedagogical alignment. Future research directions include longitudinal impact assessment and integration of adaptive learning systems.