Artificial Intelligence in Surgical Medical Education: A Systematic Review in Progress

• In Event: AERA Research in Progress Roundtable Session Five
  In Invited Roundtable: Research-in-Progress RT003 (Sunday)

Abstract

Artificial intelligence (AI)–driven decision support tools are increasingly integrated into surgical education, yet their cognitive and pedagogical impact remains unclear. Guided by Cognitive Load Theory and Situated Learning Theory, this systematic review examines how AI-assisted decision-making influences surgical trainees’ accuracy, efficiency, cognitive load, and confidence. Searches across PubMed, Embase, ERIC, and related databases follow PRISMA-P guidelines. Preliminary evidence suggests AI enhances diagnostic precision and decision speed but may initially increase cognitive load as learners interpret algorithmic feedback. Over time, AI use may promote schema automation and reduce intrinsic load. This review will synthesize emerging findings to inform educational design and guide safe, effective integration of AI systems that strengthen rather than substitute clinical reasoning in surgical training.

Authors

• Heba Abushanab, Utah State University

• Ramy Shaaban, Utah State University