Name
Student-Led AI-Assisted 3D Interactive Anatomy Tool to Foster Clinical Reasoning in Anatomy Education
Speakers
Authors
Tejas Ravindra, Rutgers University School of Arts and Sciences
Kun Li, Rutgers University Robert Wood Johnson Medical School
Gail Elliott, Rutgers University Robert Wood Johnson Medical School
Grace Pinhal-Enfield, Rutgers University Robert Wood Johnson Medical School
Date & Time
Wednesday, October 21, 2026, 10:15 AM - 10:29 AM
Presentation Category
Redefining Competence: Professional Identity, Collaborative Learning, and Resilience in an AI-Shaped World
Presentation Tag(s)
Student Presenter
Description
Background: Anatomy curricula continue to become increasingly compressed, making it more difficult for learners to translate foundational anatomical knowledge into clinical reasoning. Rather than viewing artificial intelligence (AI) solely as a tool for content generation, we explored its potential to empower medical students as educational innovators by designing interactive learning experiences that address persistent educational challenges.
Innovation: We developed a student-led, AI-assisted three-dimensional (3D) interactive simulation enabling learners to explore upper limb nerve lesions through dynamic visualization of motor, sensory, and reflex deficits. The platform promotes active clinical reasoning by integrating anatomical relationships with authentic patient presentations. In a pilot study, first-year medical students completed either a traditional PowerPoint review or the interactive simulation after the standard anatomy curriculum. Knowledge acquisition was assessed using identical pre- and post-tests, while learner perceptions were explored through surveys and qualitative thematic analysis.
Outcomes: Although quantitative differences between instructional modalities were not statistically significant in this exploratory pilot, learners using the simulation demonstrated greater improvement from a lower baseline and reported enhanced spatial understanding, stronger integration of anatomy with clinical reasoning, and higher engagement. Qualitative findings suggested that each modality supported distinct learner needs, highlighting interactive technologies as complementary to traditional instruction. More broadly, this work demonstrates how AI can empower medical students to become active creators of educational resources, fostering innovation, strengthening professional identity, and supporting learner-centered educational design while preserving the human-centered values of health professions education.