Enhancing Anatomy Education Throu€gh Flipped Classroom and Adaptive Learning A Pilot Project on Liver Anatomy.

Abstract

Objectives: Anatomy education plays a critical role in medical practice, and the level of anatomical knowledge among students and physicians significantly impacts patient care. This article presents a pilot project aimed at exploring the effectiveness of the Area9's Rhapsode platform, an intelligent tutoring system that uses artificial intelligence (AI) to personalize learning and collect data on mastery acquisition.

Methods: The study focused on liver anatomy (microscopic and macroscopic anatomy, embryology, clinical anatomy) and employed a flipped classroom approach, incorporating adaptive learning modules and an interactive in-class session. A total of 123 first-year medicine students (55 M/68F) participated to the study. Content and resources of the module were adaptable to various digital devices. Statistics were compiled based, on the one hand, on the measurement of mastery for every single learning objective provided automatically by the platform via the student interactions with the system probes (questions); on the other hand, metacognition data were worked out by crossing mastery data with the self-awareness declared in every question and learning resource by each learner.

Results and conclusions: At the outset of the study, students displayed a 18.11% level of conscious incompetence and a 19.43% level of unconscious incompetence. Additionally, 50.86% of students demonstrated conscious competence. By the conclusion of the learning module, the level of conscious incompetence had decreased to 1.87%, and 98.73% of students exhibited conscious mastery of the materials. The results demonstrated improved learning quality, positive repurposing of study time, enhanced metacognitive awareness among students, with most students demonstrating conscious mastery of the materials and a clear understanding of their level of competence. This approach, by providing valuable insights into the potential of AI-based adaptive learning systems in anatomy education, could address the challenges posed by limited teaching hours, shortage of anatomist, and the need for individualized instruction.