PDF(Pubmed)
Abstract：
OBJECTIVE: 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.
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.
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.
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.
摘要：
目的：解剖学教育在医疗实践中起着至关重要的作用，学生和医生的解剖学知识水平显著影响患者护理。本文提出了一个试点项目，旨在探索Area9的Rhapsode平台的有效性，使用人工智能（AI）进行个性化学习并收集掌握数据的智能辅导系统。
方法：该研究集中于肝脏解剖学（微观和宏观解剖学，胚胎学,临床解剖学)，并采用翻转课堂方法，结合自适应学习模块和交互式课堂会话。共有123名一年级医学学生（55M/68F）参加了这项研究。该模块的内容和资源适用于各种数字设备。统计数据是根据编制的，一方面,关于平台通过学生与系统探针(问题)的互动自动提供的每一个学习目标的掌握程度的测量；另一方面，元认知数据是通过将掌握数据与每个学习者在每个问题和学习资源中声明的自我意识进行交叉来计算的。
结论：在研究开始时，学生表现出18.11%的意识无能水平和19.43%的无意识无能水平。此外,50.86%的学生表现出有意识的能力。通过学习模块的结论，意识无能水平下降到1.87%，98.73%的学生表现出对材料的自觉掌握。结果显示学习质量有所提高，积极地重新利用学习时间，增强学生的元认知意识，大多数学生表现出对材料的有意识的掌握和对他们的能力水平的清晰理解。这种方法,通过提供对基于AI的自适应学习系统在解剖学教育中的潜力的宝贵见解，可以解决有限的教学时间带来的挑战，缺少解剖学家，以及个性化教学的需要。
方法：该研究集中于肝脏解剖学（微观和宏观解剖学，胚胎学,临床解剖学)，并采用翻转课堂方法，结合自适应学习模块和交互式课堂会话。共有123名一年级医学学生（55M/68F）参加了这项研究。该模块的内容和资源适用于各种数字设备。统计数据是根据编制的，一方面,关于平台通过学生与系统探针(问题)的互动自动提供的每一个学习目标的掌握程度的测量；另一方面，元认知数据是通过将掌握数据与每个学习者在每个问题和学习资源中声明的自我意识进行交叉来计算的。
结论：在研究开始时，学生表现出18.11%的意识无能水平和19.43%的无意识无能水平。此外,50.86%的学生表现出有意识的能力。通过学习模块的结论，意识无能水平下降到1.87%，98.73%的学生表现出对材料的自觉掌握。结果显示学习质量有所提高，积极地重新利用学习时间，增强学生的元认知意识，大多数学生表现出对材料的有意识的掌握和对他们的能力水平的清晰理解。这种方法,通过提供对基于AI的自适应学习系统在解剖学教育中的潜力的宝贵见解，可以解决有限的教学时间带来的挑战，缺少解剖学家，以及个性化教学的需要。
