Bland Tyler
Department of Medical Education, University of Idaho, 875 Perimeter Drive MS 4061, WWAMI Medical Education, Moscow, ID, 83844-9803, United States, 1 5092090908.
JMIR Med Educ. 2025 Jan 6;11:e63865. doi: 10.2196/63865.
Medical students often struggle to engage with and retain complex pharmacology topics during their preclinical education. Traditional teaching methods can lead to passive learning and poor long-term retention of critical concepts.
This study aims to enhance the teaching of clinical pharmacology in medical school by using a multimodal generative artificial intelligence (genAI) approach to create compelling, cinematic clinical narratives (CCNs).
We transformed a standard clinical case into an engaging, interactive multimedia experience called "Shattered Slippers." This CCN used various genAI tools for content creation: GPT-4 for developing the storyline, Leonardo.ai and Stable Diffusion for generating images, Eleven Labs for creating audio narrations, and Suno for composing a theme song. The CCN integrated narrative styles and pop culture references to enhance student engagement. It was applied in teaching first-year medical students about immune system pharmacology. Student responses were assessed through the Situational Interest Survey for Multimedia and examination performance. The target audience comprised first-year medical students (n=40), with 18 responding to the Situational Interest Survey for Multimedia survey (n=18).
The study revealed a marked preference for the genAI-enhanced CCNs over traditional teaching methods. Key findings include the majority of surveyed students preferring the CCN over traditional clinical cases (14/18), as well as high average scores for triggered situational interest (mean 4.58, SD 0.53), maintained interest (mean 4.40, SD 0.53), maintained-feeling interest (mean 4.38, SD 0.51), and maintained-value interest (mean 4.42, SD 0.54). Students achieved an average score of 88% on examination questions related to the CCN material, indicating successful learning and retention. Qualitative feedback highlighted increased engagement, improved recall, and appreciation for the narrative style and pop culture references.
This study demonstrates the potential of using a multimodal genAI-driven approach to create CCNs in medical education. The "Shattered Slippers" case effectively enhanced student engagement and promoted knowledge retention in complex pharmacological topics. This innovative method suggests a novel direction for curriculum development that could improve learning outcomes and student satisfaction in medical education. Future research should explore the long-term retention of knowledge and the applicability of learned material in clinical settings, as well as the potential for broader implementation of this approach across various medical education contexts.
医学生在临床前教育阶段常常难以理解和记住复杂的药理学主题。传统教学方法可能导致被动学习，且关键概念的长期记忆效果不佳。
本研究旨在通过使用多模态生成式人工智能（genAI）方法创建引人入胜的电影化临床叙事（CCN），以加强医学院校临床药理学的教学。
我们将一个标准临床病例转化为一种名为“破碎拖鞋”的引人入胜的交互式多媒体体验。这个CCN使用了各种genAI工具进行内容创作：利用GPT-4编写故事情节，使用Leonardo.ai和Stable Diffusion生成图像，借助Eleven Labs创建音频旁白，并通过Suno创作主题曲。该CCN整合了叙事风格和流行文化元素以提高学生的参与度。它被应用于向一年级医学生讲授免疫系统药理学。通过多媒体情境兴趣调查和考试成绩来评估学生的反应。目标受众为一年级医学生（n = 40），其中18人回应了多媒体情境兴趣调查（n = 18）。
研究表明，与传统教学方法相比，学生对genAI增强的CCN有明显偏好。主要发现包括：大多数接受调查的学生更喜欢CCN而非传统临床病例（14/18），以及在引发的情境兴趣（平均4.58，标准差0.53）、保持的兴趣（平均4.40，标准差0.53）、持续的情感兴趣（平均4.38，标准差0.51）和持续的价值兴趣（平均4.42，标准差0.54）方面得分较高。学生在与CCN材料相关的考试问题上平均得分88%，表明学习和记忆效果良好。定性反馈突出了学生参与度的提高、记忆力的改善以及对叙事风格和流行文化元素的欣赏。
本研究证明了在医学教育中使用多模态genAI驱动方法创建CCN的潜力。 “破碎拖鞋”案例有效地提高了学生的参与度，并促进了对复杂药理学主题的知识保留。这种创新方法为课程开发提出了一个新方向，有望改善医学教育中的学习成果和学生满意度。未来的研究应探索知识的长期保留情况以及所学材料在临床环境中的适用性，以及这种方法在各种医学教育背景下更广泛实施的潜力。
