Arain Shoukat Ali, Akhund Shahid Akhtar, Barakzai Muhammad Abrar, Meo Sultan Ayoub
Department of Pathology, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Department of Anatomy and Genetics and Department of Medical Education, College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.
Adv Physiol Educ. 2025 Jun 1;49(2):398-404. doi: 10.1152/advan.00209.2024. Epub 2025 Feb 7.
The alignment of learning materials with learning objectives (LOs) is critical for successfully implementing the problem-based learning (PBL) curriculum. This study investigated the capabilities of Gemini Advanced, a large language model (LLM), in creating clinical vignettes that align with LOs and comprehensive tutor guides. This study used a faculty-written clinical vignette about diabetes mellitus for third-year medical students. We submitted the LOs and the associated clinical vignette and tutor guide to the LLM to evaluate their alignment and generate new versions. Four faculty members compared both versions, using a structured questionnaire. The mean evaluation scores for original and LLM-generated versions are reported. The LLM identified new triggers for the clinical vignette to align it better with the LOs. Moreover, it restructured the tutor guide for better organization and flow and included thought-provoking questions. The medical information provided by the LLM was scientifically appropriate and accurate. The LLM-generated clinical vignette scored higher (3.0 vs. 1.25) for alignment with the LOs. However, the original version scored better for being educational level-appropriate (2.25 vs. 1.25) and adhering to PBL design (2.50 vs. 1.25). The LLM-generated tutor guide scored higher for better flow (3.0 vs. 1.25), comprehensive and relevant content (2.75 vs. 1.50), and thought-provoking questions (2.25 vs. 1.75). However, LLM-generated learning material lacked visual elements. In conclusion, this study demonstrated that Gemini could align and improve PBL learning materials. By leveraging the potential of LLMs while acknowledging their limitations, medical educators can create innovative and effective learning experiences for future physicians. This study evaluated a large language model (LLM) (Gemini Advanced) for creating aligned problem-based learning (PBL) materials. The LLM improved the alignment of the clinical vignette with learning goals. The LLM also restructured the tutor guide and added thought-provoking questions. The LLM guide was well organized and informative, but the original vignette was considered more educational level-appropriate. Although the LLM could not generate visuals, AI can improve PBL materials, especially when combined with human expertise.
学习材料与学习目标（LOs）的一致性对于成功实施基于问题的学习（PBL）课程至关重要。本研究调查了大型语言模型（LLM）Gemini Advanced在创建与学习目标和全面的导师指南相一致的临床案例方面的能力。本研究使用了一份由教师编写的针对三年级医学生的关于糖尿病的临床案例。我们将学习目标、相关的临床案例和导师指南提交给该大型语言模型，以评估它们的一致性并生成新版本。四位教师使用结构化问卷对两个版本进行了比较。报告了原始版本和由大型语言模型生成版本的平均评估分数。该大型语言模型为临床案例确定了新的触发因素，使其与学习目标更好地保持一致。此外，它还对导师指南进行了重新组织，以使其结构更合理、流程更顺畅，并纳入了发人深省的问题。该大型语言模型提供的医学信息在科学上是恰当且准确的。由大型语言模型生成的临床案例在与学习目标的一致性方面得分更高（3.0对1.25）。然而，原始版本在教育水平适宜性方面得分更高（2.25对1.25），并且在遵循PBL设计方面表现更好（2.50对1.25）。由大型语言模型生成的导师指南在流程更顺畅方面得分更高（3.0对1.25），在内容全面且相关方面得分更高（2.75对1.50），在有发人深省的问题方面得分更高（2.25对1.75）。然而，由大型语言模型生成的学习材料缺乏视觉元素。总之，本研究表明Gemini可以使PBL学习材料保持一致并加以改进。通过利用大型语言模型的潜力同时认识到它们的局限性，医学教育工作者可以为未来的医生创造创新且有效的学习体验。本研究评估了一个大型语言模型（Gemini Advanced）用于创建一致的基于问题的学习（PBL）材料的情况。该大型语言模型提高了临床案例与学习目标的一致性。该大型语言模型还对导师指南进行了重新组织，并添加了发人深省的问题。由大型语言模型生成的指南组织良好且信息丰富，但原始案例被认为在教育水平上更适宜。尽管该大型语言模型无法生成视觉内容，但人工智能可以改进PBL材料，尤其是与人类专业知识相结合时。
