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比较人工智能辅助解决问题的能力与互联网搜索引擎和电子书在医学学生具有可变的先前主题知识:横截面研究

Ajiith Xavier¹, Syed Shariq Naeem¹, Waseem Rizwi¹

¹Department of Pharmacology, Jawaharlal Nehru Medical College and Hospital, Aligarh Muslim University, Medical Road, Aligarh, Uttar Pradesh, 202001, India, 91 9634912166.

JMIR medical education

|January 6, 2026

概括

人工智能 (AI) 大型语言模型 (LLM) 显著提高了医学学生在多选题中解决问题的技能. 使用人工智能的天真学生甚至超过了使用传统资源的知识博的同学.

科学领域:

医疗教育技术技术医学教育
医疗保健中的人工智能
教育评估的教育评估

背景情况:

人工智能 (AI),特别是大型语言模型 (LLM),正在越来越多地影响医学教育.
对具有不同先前知识水平的学生来说,AI-LLM的有效性尚未得到充分理解.

研究的目的:

用AI-LLM GPT,互联网搜索引擎,电子书或多选题 (MCQ) 自我知识来评估具有或没有正式药理学知识的医学学生的表现.

主要方法:

一项横截面研究涉及100名医学学生 (50名天真,50名学术) 在一家三级医疗医院.
参与者通过自我认识,电子书,谷歌或ChatGPT-4o回答了MCQ.
用协变性分析比较得分,将自我认识作为一个协变量.

主要成果:

熟练的学生在所有方法中表现优于天真的学生 (P<.001),AI-LLM GPTs的效果大小最大.
所有学生的绩效层次:AI-LLM GPT > 互联网搜索引擎 > 自我知识 ≈电子书.
使用人工智能的天真学生得分高于使用谷歌或电子书的熟练学生 (P<.01).

结论:

AI-LLM GPT提高了MCQ的表现,特别是对于以前知识有限的学生来说.
人工智能工具可以使知识较少的学生能够使用传统资源优于同龄人.
需要进一步研究人工智能对医学教育中的深度学习和批判性思维的影响.

关键词:

在这里,我们可以看到AIAIAI.聊天GPT 聊天在GPT 聊天在法学士 (LLM) 课程中.人工智能的人工智能是人工智能.认知表现认知表现大型语言模型.医学教育医学教育主题素朴的学习者.

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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...