Comparing the performance of artificial intelligence learning models to medical students in solving histology and embryology multiple choice questions

IF 2 3区 医学 Q2 ANATOMY & MORPHOLOGY
Miloš Bajčetić , Aleksandar Mirčić , Jelena Rakočević, Danilo Đoković, Katarina Milutinović, Ivan Zaletel
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引用次数: 0

Abstract

Introduction

The appearance of artificial intelligence language models (AI LMs) in the form of chatbots has gained a lot of popularity worldwide, potentially interfering with different aspects of education, including medical education as well. The present study aims to assess the accuracy and consistency of different AI LMs regarding the histology and embryology knowledge obtained during the 1st year of medical studies.

Methods

Five different chatbots (ChatGPT, Bing AI, Bard AI, Perplexity AI, and ChatSonic) were given two sets of multiple-choice questions (MCQs). AI LMs test results were compared to the same test results obtained from 1st year medical students. Chatbots were instructed to use revised Bloom’s taxonomy when classifying questions depending on hierarchical cognitive domains. Simultaneously, two histology teachers independently rated the questions applying the same criteria, followed by the comparison between chatbots’ and teachers’ question classification. The consistency of chatbots’ answers was explored by giving the chatbots the same tests two months apart.

Results

AI LMs successfully and correctly solved MCQs regarding histology and embryology material. All five chatbots showed better results than the 1st year medical students on both histology and embryology tests. Chatbots showed poor results when asked to classify the questions according to revised Bloom’s cognitive taxonomy compared to teachers. There was an inverse correlation between the difficulty of questions and their correct classification by the chatbots. Retesting the chatbots after two months showed a lack of consistency concerning both MCQs answers and question classification according to revised Bloom’s taxonomy learning stage.

Conclusion

Despite the ability of certain chatbots to provide correct answers to the majority of diverse and heterogeneous questions, a lack of consistency in answers over time warrants their careful use as a medical education tool.

比较人工智能学习模型在医学生解答组织学和胚胎学选择题中的表现
引言 以聊天机器人形式出现的人工智能语言模型(AI LMs)在全球范围内大受欢迎,有可能干扰教育的各个方面,包括医学教育。本研究旨在评估不同的人工智能语言模型对医学专业一年级学生所学的组织学和胚胎学知识的准确性和一致性。人工智能 LM 的测试结果与一年级医学生的相同测试结果进行了比较。在根据认知领域的层次对问题进行分类时,指导聊天机器人使用修订后的布鲁姆分类法。同时,两名组织学教师采用相同的标准对问题进行独立评分,然后比较聊天机器人和教师的问题分类。结果AI LMs 成功并正确地解决了有关组织学和胚胎学材料的 MCQ。所有五个聊天机器人在组织学和胚胎学测试中的成绩都优于一年级医学生。与教师相比,当要求聊天机器人根据修订后的布鲁姆认知分类法对问题进行分类时,聊天机器人的成绩较差。问题的难度与聊天机器人的正确分类之间存在反相关关系。两个月后对聊天机器人进行的重新测试表明,MCQ 答案和根据修订版布鲁姆分类法学习阶段进行的问题分类都缺乏一致性。
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来源期刊
Annals of Anatomy-Anatomischer Anzeiger
Annals of Anatomy-Anatomischer Anzeiger 医学-解剖学与形态学
CiteScore
4.40
自引率
22.70%
发文量
137
审稿时长
33 days
期刊介绍: Annals of Anatomy publish peer reviewed original articles as well as brief review articles. The journal is open to original papers covering a link between anatomy and areas such as •molecular biology, •cell biology •reproductive biology •immunobiology •developmental biology, neurobiology •embryology as well as •neuroanatomy •neuroimmunology •clinical anatomy •comparative anatomy •modern imaging techniques •evolution, and especially also •aging
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