将人工智能的新工具与全球健康学生的真实智能进行比较。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Shilpa R Thandla, Grace Q Armstrong, Adil Menon, Aashna Shah, David L Gueye, Clara Harb, Estefania Hernandez, Yasaswini Iyer, Abigail R Hotchner, Riddhi Modi, Anusha Mudigonda, Maria A Prokos, Tharun M Rao, Olivia R Thomas, Camilo A Beltran, Taylor Guerrieri, Sydney LeBlanc, Skanda Moorthy, Sara G Yacoub, Jacob E Gardner, Benjamin M Greenberg, Alyssa Hubal, Yuliana P Lapina, Jacqueline Moran, Joseph P O'Brien, Anna C Winnicki, Christina Yoka, Junwei Zhang, Peter A Zimmerman
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引用次数: 0

摘要

人工智能(AI)的变革性特征是将非结构化数据解释和转换为连贯且有意义的上下文的巨大能力。总的来说,人工智能将改变学生研究及其评估的传统方法的潜力似乎是巨大的。在全球卫生研究方面,学生和研究专家必须评估基因人工智能在这一领域的优势和局限性。因此,我们研究的目的是将GenAI的信息素养与研究生在撰写研究论文时所达到的期望进行比较。方法:在完成凯斯西储大学(CWRU)的全球健康基础(INTH 401)课程后,招募成功完成要求的研究论文的研究生,使用原始作业提示将其原始论文与chatgpt - 40生成的论文进行比较。学生们还完成了一份谷歌表格调查,以评估人工智能生成论文的不同部分(例如,对引言指南的遵守程度、三个观点的呈现、结论)和他们的原始论文,以及他们对人工智能工作的总体满意度。原学生对chatgpt - 40的比较也使评价叙述元素和参考文献成为可能。结果:在完成要求的研究论文的54名学生中,有28名(51.8%)同意在比较项目中合作。对调查反馈的总结表明,学生对人工智能生成的论文的评价不如或类似于他们自己的论文(总体满意度平均= 2.39 (1.61-3.17);李克特量表:1至5分,分数越低表示自卑)。评估5个李克特项目查询的平均个人学生回答显示,17个得分为讨论:我们的研究结果揭示了人工智能工具在帮助理解全球健康主题复杂性方面的优势和局限性。学生们提到的优势包括chatgpt - 40能够非常快速地生成内容,并提出他们在论文的三视角部分中没有考虑到的主题。始终如一地提供最新的事实和参考资料,以及进一步审查或总结全球卫生主题的复杂性,似乎是chatgpt - 40目前的局限性。由于chatgpt - 40从不存在的高度可信的生物医学研究期刊中生成参考文献,我们的研究结果得出结论,chatgpt - 40在有效利用信息方面失败了一个重要组成部分。此外,对可信赖的公共卫生信息来源的歪曲令人高度关切,特别是考虑到最近的COVID-19大流行以及最近在报告自然灾害影响和应对方面的经验。这是GenAI满足研究生所期望的信息素养标准的能力的一个重大限制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparing new tools of artificial intelligence to the authentic intelligence of our global health students.

Introduction: The transformative feature of Artificial Intelligence (AI) is the massive capacity for interpreting and transforming unstructured data into a coherent and meaningful context. In general, the potential that AI will alter traditional approaches to student research and its evaluation appears to be significant. With regard to research in global health, it is important for students and research experts to assess strengths and limitations of GenAI within this space. Thus, the goal of our research was to evaluate the information literacy of GenAI compared to expectations that graduate students meet in writing research papers.

Methods: After completing the course, Fundamentals of Global Health (INTH 401) at Case Western Reserve University (CWRU), Graduate students who successfully completed their required research paper were recruited to compare their original papers with a paper they generated by ChatGPT-4o using the original assignment prompt. Students also completed a Google Forms survey to evaluate different sections of the AI-generated paper (e.g., Adherence to Introduction guidelines, Presentation of three perspectives, Conclusion) and their original papers and their overall satisfaction with the AI work. The original student to ChatGPT-4o comparison also enabled evaluation of narrative elements and references.

Results: Of the 54 students who completed the required research paper, 28 (51.8%) agreed to collaborate in the comparison project. A summary of the survey responses suggested that students evaluated the AI-generated paper as inferior or similar to their own paper (overall satisfaction average = 2.39 (1.61-3.17); Likert scale: 1 to 5 with lower scores indicating inferiority). Evaluating the average individual student responses for 5 Likert item queries showed that 17 scores were < 2.9; 7 scores were between 3.0 to 3.9; 4 scores were ≥ 4.0, consistent with inferiority of the AI-generated paper. Evaluation of reference selection by ChatGPT-4o (n = 729 total references) showed that 54% (n = 396) were authentic, 46% (n = 333) did not exist. Of the authentic references, 26.5% (105/396) were relevant to the paper narrative; 14.4% of the 729 total references.

Discussion: Our findings reveal strengths and limitations on the potential of AI tools to assist in understanding the complexities of global health topics. Strengths mentioned by students included the ability of ChatGPT-4o to produce content very quickly and to suggest topics that they had not considered in the 3-perspective sections of their papers. Consistently presenting up-to-date facts and references, as well as further examining or summarizing the complexities of global health topics, appears to be a current limitation of ChatGPT-4o. Because ChatGPT-4o generated references from highly credible biomedical research journals that did not exist, our findings conclude that ChatGPT-4o failed an important component in using information effectively. Moreover, misrepresenting trusted sources of public health information is highly concerning, particularly given recent experiences from the COVID-19 pandemic and more recently in reporting on the impact of, and response to natural disasters. This is a significant limitation of GenAI's ability to meet information literacy standards expected of graduate students.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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