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印度医学教师对用于教育目的的大型语言模型聊天机器人的使用情况评估:一项全国性调查

Assessing the Utilization of Large Language Model Chatbots for Educational Purposes by Medical Teachers: A Nationwide Survey From India.

作者信息

Deb Roy Asitava, Bharat Jaiswal Ichchhit, Nath Tiu Devendra, Das Dipmala, Mondal Shaikat, Behera Joshil Kumar, Mondal Himel

机构信息

Pathology, Mata Gujri Memorial Medical College, Kishanganj, IND.

Dermatology, Mata Gujri Memorial Medical College, Kishanganj, IND.

出版信息

Cureus. 2024 Nov 11;16(11):e73484. doi: 10.7759/cureus.73484. eCollection 2024 Nov.

Abstract

Background Large language models (LLMs) are increasingly explored in healthcare and education. In medical education, they hold the potential to enhance learning by supporting personalized teaching, resource development, and student engagement. However, LLM use also raises concerns about ethics, accuracy, and reliance. Understanding how educators leverage LLMs can help assess their role and implications in medical education. Methods This cross-sectional online survey was conducted among medical teachers in India from December 2023 to March 2024. A validated questionnaire with acceptable internal consistency and test-retest reliability was used. It collected data on LLM chatbot usage patterns, as well as teachers' knowledge, attitudes, and practices regarding LLMs for educational purposes. Results A total of 396 medical teachers with an average teaching experience of 4.12±2.47 (minimum six months, maximum 13 years) years participated from different parts of India. The majority of the teachers heard about ChatGPT (OpenAI, San Francisco, CA, USA) (85%), followed by Copilot/Bing (Microsoft, Washington, DC, USA) (53%), and Gemini/Bard (Google, Mountain View, CA, USA) (45%) (p-value < 0.0001). However, 29% of the respondents never used it and 47% rarely use LLMs for educational purposes (p-value < 0.0001). The majority of the teachers use it for making any topic simple (55%), generating text for PowerPoint slides (55%), generating multiple-choice questions (MCQs) (52%), and finding answers to student's queries (35%). Knowledge (3.4±0.47) showed the highest score, followed by practice (3.3±0.81) and attitude (3.14±0.46) (p-value = 0.0023). Conclusion While awareness of LLMs was high among medical teachers in India, their actual usage for educational purposes remains limited. Despite recognizing the potential of LLMs for simplifying topics, generating teaching materials, and addressing student queries, a significant proportion of educators seldom integrate these technologies into their teaching practices. Institutions may provide training to help medical educators effectively integrate LLMs into teaching practices.

摘要

背景 大语言模型(LLMs)在医疗保健和教育领域正得到越来越多的探索。在医学教育中,它们有潜力通过支持个性化教学、资源开发和学生参与来提升学习效果。然而,大语言模型的使用也引发了对伦理、准确性和依赖性的担忧。了解教育工作者如何利用大语言模型有助于评估它们在医学教育中的作用和影响。方法 2023年12月至2024年3月期间,对印度的医学教师进行了这项横断面在线调查。使用了一份经过验证的问卷,其内部一致性和重测信度均可接受。该问卷收集了有关大语言模型聊天机器人使用模式的数据,以及教师在教育目的方面对大语言模型的知识、态度和实践情况。结果 共有396名医学教师参与,他们来自印度不同地区,平均教学经验为4.12±2.47(最短6个月,最长13年)年。大多数教师听说过ChatGPT(美国加利福尼亚州旧金山的OpenAI公司)(85%),其次是Copilot/Bing(美国华盛顿特区的微软公司)(53%),以及Gemini/Bard(美国加利福尼亚州山景城的谷歌公司)(45%)(p值<0.0001)。然而,29%的受访者从未使用过大语言模型,47%的受访者很少将其用于教育目的(p值<0.0001)。大多数教师用它来简化任何主题(55%)、为PowerPoint幻灯片生成文本(55%)、生成多项选择题(52%)以及查找学生问题的答案(35%)。知识(3.4±0.47)得分最高,其次是实践(3.3±0.81)和态度(3.14±0.46)(p值=0.0023)。结论 虽然印度的医学教师对大语言模型的认知度很高,但它们在教育目的方面的实际使用仍然有限。尽管认识到大语言模型在简化主题、生成教学材料和解答学生问题方面的潜力,但相当一部分教育工作者很少将这些技术融入他们的教学实践中。机构可以提供培训,以帮助医学教育工作者有效地将大语言模型融入教学实践。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6da0/11634817/2ead211f8838/cureus-0016-00000073484-i01.jpg

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