Medical Teacher最新文献

Assessing readability of explanations and reliability of answers by GPT-3.5 and GPT-4 in non-traumatic spinal cord injury education. 用GPT-3.5和GPT-4评估非创伤性脊髓损伤教育中解释的可读性和答案的可靠性。

IF 3.3 2区教育学

Medical Teacher Pub Date : 2025-01-20 DOI: 10.1080/0142159X.2024.2430365

Alejandro García-Rudolph, David Sanchez-Pinsach, Mark Andrew Wright, Eloy Opisso, Joan Vidal

{"title":"Assessing readability of explanations and reliability of answers by GPT-3.5 and GPT-4 in non-traumatic spinal cord injury education.","authors":"Alejandro García-Rudolph, David Sanchez-Pinsach, Mark Andrew Wright, Eloy Opisso, Joan Vidal","doi":"10.1080/0142159X.2024.2430365","DOIUrl":"https://doi.org/10.1080/0142159X.2024.2430365","url":null,"abstract":"Purpose: Our study aimed to: i) Assess the readability of textbook explanations using established indexes; ii) Compare these with GPT-4's default explanations, ensuring similar word counts for direct comparisons; iii) Evaluate GPT-4's adaptability by simplifying high-complexity explanations; iv) Determine the reliability of GPT-3.5 and GPT-4 in providing accurate answers.Material and methods: We utilized a textbook designed for ABPMR certification. Our analysis covered 50 multiple-choice questions, each with a detailed explanation, focusing on non-traumatic spinal cord injury (NTSCI).Results: Our analysis revealed statistically significant differences in readability scores, with the textbook achieving 14.5 (SD = 2.5) compared to GPT-4's 17.3 (SD = 1.9), indicating that GPT-4's explanations are generally more complex (p < 0.001). Using the Flesch Reading Ease Score, 86% of GPT-4's explanations fell into the 'Very difficult' category, significantly higher than the textbook's 58% (p = 0.006). GPT-4 successfully demonstrated adaptability by reducing the mean readability score of the top-nine most complex explanations, maintaining the word count. Regarding reliability, GPT-3.5 and GPT-4 scored 84% and 96% respectively, with GPT-4 outperforming GPT-3.5 (p = 0.046).Conclusions: Our results confirmed GPT-4's potential in medical education by providing highly accurate yet often complex explanations for NTSCI, which were successfully simplified without losing accuracy.","PeriodicalId":18643,"journal":{"name":"Medical Teacher","volume":" ","pages":"1-8"},"PeriodicalIF":3.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143008431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A randomised cross-over trial assessing the impact of AI-generated individual feedback on written online assignments for medical students. 一项随机交叉试验，评估人工智能生成的个人反馈对医学生书面在线作业的影响。

IF 3.3 2区教育学

Medical Teacher Pub Date : 2025-01-20 DOI: 10.1080/0142159X.2025.2451870

Leon Nissen, Johanna Flora Rother, Marie Heinemann, Lara Marie Reimer, Stephan Jonas, Tobias Raupach

{"title":"A randomised cross-over trial assessing the impact of AI-generated individual feedback on written online assignments for medical students.","authors":"Leon Nissen, Johanna Flora Rother, Marie Heinemann, Lara Marie Reimer, Stephan Jonas, Tobias Raupach","doi":"10.1080/0142159X.2025.2451870","DOIUrl":"https://doi.org/10.1080/0142159X.2025.2451870","url":null,"abstract":"Purpose: Self-testing has been proven to significantly improve not only simple learning outcomes, but also higher-order skills such as clinical reasoning in medical students. Previous studies have shown that self-testing was especially beneficial when it was presented with feedback, which leaves the question whether an immediate and personalized feedback further encourages this effect. Therefore, we hypothesised that individual feedback has a greater effect on learning outcomes, compared to generic feedback.Materials and methods: In a randomised cross-over trial, German medical students were invited to voluntarily answer daily key-feature questions via an App. For half of the items they received a generalised feedback by an expert, while the feedback on the other half was generated immediately through ChatGPT. After the intervention, the students participated in a mandatory exit exam.Results: Those participants who used the app more frequently experienced a better learning outcome compared to those who did not use it frequently, even though this finding was only examined in a correlative nature. The individual ChatGPT generated feedback did not show a greater effect on exit exam scores compared to the expert comment (51.8 ± 22.0% vs. 55.8 ± 22.8%; p = 0.06).Conclusion: This study proves the concept of providing personalised feedback on medical questions. Despite the promising results, improved prompting and further development of the application seems necessary to strengthen the possible impact of the personalised feedback. Our study closes a research gap and holds great potential for further use not only in medicine but also in other academic fields.","PeriodicalId":18643,"journal":{"name":"Medical Teacher","volume":" ","pages":"1-7"},"PeriodicalIF":3.3,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143008429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Response to: "Rethinking peer evaluation in team-based learning". 回应：“重新思考团队学习中的同伴评价”。

IF 3.3 2区教育学

Medical Teacher Pub Date : 2025-01-16 DOI: 10.1080/0142159X.2025.2452695

Kyle Robertson, David Gunderman, Jessica Byram

引用次数: 0

Balancing innovation and tradition: A critical reflection on the assessment PROFILE framework. 平衡创新与传统：对评估PROFILE框架的批判性反思。

IF 3.3 2区教育学

Medical Teacher Pub Date : 2025-01-16 DOI: 10.1080/0142159X.2025.2452962

Supianto

引用次数: 0

Response to: 'A confidentiality conundrum: Case tracking for medical education'. 回应：“保密难题：医学教育的病例追踪”。

IF 3.3 2区教育学

Medical Teacher Pub Date : 2025-01-11 DOI: 10.1080/0142159X.2024.2431409

Daniel J Minter, Annabel K Frank, Logan Pierce, Brian Schwartz, Sirisha Narayana

引用次数: 0

Response to: 'Is the practice of case-tracking a substitute for traditional feedback?' 对以下问题的回应：“案例追踪是传统反馈的替代品吗？”

IF 3.3 2区教育学

Medical Teacher Pub Date : 2025-01-11 DOI: 10.1080/0142159X.2024.2431407

Daniel J Minter, Annabel K Frank, Logan Pierce, Brian Schwartz, Sirisha Narayana

引用次数: 0

Twelve tips on applying AI tools in HPE scholarship using Boyer's model. 使用Boyer模型在HPE奖学金中应用AI工具的12个提示。

IF 3.3 2区教育学

Medical Teacher Pub Date : 2025-01-10 DOI: 10.1080/0142159X.2024.2445058

Jennifer Benjamin, Ken Masters, Anoop Agrawal, Heather MacNeill, Neil Mehta

引用次数: 0

Moving Beyond Static, Individualistic Approaches to Agency: Theories of Agency for Medical Education Researchers: AMEE Guide No. 177. 超越静态的、个人主义的代理方法：医学教育研究人员代理理论：AMEE指南第177号。

IF 3.3 2区教育学

Medical Teacher Pub Date : 2025-01-10 DOI: 10.1080/0142159X.2024.2445045

Abigail Konopasky, Gabrielle M Finn, Lara Varpio

{"title":"Moving Beyond Static, Individualistic Approaches to Agency: Theories of Agency for Medical Education Researchers: AMEE Guide No. 177.","authors":"Abigail Konopasky, Gabrielle M Finn, Lara Varpio","doi":"10.1080/0142159X.2024.2445045","DOIUrl":"https://doi.org/10.1080/0142159X.2024.2445045","url":null,"abstract":"Agency - the capacity to produce an effect - is a foundational aspect of medical education. Agency is usually conceptualized at the level of the individual, with each learner charged with taking responsibility to pull themselves up by their bootstraps. This conceptualization is problematic. First, collaboration is a central component of patient care, which does not align well with an individualistic approach. Second, a growing body of literature documents how minoritized and marginalized trainees experience inequitable restrictions on their agency. Third, a myriad of structures across medicine restricts individual agency. In this guide, we present four conceptualizations of agency beyond the individual that medical researchers can incorporate to modernize and broaden their understanding of agency: (a) temporal: how individuals wrestle with their own agency across time; (b) relational: how agency is co-created dialogically with other individuals and structures; (c) cultural: how culture and cultural resources shape possibilities for agency; and (d) structural: how restrictive structures - like racism and ableism that unjustly curtail individual agency - are created, maintained, and resisted. For each dimension, we first describe it by drawing from and summarizing the work of theorists across disciplines. Next, we highlight an article from medical education that makes particularly good use of this dimension, discussing some of its relevant findings. Finally, we offer a set of questions that researchers in medical education can ask to highlight the dimension of agency in their work, and we suggest potential directions for future inquiry. We conclude by offering an example of how a researcher might understand a resident's educational experiences through each of the four proposed dimensions and further explicating the complexity of agency in medical education.","PeriodicalId":18643,"journal":{"name":"Medical Teacher","volume":" ","pages":"1-10"},"PeriodicalIF":3.3,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142951363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Creating custom GPTs for faculty development: An example using the Johari Window and Crucial Conversation frameworks for providing feedback to struggling students. 为教师发展创建定制的gpt：使用Johari窗口和关键对话框架为挣扎的学生提供反馈的示例。

IF 3.3 2区教育学

Medical Teacher Pub Date : 2025-01-09 DOI: 10.1080/0142159X.2024.2445043

Neil Mehta, Craig Nielsen, Amy Zack, Terri Christensen, J H Isaacson

{"title":"Creating custom GPTs for faculty development: An example using the Johari Window and Crucial Conversation frameworks for providing feedback to struggling students.","authors":"Neil Mehta, Craig Nielsen, Amy Zack, Terri Christensen, J H Isaacson","doi":"10.1080/0142159X.2024.2445043","DOIUrl":"https://doi.org/10.1080/0142159X.2024.2445043","url":null,"abstract":"Feedback plays a crucial role in the growth and development of trainees, particularly when addressing areas needing improvement. However, faculty members often struggle to deliver constructive feedback, particularly when discussing underperformance. A key obstacle is the lack of comfort many faculty experience in providing feedback that fosters growth. Traditional faculty development programs designed to address these challenges can be expensive and too time-intensive, for busy clinicians.. Generative AI, specifically custom GPT models simulating virtual students and coaches, offers a promising solution for faculty development in feedback training. These AI-driven tools can simulate realistic feedback scenarios using widely accepted educational frameworks and coach faculty members on best practices in delivering constructive feedback. Through interactive, low-cost, and accessible virtual simulations, faculty members can practice in a safe environment and receive immediate, tailored coaching. This approach enhances faculty confidence and competence while reducing the logistical and financial constraints of traditional faculty development programs. By providing scalable, on-demand training, custom GPT-based simulations can be seamlessly integrated into clinical environments, fostering a supportive feedback culture prioritizing trainee development. This paper describes the stepwise process of design and implementation, of a custom GPT-powered feedback training based on an accepted framework. This process can has the potential to transform faculty development in medical education.","PeriodicalId":18643,"journal":{"name":"Medical Teacher","volume":" ","pages":"1-3"},"PeriodicalIF":3.3,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142951216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Intelligence in Health Professions Education assessment: AMEE Guide No. 178. 卫生专业教育评估中的人工智能：AMEE指南第178号。

IF 3.3 2区教育学

Medical Teacher Pub Date : 2025-01-09 DOI: 10.1080/0142159X.2024.2445037

Ken Masters, Heather MacNeil, Jennifer Benjamin, Tamara Carver, Kataryna Nemethy, Sofia Valanci-Aroesty, David C M Taylor, Brent Thoma, Thomas Thesen

引用次数: 0