Twelve tips for teaching research skills in the age of agentic AI: A guide for health professions educators.

IF 4 2区教育学 Q1 EDUCATION, SCIENTIFIC DISCIPLINES

Medical Teacher Pub Date : 2026-10-01 Epub Date: 2026-07-07 DOI:10.1080/0142159X.2026.2681971

Neil Mehta, Jennifer Benjamin, Seysha Mehta, Sofia Valanci, Heather MacNeill, Ken Masters

{"title":"Twelve tips for teaching research skills in the age of agentic AI: A guide for health professions educators.","authors":"Neil Mehta, Jennifer Benjamin, Seysha Mehta, Sofia Valanci, Heather MacNeill, Ken Masters","doi":"10.1080/0142159X.2026.2681971","DOIUrl":null,"url":null,"abstract":"Background: The recent advances in Generative Artificial Intelligence (GenAI), from task-specific assistants to autonomous agentic artificial intelligence (AI) are changing how research is conceived, conducted, and written. Across this spectrum AI can now assist with literature searches and synthesis, protocol drafting, statistical analysis, and manuscript preparation, particularly in computational domains. Yet AI outputs remain error-prone, opaque, and carry real stakes for patients, learners, and equitable outcomes, making strong foundational research skills more important than ever.Purpose: This article offers practical guidance for medical educators responsible for research training in an AI-augmented environment.Tips: Drawing on published work on biomedical research competencies and emerging scholarship on AI in medical education, and our own experience, twelve tips are organized around three themes: understanding the changing AI landscape, protecting non-delegable human responsibilities, and teaching new AI-era competencies.Conclusions: AI-augmented research does not reduce the need for research education; it changes which skills deserve the most attention. Medical curricula should now emphasize critical appraisal, ethical reasoning, verification of AI outputs, and assessment strategies that distinguish independent mastery from AI-assisted performance.","PeriodicalId":18643,"journal":{"name":"Medical Teacher","volume":" ","pages":"1645-1656"},"PeriodicalIF":4.0000,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical Teacher","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1080/0142159X.2026.2681971","RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/7/7 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}

引用次数: 0

Abstract

Background: The recent advances in Generative Artificial Intelligence (GenAI), from task-specific assistants to autonomous agentic artificial intelligence (AI) are changing how research is conceived, conducted, and written. Across this spectrum AI can now assist with literature searches and synthesis, protocol drafting, statistical analysis, and manuscript preparation, particularly in computational domains. Yet AI outputs remain error-prone, opaque, and carry real stakes for patients, learners, and equitable outcomes, making strong foundational research skills more important than ever.

Purpose: This article offers practical guidance for medical educators responsible for research training in an AI-augmented environment.

Tips: Drawing on published work on biomedical research competencies and emerging scholarship on AI in medical education, and our own experience, twelve tips are organized around three themes: understanding the changing AI landscape, protecting non-delegable human responsibilities, and teaching new AI-era competencies.

Conclusions: AI-augmented research does not reduce the need for research education; it changes which skills deserve the most attention. Medical curricula should now emphasize critical appraisal, ethical reasoning, verification of AI outputs, and assessment strategies that distinguish independent mastery from AI-assisted performance.

查看原文本刊更多论文

人工智能时代教学研究技能的12条建议：卫生专业教育工作者指南。

背景：生成式人工智能（GenAI）的最新进展，从特定任务助手到自主代理人工智能（AI），正在改变研究的构思、实施和写作方式。在这个范围内，人工智能现在可以协助文献搜索和合成、协议起草、统计分析和手稿准备，特别是在计算领域。然而，人工智能输出仍然容易出错、不透明，对患者、学习者和公平的结果都有真正的利害关系，这使得强大的基础研究技能比以往任何时候都更加重要。目的：本文为在人工智能增强环境中负责研究培训的医学教育工作者提供实用指导。根据已发表的关于生物医学研究能力的著作和医学教育中人工智能的新兴学术研究，以及我们自己的经验，我们围绕三个主题组织了12条建议：理解不断变化的人工智能景观，保护不可委托的人类责任，以及教授新的人工智能时代能力。结论：人工智能增强研究并没有减少对研究教育的需求；它改变了哪些技能最值得关注。医学课程现在应该强调批判性评估、伦理推理、人工智能输出的验证，以及区分独立掌握和人工智能辅助表现的评估策略。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Medical Teacher 医学-卫生保健

CiteScore

7.80

自引率

8.50%

发文量

396

审稿时长

3-6 weeks

期刊介绍： Medical Teacher provides accounts of new teaching methods, guidance on structuring courses and assessing achievement, and serves as a forum for communication between medical teachers and those involved in general education. In particular, the journal recognizes the problems teachers have in keeping up-to-date with the developments in educational methods that lead to more effective teaching and learning at a time when the content of the curriculum—from medical procedures to policy changes in health care provision—is also changing. The journal features reports of innovation and research in medical education, case studies, survey articles, practical guidelines, reviews of current literature and book reviews. All articles are peer reviewed.