Twelve tips for teaching research skills in the age of agentic AI: A guide for health professions educators.
TL;DR
Medical curricula should now emphasize critical appraisal, ethical reasoning, verification of AI outputs, and assessment strategies that distinguish independent mastery from AI-assisted performance, according to the changing AI landscape.
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.