Jul 2026· Medical Teacher· pp.
1-12
· 0 citations· 21 references
Medicine
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.
Artificial intelligence (AI), treated in this study as an umbrella term for AI-enabled clinical and educational technologies rather than as a single platform, is reshaping medical education, including how diagnostic skills, treatment planning, and patient care are taught. This study examines AI integration in medical education through the perceptions and readiness of clinical educators. Guided by the Unified Theory of Acceptance and Use of Technology, the study explores factors influencing AI adoption in medical training, including performance expectancy, effort expectancy, social influence, and facilitating conditions. In this exploratory study, semi-structured interviews were conducted with 15 clinical educators in the south-central United States who supervise third-year medical students. Findings suggested six recurring themes: the technological learning curve, the need for hands-on learning, institutional support, mentorship, preservation of human elements, and generational differences in comfort with AI. While some AI-enabled applications may support adaptive and personalized learning, educators expressed concerns about maintaining empathy, patient interaction, and human-centered care. The findings suggest that effective AI integration may require strategic institutional support, ongoing training, and pedagogical change. This study provides insight into developing AI-ready medical education models that balance technical competence with humanistic values.
T. Murphy, Ginger Vaughn, Rob E. Carpenter et al.· International Medical Educat...· 0 citations
This article examines the evolving role of artificial intelligence (AI) as a transformative cognitive partner across educational and professional fields. It explores how AI shifts attention away from routine technical tasks and toward human‐centered skills such as critical thinking, ethical decision‐making, and professional judgment. For example, in medicine and the military, AI supports learning through realistic simulations and personalized training experiences that strengthen essential skills. The article also emphasizes that, despite AI's ability to process data and identify patterns, it cannot replicate empathy, moral reasoning, or lived experience. As a result, integrating AI into education and professional practice requires redefining competence within human systems shaped by technology. The author stresses the importance of critically evaluating AI‐generated outputs and maintaining human responsibility in decision‐making. Ultimately, AI is presented not as a replacement for human expertise, but as a tool that enhances efficiency while supporting ethical and meaningful professional practice.
R. Wlodarsky· New Directions for Adult and...· 0 citations
This curriculum offers a generalizable, no-cost model for closing the AI evaluation skills gap in healthcare education, combining interactive correction with a mandatory, learner-defined capstone project.
Hiba Hamdar· Journal of Medical Education...· 0 citations
BACKGROUND
Artificial intelligence (AI) is transforming higher education and healthcare, yet nursing faculty lack practical guidance for determining appropriate AI use in specific academic tasks. Institutional AI policies establish boundaries but rarely address learning outcomes, task purposes, or nursing-specific responsibilities such as patient privacy, clinical judgment development, and professional accountability.
PURPOSE
To present the AI Use Framework for Nursing Education, a six-category pedagogical framework that operationalizes responsible AI integration at the task level.
METHODS
A 10-member taskforce adapted the Artificial Intelligence Assessment Scale through targeted review of existing frameworks, iterative refinement guided by four principles (transparency over detection, learning outcome alignment, nursing-specific contextualization, and developmental scaffolding), and consultation with faculty across pre-licensure through doctoral programs.
RESULTS
The framework provides six categories: (a) No AI, (b) AI-Assisted Editing and Formatting, (c) AI-Assisted Planning and Ideation, (d) AI-Assisted Content Creation with limited and extensive subcategories, (e) AI System Evaluation and Research, and (f) AI Application Design and Development. Three distinguishing features include embedded protected health information/personally identifiable information safeguards, differentiated process evidence recommendations enabling faculty to tailor documentation requirements to learning outcomes and task complexity, and Bloom's-aligned separation of AI evaluation from AI creation activities.
CONCLUSION
The framework bridges institutional policy and pedagogical practice through healthcare-specific adaptations. Implementation requires comprehensive infrastructure including AI literacy education, institutional guidelines, and faculty support. This structured, task-level guidance positions nursing education to lead intentional AI integration while preparing students for AI-enabled practice environments.
Artificial intelligence is reshaping medical education at a pace that outstrips most institutions' capacity to respond, yet practical, multi-domain guidance for educators remains limited. This article translates the first three levels of the International Advisory Committee for Artificial Intelligence (IACAI) Learner Matrix, the Intrapersonal, Micro, and Meso levels of a five-level socio-ecological framework, into 12 practical tips for educators and curriculum designers. The tips span foundational AI culture and literacy, ethical practice, critical tool evaluation, learning design, clinical skills integration, assessment, career preparation, curriculum co-design, research integrity, workforce readiness, and learner well-being. Each tip identifies a core challenge and offers concrete implementation strategies at the learner and institutional levels. Taken together, the tips provide a coherent, learner-centered scaffold to help educators worldwide prepare learners for AI-augmented practice.
Diego F. Niño, K. Masters, Rakesh Patel et al.· Medical Teacher· 0 citations
Artificial intelligence (AI) is being increasingly utilized in medical education, with growing interest in its potential to enhance learning, assessment, and faculty support across undergraduate, postgraduate, and continuing medical education. While much of the literature focuses on specialty training or technology-driven innovation in isolation, family medicine represents a distinct educational domain characterized by its breadth, clinical uncertainty, multimorbidity, longitudinal care, prevention, and shared decision-making. These features present specific educational challenges and opportunities for integrating AI-supported tools. This narrative review synthesizes peer-reviewed literature on the application of AI in medical education, specifically within the context of family medicine. This review utilized PubMed and a consensus search engine to identify relevant reviews, empirical studies, and articles that address AI-supported learning, simulation training, competency-based education, workplace assessment, faculty support, and educational governance. The evidence was integrated through a narrative synthesis, guided by the Scale for Assessment of Narrative Review Articles. The literature indicates that AI can facilitate personalized learning pathways, augment simulation-based training, and aid in the integration of longitudinal assessment data, particularly in educational contexts characterized by distributed supervision and diverse learner requirements. There is a paucity of evidence concerning the application of AI in high-stakes assessments, autonomous decision-making, and its long-term impact on professional identity formation. In all areas, the successful incorporation of AI necessitates robust educational governance, faculty oversight and alignment with fundamental educational principles. AI should be considered an augmentative educational technology that can enhance but not supplant human-centered teaching, supervision, and professional judgment. Future research should prioritize the validation of educational outcomes, the clarification of ethical safeguards, and the establishment of best practices for the responsible integration of AI into family medicine education.
C. Wiedermann, Anne Wiedermann, Hendrik Reismann· Journal of Medical Education...· 0 citations