Aug 2026· Journal of Medical Education and Curricular Development· Vol 13· 0 citations· 8 references
Medicine
TL;DR
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.
Abstract
Introduction A persistent skills gap separates the rapid development of artificial intelligence (AI) tools in medicine from clinicians’ ability to critically evaluate, adopt, and govern them. Existing AI-in-healthcare education is typically delivered through passive, lecture-based formats that do not require learners to apply frameworks to real clinical or institutional problems. Methods We designed and piloted a 10-week, interactive AI in Healthcare curriculum using a Socratic teaching method: each concept is taught, followed by a targeted question the learner must answer before advancing; every task submission receives detailed individualized correction. The curriculum requires each learner to develop an original AI project addressing a real clinical or educational problem, culminating in a published GitHub repository documenting a structured readiness-objectives-adoption-data (R.O.A.D.) analysis, algorithm selection, an ethics and governance framework, and a validation plan. Results Pilot delivery of the curriculum’s foundational module demonstrated feasibility of the interactive correction model, with measurable improvement across four graded learner tasks, including increased precision in formulating measurable, evidence-based project objectives following targeted instructor correction. Discussion 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. We discuss plans for formal assessment validation and full-cohort implementation.
The rapid integration of artificial intelligence (AI) into healthcare is transforming clinical practice and redefining the competencies required of future physicians. As AI increasingly supports diagnostics, clinical decision-making, and healthcare management, medical curricula must evolve to ensure graduates possess the knowledge, skills, and ethical competencies necessary to interact effectively with AI-enabled systems.
A systematic review was conducted following PRISMA guidelines. Literature searches were performed in PubMed, Scopus, and IEEE Xplore for studies published between 2020 and 2025. Eligibility criteria focused on original studies addressing AI competency development, curricular interventions, educational frameworks, and AI-related training within medical education. Following screening and eligibility assessment, 20 studies were included in the final synthesis.
AI literacy was the most frequently identified competency, alongside clinical AI applications, data science, ethical reasoning, critical appraisal, and human–AI collaboration. Integrated and longitudinal curriculum models emerged as the predominant approaches for competency development. Active learning strategies, particularly simulations, workshops, project-based learning, and authentic clinical applications, were consistently associated with positive educational outcomes.
The evidence indicates a transition from isolated AI educational initiatives toward competency-based and longitudinal curriculum integration. Effective AI education extends beyond technical literacy and increasingly incorporates ethical, clinical, and professional competencies necessary for responsible AI adoption in healthcare.
AI competencies should be recognized as a core component of the medical curriculum. Integrated, longitudinal, and active learning-based educational models provide the strongest foundation for preparing future physicians to critically evaluate, ethically govern, and effectively collaborate with AI technologies in clinical practice.
M. Morales-Cevallos, Diego Fabián Vique López, Catherine Hortensia Martínez Avalos et al.· Frontiers in Medicine· 0 citations
Healthcare e-learning operates under pressures that are difficult to satisfy simultaneously: it must be evidence-based, regulator-acceptable, clinically credible, and delivered on time. Established instructional design models, including ADDIE, the Successive Approximation Model, Dick and Carey, and Kemp, were not developed with these combined demands in mind and predate the routine use of artificial intelligence (AI) in content production. The field lacks a methodology that combines the structured governance required for regulated healthcare training, particularly in postgraduate medical education and continuing professional development (CPD), with the responsiveness needed to keep pace with evolving clinical evidence and AI-enabled production.
This article develops and presents INSPIRE, a healthcare-specialized instructional design methodology, as a conceptual contribution. The methodology was developed through a structured synthesis of the strengths and limitations of established instructional design models, integration of recent evidence on artificial intelligence in medical education, and iterative practical refinement in a national health professions education setting. The framework is described in relation to its theoretical foundations, systematically mapped against four established alternatives on six dimensions relevant to healthcare, and illustrated through phase-level integration of AI-supported tasks paired with explicit human checkpoints.
INSPIRE is a seven-phase agile-phased hybrid framework comprising Intake, Needs Analysis, Structure, Production, Implementation, Review, and End Closure. It treats early technical integration, continuous quality assurance, and human-in-the-loop use of artificial intelligence as core design commitments rather than optional additions. The framework addresses three combined gaps that no single existing model resolves: it integrates and organizes agility and governance into a single workflow suited to compliance-bound healthcare education; it formalizes the role of artificial intelligence across the lifecycle while retaining clinical and pedagogical accountability with human experts; and it makes quality review a continuous rather than terminal function.
INSPIRE integrates phase-based governance, iterative production, continuous quality assurance, and human-in-the-loop AI use in one workflow built for healthcare e-learning. The contribution is conceptual; empirical validation against time-to-delivery, learner outcomes, and accreditation-quality measures is the next step. National regulators, postgraduate training boards, and CPD providers are the intended adopters.
Hilalah Alturkistani, Fahad Almalki· BMC 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 (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
Paramedic education is entering a pivotal era as artificial intelligence (AI) rapidly evolves from decision-support tools to sophisticated learning and simulation technologies. This article examines which elements of paramedic training should remain inherently human, which are best delivered through hybrid human–AI models, and which tasks can be ethically and safely automated with oversight. Core relational competencies such as delivering bad news, communicating prognostic uncertainty and demonstrating empathy are identified as irreducibly human owing to their emotional, ethical and interpersonal complexity. Hybrid approaches show promise in simulation, assessment and clinical reasoning, where AI can enhance exposure, personalise feedback and support structured decision-making, while retaining expert supervision. Certain pattern-recognition or administrative tasks, including electrocardiogram (ECG) interpretation and teaching-material generation, can be responsibly automated within governance frameworks. The authors propose a curriculum-design framework and implementation strategy to guide educators in developing AI-literate, compassionate practitioners while ensuring transparency, safety and ongoing evaluation.
Chris Elliott, Willie M. Edwards· Journal of Paramedic Practic...· 0 citations
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.
Neil Mehta, Jennifer Benjamin, Seysha Mehta et al.· Medical Teacher· 0 citations