JMIR Medical Education

Undergraduate students, including those preparing for health professions, report high rates of psychological distress and underuse of traditional counseling services. Credit-bearing wellness courses that combine psychoeducation with experiential learning may offer a scalable, curriculum-based approach to supporting student well-being.

Generative artificial intelligence (GenAI) tools are being increasingly applied to teaching and learning in medical education creating both instructional opportunities and pedagogical challenges. While GenAI offers potential to enhance teaching, assessment, and curriculum design, many medical faculty lack structured guidance on how to integrate these tools ethically and pedagogically within discipline-specific, high-stakes educational contexts.

Assessment is a critical component of teaching and learning and serves as the foundation for how learners demonstrate success in achieving learning objectives. Formative assessments (FAs) and timely feedback play a crucial role in integrated curricula, whereas basic and clinical sciences are taught in a coordinated manner. Feedback-based FA supports student learning, and teachers can determine learning gaps to monitor progress in learning. Based on existing evidence, limited literature compared the effect of online versus onsite FA on summative performance in a fully integrated curriculum.

Health professions education faces increasing challenges from rising health care complexity, pedagogical shifts, and constrained curricular space, and rapidly expanding knowledge and technological advances. While artificial intelligence (AI) shows promise for transforming health professions education, evidence of its effectiveness remains unclear.

Three-dimensional modalities are increasingly being used as adjuncts for medical trainees learning about complex anatomical concepts, such as congenital heart disease.

Digital entrustable professional activities (EPAs) in simulated environments may accelerate competency acquisition, but adoption depends on learner acceptance. The Technology Acceptance Model (TAM) posits that perceived usefulness (PU) and perceived ease of use (PEU) shape attitudes (AT) and, in turn, behavioral intention (BI).

Graduate health informatics programs in the United States differ widely in cost, curriculum, and program design. However, it is unclear how these differences influence affordability, accreditation signaling, and preparation for a data-driven workforce.

The increasing adoption of virtual reality (VR) in medical education offers substantial opportunities for immersive, practice-oriented training that complements traditional teaching methods. In particular, VR enables repeated, risk-free exposure to complex clinical scenarios and supports the development of clinical reasoning, communication skills, and procedural competence. However, implementing VR-based courses remains challenging due to high development costs, technical complexity, and the need for close interdisciplinary collaboration. This tutorial presents key insights and best practices from the medical tr.AI.ning project, a 3-year interdisciplinary initiative funded by the German Federal Ministry of Education and Research. The project’s objective was to develop an artificial intelligence (AI)-supported, VR-based training platform that allows medical students to practice clinical decision-making in immersive, interactive scenarios. The paper is structured as a tutorial and offers recommendations for planning, developing, and integrating VR courses into medical curricula. Each recommendation is illustrated with concrete examples from our project, serving as a practical blueprint to guide educators and developers in applying these guidelines in their own contexts. Successful implementation of a VR project in medical education requires strategic planning and collaboration, starting with a thorough identification of curricular gaps that VR can address and a clear justification of its added educational value. An interdisciplinary consortium that combines expertise from medical didactics experts, computer science, and design is essential to ensure the development of high-quality, pedagogically sound simulations and intuitive user interfaces. Key factors for success include defining specific learning objectives aligned with competency-based frameworks; iterative development with continuous feedback from medical experts, educators, and students; and structured pilot testing with systematic collection of quantitative and qualitative data to assess usability, immersion, and learning outcomes. Early engagement and walkthroughs with end users help identify practical challenges and inform iterative improvements. A dedicated authoring tool within the project allows medical teachers to create and adapt VR scenarios without prior technical experience, supporting the scalability and sustainability of the approach. Effective project management frameworks facilitate collaboration, clear task allocation, and adaptive progress throughout development. Additionally, considerations for hardware selection, technical infrastructure, and sustainable dissemination strategies, including open-access publications, project websites, and professional networking, are crucial to ensure long-term viability and broad adoption across institutions. By combining a tutorial format with practical, step-by-step recommendations, this article provides a comprehensive guide for educators and developers on implementing immersive, AI-supported VR courses to enhance medical education. It highlights key lessons learned in interdisciplinary collaboration, iterative testing, systematic evaluation, and alignment with educational objectives, thereby facilitating the effective, evidence-based, and sustainable integration of VR into medical curricula across diverse institutions.

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The rapid integration of artificial intelligence (AI) into clinical practice necessitates urgent restructuring of medical education and physician assessment to ensure that future physicians are proficient and responsible users of AI tools. Despite the existence of core AI competencies, the current state of AI education in Canadian undergraduate medical education is highly inconsistent and disjointed, and available data indicate that most medical students receive minimal to no formal AI training even as they anticipate that AI will profoundly shape their future careers. National policy, specifically the Pan-Canadian AI for Health Guiding Principles, has advanced the agenda by calling for AI literacy among health professionals and emphasizing core values such as equity, robust data practices, and Indigenous-led data governance. However, these principles offer limited practical guidance on the educational and regulatory mechanisms required for effective implementation. We contend that this critical implementation deficit arises from a traditional, sequential reform model in which faculty development, curriculum change, and regulatory updates occur in isolation. This slow, siloed approach is fundamentally inadequate for addressing AI’s inherent speed, opacity, and significant equity risks. To overcome this challenge, we propose a 3-lever concurrent implementation framework that provides a conceptual lens to address the interdependencies among faculty development, curriculum, and regulation. This model posits that AI competencies transition from abstract requirements to practical application only when 3 levers—clinician-educator capacity, digitally enabled learning environments, and regulatory and assessment reform—are activated simultaneously and in alignment. This Viewpoint extends existing AI competency frameworks by theorizing AI curriculum implementation as a problem of concurrent lever activation and by outlining minimum concurrent actions for deans and regulators that can be adapted to competency-based medical education systems. Although illustrated with Canadian examples, the framework is designed to be transferable beyond Canada and offers a testable, licensure-level blueprint for embedding AI competence in medical education.

Generative artificial intelligence (GenAI) is rapidly expanding in higher education and clinical practice. However, its use during clinical placements, where cognitive demands and responsibility for patient care increase, remains insufficiently documented.

Artificial intelligence (AI) is reshaping medical and health professions education; yet, adoption in anatomy remains uneven and often ad hoc. Anatomy’s spatial and visualization demands make it a compelling domain for AI, but discipline-specific opportunities and risks are not well characterized in the United Arab Emirates.