Simulation, artificial intelligence, and competency-based frameworks in veterinary medical education: a narrative review.

importanceVeterinary medical education is rapidly evolving through the adoption of simulation, virtual reality (VR), artificial intelligence (AI), and competency-based frameworks. These innovations aim to address long-standing challenges such as limited patient availability, variable case exposure, and inconsistent assessment practices across institutions. OBSERVATIONS: Existing studies show that high-fidelity simulators and VR improve procedural confidence, skill acquisition, and learner satisfaction while reducing the dependence on live animals. AI-assisted learning systems support decision-making and personalized instruction but raise concerns about accuracy, bias, and data governance. Competency-based education frameworks, including entrustable professional activities and programmatic assessment, can improve workplace readiness, but their application varies widely between veterinary schools. Persistent barriers include unequal access to digital resources, high implementation costs, and limited faculty training. CONCLUSIONS AND RELEVANCE: Evidence from the literature suggests that, when integrated appropriately, simulation technologies, digital learning environments, and AI-assisted tools collectively enhance the key elements of veterinary training. These approaches can support safer clinical practice, help standardize skill development, and broaden access to quality training, particularly in resource-limited settings. Instead of hypothesizing new mechanisms, this review uses current evidence to highlight practical implications for curriculum design and clinical preparedness. Continued investment in faculty development, ethical use of AI, and infrastructure is essential for achieving sustainable, technology-enhanced veterinary education.