AI in Veterinary Education: A Fast, Practical Guide

AI is reshaping veterinary education—building AI literacy into curricula, powering simulation labs with instant feedback, and preparing graduates to work alongside decision-support tools already common in clinics. The shift spans the classroom, skills labs, and continuing education, with early pilots and strong student interest pointing the way.

Why it matters

Students and educators see clear value: better diagnostics, clearer client communication, and smarter study habits. Competence with AI is fast becoming part of practice readiness, with CE tracks and faculty development catching up across the profession.

Where it fits in the curriculum

Replace ad-hoc exposure with a structured thread:

• Fundamentals: Core concepts, capabilities, and limits of AI in medicine.

• Clinical applications: Imaging triage, decision support, workflow automation.

• Ethics & safety: Bias, privacy, disclosure, and human oversight.
  Short electives can evolve into required modules as resources grow.

Simulation and XR (what to deploy first)

VR/AR labs provide safe, repeatable practice for anatomy and procedural skills. Purpose-built modules (e.g., anesthesia induction, canine intubation) let learners rehearse high-stakes steps before live-animal practice. Add AI to track actions and timing, adapt scenarios, and deliver targeted feedback—at scale.

Communication training (beyond role-play)

AI-generated cases and AI-standardized clients expand scenario volume and consistency. Students can practice history-taking, risk explanation, and cost conversations, then receive structured feedback that aligns with rubric-based assessment.

Research and LLM skills

Teach prompt design, literature synthesis, and responsible drafting for client handouts and academic writing. Make verification, citation, and data-privacy policies explicit so students learn to use these tools confidently—and critically.

Ethics and animal welfare

AI-enabled simulations support the “reduce and refine” principle by limiting live-animal use where appropriate. Courses should also cover model limitations and hallucinations, reinforcing that clinicians—not algorithms—own final decisions.

Assessment and feedback

AI-enhanced simulators log decision paths and timing to power objective, competency-based assessment. Standardized AI cases help ensure consistent grading across cohorts while lowering the staffing burden of traditional standardized patient programs.

Implementation roadmap

• Start with a foundations module anchored in real veterinary cases and common tools.

• Pilot one AI-augmented XR skill (e.g., intubation), measure skill acquisition and retention, then expand to anatomy or anesthesia.

• Integrate AI casework into communications courses with clear rubrics and reflection prompts.

• Embed LLM literacy in research methods and clinical communication, including policies for verification, attribution, and data handling.

• Create a faculty playbook with example assignments, assessment templates, and guidance on classroom use.

Bottom line

Treat AI as a core competency, not an add-on. Build literacy early, practice skills in AI-enabled simulations, and graduate veterinarians ready to collaborate with decision-support tools—safely, ethically, and effectively.