Competency-Based Veterinary Education (CBVE) Drives Increased Use of Digital Simulations and AI

Competency-Based Veterinary Education (CBVE) Drives Increased Use of Digital Simulations and AI

At the end of my formal academic career a decade ago, I chaired a “pre-clinical” department responsible for instruction in veterinary anatomy, physiology, histology, neurobiology, pharmacology and toxicology. Since then, the veterinary education has experienced multiple waves of seed changes.   Even in 2016, there were the expense and public sensitivity to the use dead animals for instruction, and the concern over student exposure to embalming solutions. The use of live animals for laboratories and surgical practice had long been limited on animal humane grounds.  

The introduction of a new curriculum 15 years ago at the University of Illinois College of Veterinary Medicine brought first and second year students into 8 weeks of clinical blocks alongside final-year students. It quickly became apparent that these students were eager but not properly prepared to appreciate this early clinical exposure’s value in enhancing the relevance of the pre-clinical classroom instruction.  The curricular result was expansion of the Clinical Skills Laboratory and of the nature of Objective Subjective Clinical Examinations (OSCEs).  At first these focused on the manual processes associated with clinical practice: draping a patient, tying surgical knots, etc.  They were not initially designed to evaluate clinical reasoning skills. As a simple example, we still evaluated first and second-year student competency with dosage calculation with a set of online multiple-choice quizzes.    The “on-the-fly” assessment of their interpretation of an antibiotic culture and sensitivity, and prescribing ability was only going to come later.

Then, the Competency-Based Veterinary Education (CBVE) initiative by the American Association of Veterinary Medical Colleges moved through its phases to the now refined 32 Competencies in 9 Domains with granular performance Milestones,  and 8 Entrustable Professional Activities (EPAs)  https://www.aavmc.org/resources/competency-based-veterinary-education/cbve-education-2-0-model/ .  Veterinary schools, particularly the newly developed ones, are designing their curricula around this framework, and existing schools are trying to identify where these competencies are taught and/or redesigning their curricula to ensure that they are.

As a recent narrative review describes, the educational simulation technology, clinical skills labs, and externally constructed competency framework are now being supplemented by artificial intelligence (AI).¹  

The potential of AI is not only in its “out-of-the-box” capability to create multiple choice questions for a student,² or pass an examination ³, although the capabilities of the AI models are becoming more and more impressive.   It is about the potential to interact with specialized or even customized medical professional information in databases (knowledgebases), and about the potential to interact with simulators to customize learning experiences.¹  Large Language models (LLMs) can help faculty identify where in a curriculum the competencies are being taught and evaluate whether student progress is being adequately measured against the Milestone standards.

I can recall the excitement in the early 1990’s when videodisks of interactive educational programs were going to change the nature of veterinary education.  However, the subsequent pace of development was slow, and the external review and national or international standards not yet developed.   Four decades later, the veterinary profession now could work virtually and collectively on educational programs and standards with the aid of AI.  The costs of educationally effective approaches might be higher, but our we can no longer afford the tendency to reinvent the wheel at each of our institutions. The truth is that we never could, but we also probably never gave faculty the credit necessary for inter-institutional cooperation on educational research and development, like we did for other kinds of research.  There are examples of such inter-institutional cooperation over AI-driven simulated experiences for medical students.⁴   Why shouldn’t we work together to conduct the necessary educational research in a cooperative manner?  Our curricula, faculty,  and, certainly, our students deserve it.

References

1.    Zhang H, Li A,Ying Li, Nab F, Dong H, Ma Y: Simulation, artificial intelligence, and competency-based frameworks in veterinary medical education: a narrative review. J Vet Sci. 2026 May;27(3):e37  https://vetsci.org/DOIx.php?id=10.4142/jvs.25194

2.    2. Sousa SA, Flay KJ (2025): A survey of veterinary student perceptions on integrating ChatGPT in veterinary education through AI-driven exercises. J. Vet. Med. Ed. 52(6): 734-742.
https://doi.org/10.3138/jvme-2024-0075

3.    3. Coleman MC, Moore JN (2024): Two artificial intelligence models underperform on examinations in a veterinary curriculum. JAVMA 262(5):692-697.
https://doi.org/10.2460/javma.23.12.0666

4.    4. Hicke Y, Geathers J, Vu K et al. (2026): MedSimAI: Simulation and formative feedback generation to enhance deliberate practice in medical education. In LAK26: 16th International Learning Analytics and Knowledge Conference, April 27-May 01, 2026, Bergen, Norway. ACM, New York, NY, USA. 11 pages. https://doi.org/10.1145/3785022.3785092