Veterinary students surveyed in several settings are generally interested in AI and see potential value in learning and future practice—but interest is not the same as expertise or trust. The available studies show uneven familiarity with veterinary-specific tools, demand for more training, and caution about checking AI output. They do not establish one representative view shared by veterinary students everywhere.
Do veterinary students use AI?
Yes, especially general text-generating tools. In a 2025 survey of 604 veterinary students at Spain’s CEU Cardenal Herrera University, 49.9% said they used text-generating AI weekly. The survey included students from all five years of the degree and 34 nationalities, but it was conducted at a single faculty, so it should not be treated as a global estimate. The study describes academic use and text generation as prominent.
Use of general-purpose chatbots did not translate into widespread experience with specialized veterinary systems. In that same survey, 64.7% said they had never used veterinary imaging-diagnostic tools, 76.5% had never used clinical management tools, and 69.2% had never used clinical case simulators. Many students also said they did not have enough experience to assess such tools.
Are veterinary students confident about AI in clinical work?
Not uniformly. Students in the Spanish survey appeared more comfortable thinking about AI as a learning aid than evaluating specific clinical uses. More than one in five selected “I do not know” when asked about AI-generated clinical cases, diagnosis, treatment recommendations, or data privacy. That signals uncertainty and limited exposure, not necessarily opposition.
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Do students think AI will help veterinary medicine?
A 2025 survey at the University of California, Davis found substantial interest among respondents. Of 594 veterinary students invited, 176 responded, a 29.6% response rate. Among those respondents, 79% were moderately to extremely interested in learning about AI and machine-learning applications, 59% expected to use such tools in practice, and 77% thought AI/ML would improve veterinary medicine. At the same time, 80% reported slight or no knowledge of AI/ML. The response rate and single-school setting limit how confidently these figures can be generalized. The study is indexed by PubMed.
A separate international cross-sectional survey covered 4,596 students at 192 faculties in 48 countries, but only 78 respondents were veterinary students. Its reported attitudes toward AI in healthcare combine medical, dental, and veterinary students. It offers broad context about health-professions education, not a veterinary-only percentage or a precise measure of veterinary students’ views. The full study also reports a desire for more AI teaching.
Do veterinary students want AI taught in vet school?
The surveyed students indicate that they do. The CEU study found support for faculty involvement in promoting, training, and regulating AI use; the international survey likewise reported demand for more teaching. The authors of the CEU study summarize their finding this way: “The majority believed universities should encourage and regulate AI use.” That statement refers to the study’s single-faculty sample, not all veterinary students.
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Useful instruction would go beyond showing students how to prompt a chatbot. It would help them understand what a tool can and cannot do, evaluate its output, recognize uncertainty, protect sensitive information, and decide when an answer needs checking against reliable clinical sources or a qualified professional. The surveys support these as relevant teaching needs; they do not establish that one particular course design is best.
A classroom example
A small course exercise at City University of Hong Kong illustrates one way to teach critical use. Fifth-year equine students created multiple-choice questions and evaluated ChatGPT’s answers. Among the 22 students surveyed, 72.7% found the exercise moderately to extremely engaging, 77.3% said it deepened their understanding, and 68.2% reported improved critical thinking. Some students also said AI-generated responses caused confusion. Because this was a small study of one class exercise, it is an example rather than proof of broad learning outcomes. The study is indexed by PubMed.
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Are veterinary students worried AI will replace veterinarians?
The studies summarized here do not provide a direct, representative measure of replacement fears across veterinary students. They show interest in AI’s potential and expected future use, alongside low reported knowledge in some cohorts and a desire for instruction. Those findings cannot be converted into a reliable claim about how many students fear replacement or believe it will happen.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What these surveys can—and cannot—tell us
- They show a recurring pattern: surveyed students are interested in AI, want more education, and often favor checking its output.
- They reveal uneven familiarity: general text generation is more familiar than some specialized veterinary applications in the Spanish survey.
- They are not one global poll: the studies differ in country, institution, sample size, response rate, and question focus.
- They do not support a worldwide percentage: the international survey includes only 78 veterinary students among 4,596 respondents, while other studies are institution- or course-based.
Together, the evidence suggests neither blanket enthusiasm nor blanket rejection. It points to students who see potential but need opportunities to build practical knowledge and sound judgment before relying on AI in veterinary settings.
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