Agentic education · Learning science

Actionable companion · Cognitive load & attention

Learning with agency: Artificial Intelligence in Veterinary Education: Preparing the Workforce for Clinical Applications in Diagnostics and Animal Health

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

Read original paperSearch paper agentCC BY 4.0

Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Health-professions learning must be distinguished from K–12 instruction.

Original authors’ abstract, reproduced under the linked source license:

Simple Summary Artificial intelligence (AI) is increasingly used in veterinary medicine to support diagnosis, disease surveillance and clinical decision-making. To ensure that these technologies are applied safely and responsibly in practice, veterinarians must be adequately trained during their education. This review explores how AI is currently being incorporated into veterinary curricula and how the educational use of AI can prepare students for future clinical applications. We describe the main types of AI tools used in veterinary education, including generative and multimodal models, virtual and augmented reality, and decision support systems across areas such as imaging, epidemiology, parasitology, food safety and animal health. The review also highlights important challenges related to ethics, data privacy, bias and overreliance on AI. Overall, we show that veterinary education plays a key role in bridging technological innovation and clinical practice, helping future veterinarians to develop the skills needed to use AI in a safe, ethical and clinically meaningful way. Keywords: artificial intelligence, higher education, veterinary medicine, multimodal learning, educational innovation Abstract Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is rapidly transforming clinical veterinary practice by enhancing diagnostics, disease surveillance and decision support processes across animal health domains. The safe and effective clinical deployment of these technologies, however, depends critically on the preparedness of the veterinary workforce, positioning veterinary education as a strategic enabler of translational adoption. This narrative review examines the integration of AI within veterinary education as a foundational step toward its responsible application in clinical practice. We synthesize current evidence on AI-driven tools relevant to veterinary curricula, including generative and multimodal large language models, intelligent tutoring systems, virtual and augmented reality platforms and AI-based decision support tools applied to imaging, epidemiology, parasitology, food safety and animal health. Particular attention is given to how the structured educational use of AI mirrors real-world clinical workflows and supports the development of competencies essential for clinical translation, such as data interpretation, uncertainty management, ethical reasoning and professional accountability. The review further addresses ethical, regulatory and cognitive considerations associated with AI adoption, including algorithmic bias, data privacy, equity of access and the risks of overreliance, emphasizing their direct implications for diagnostic reliability and animal welfare. By framing veterinary education as a controlled and reflective environment for AI engagement, this article highlights how pedagogically grounded training can facilitate safer clinical deployment, foster interdisciplinary collaboration and align technological innovation with professional standards in veterinary medicine.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Evidence synthesis

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This study was conducted as a narrative review with scoping elements, explicitly designed to synthesize and contextualize the existing literature, rather than to conduct empirical educational research [14]. Accordingly, this review does not follow a protocol-driven systematic or PRISMA-based scoping methodology but adopts a flexible and interpretative approach appropriate for synthesizing a rapidly evolving and heterogeneous body of literature. A comprehensive literature search was carried out using Scopus, Web of Science, ERIC, PubMed and Google Scholar, focusing primarily on publications from 2020 to 2025 in order to capture the most recent developments in AI-supported educational practices. Search terms included combinations of “artificial intelligence”, “generative AI”, “ChatGPT”, “large language models”, “autonomous learning”, “veterinary education”, “health professions education”, “higher education”, “simulation” and “multimodal learning”.

Limitations and uncertainty · source passage

The rapid growth of AI in veterinary education demands not only an exploration of its pedagogical benefits but also critical reflection on its risks and limitations. Ethical concerns (including data privacy, algorithmic bias, equity of access, academic integrity and the potential erosion of students’ independent reasoning) must be addressed to ensure that AI supports rather than replaces human expertise in clinical decision making [12]. Furthermore, regulatory frameworks in animal health and veterinary medical practice add a layer of complexity that institutions must navigate with care.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

  • Limit simultaneous instructions and unnecessary dialogue. Compare task performance and a suitable mental-effort measure; easier-feeling work is not automatically better learning.
  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
  • Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Esteban Pérez-García; Ana S Ramírez; Miguel Ángel Quintana-Suárez; Magnolia M Conde-Felipe; Conrado Carrascosa; Inmaculada Morales; Juan Alberto Corbera; Esther SanJuan; Jose Raduan Jaber. Artificial Intelligence in Veterinary Education: Preparing the Workforce for Clinical Applications in Diagnostics and Animal Health. Veterinary Sciences (2026-02-12). https://doi.org/10.3390/vetsci13020181

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.