Agentic education · Learning science

Actionable companion · Learning design & outcomes

Learning with agency: From Tradition to Future in Veterinary Public Health Education: Students’ Attitudes, Anxiety and Experiences With AI‐Supported Learning

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.

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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): Age / stage not established. Verify the participant population and task before transferring the findings.

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

ABSTRACT Background Artificial intelligence (AI) has the potential to reshape learning processes, especially through tools such as ChatGPT. Objectives This study aims to evaluate veterinary students’ attitudes towards AI‐supported veterinary public health education applications, their anxiety levels and their experiences regarding the education process. Methods The study adopted a mixed method design in which quantitative and qualitative methods were used together. The study was conducted with final‐year veterinary students (n = 60) enrolled in the Veterinary Public Health course. This study consisted of four phases: (1) the Artificial Intelligence Anxiety Scale (AIAS) was administered as a pre‐test, (2) traditional face‐to‐face training was given, (3) the same topic was repeated with a ChatGPT‐based AI‐supported exercise and (4) after the trainings, the AIAS was administered again and post‐test was conducted and a questionnaire consisting of closed and open‐ended questions was administered to the students. Factor analysis revealed that the scale had high internal consistency (Cronbach's α > 0.90). Results Analysis of the scale scores revealed that veterinary students exhibited moderate anxiety towards AI and that AI‐focused education did not significantly alter this anxiety in the short term. In the content analysis of qualitative data, students stated that they benefited from the aspects of AI such as fast access to information, practicality and time‐saving; the same time, they expressed concerns about ethical concerns, information reliability and professional role change. Conclusion Overall, the findings indicate a duality in students’ potential attitudes towards AI. These findings indicate the applicability of AI‐based educational practices in the context of veterinary public health education and point to the need for multidimensional evaluation of student attitudes. Keywords: anxiety, artificial intelligence, ChatGPT, perceptions, veterinary public health

What kind of evidence is this?

Supporting AI learning science · Comparative / pre-post study reported

Stage: Age / stage not established. 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

Informed consent was obtained from all participants prior to the study. To ensure confidentiality, the responses were kept anonymous. Each participant was assigned a random number, and the analyses were carried out based on these codes. The researchers did not have access to information linking individual responses to specific students.

Limitations and uncertainty · source passage

This study has several limitations that should be acknowledged. First, the study was conducted at a single veterinary faculty, which may limit the generalizability of the findings. Second, the wide variation in participants’ prior AI experience may have influenced their perceptions and anxiety scores. Third, the short‐term nature of the training limited the ability to capture long‐term changes in anxiety or perceptions. Finally, the data were based on self‐reported measures, which may be subject to bias. Future research should therefore include larger, multi‐institutional samples, longitudinal designs and objective performance outcomes.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.

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. Treat the paper as background evidence until its population is verified in the full text. Do not assign an age-specific intervention or extend adult results to children. 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. Extract the participant ages or education stage, recruitment, task and comparator before selecting a local pilot. If these cannot be established, retain the paper for design discussion rather than age-band efficacy claims. 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

Selçuk Alan; Gönül Damla Büyük; Seda Çavuş Alan. From Tradition to Future in Veterinary Public Health Education: Students’ Attitudes, Anxiety and Experiences With AI‐Supported Learning. Veterinary Medicine and Science (2026-07-01). https://doi.org/10.1002/vms3.71056

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.