Introducing AI-generated cases (AI-cases) & standardized clients (AI-SCs) in communication training for veterinary students: perceptions and adoption challenges
Bibliographic record
Abstract
Introduction: The integration of Artificial Intelligence (AI) into medical education and healthcare has grown steadily over these past couple of years, though its application in veterinary education and practice remains relatively underexplored. This study is among the first to introduce veterinary students to AI-generated cases (AI-cases) and AI-standardized clients (AI-SCs) for teaching and learning communication skills. The study aimed to evaluate students' beliefs and perceptions surrounding the use of AI in veterinary education, with specific focus on communication skills training. Methods: = 237), who participated in a 90-min communication skills laboratory activity. Each class was introduced to two AI-cases and two AI-SCs, developed using OpenAI's ChatGPT-3.5. The Calgary Cambridge Guide (CCG) served as the framework for practicing communication skills. Results: Results showed that although students recognized the widespread use of AI in everyday life, their familiarity, comfort and application of AI in veterinary education were limited. Notably, upper-year students were more hesitant to adopt AI-based tools, particularly in communication skills training. Discussion: The findings suggest that veterinary institutions should prioritize AI-literacy and further explore how AI can enhance and complement communication training, veterinary education and practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".