Comparison of Generative Artificial Intelligence and Student-Generated Veterinary Handouts
Bibliographic record
Abstract
Generative artificial intelligence (gAI) is becoming increasingly prevalent in our daily lives. Students across multiple disciplines are using gAI for writing assessments and completing projects. This leads to the question: could gAI platforms perform similarly, worse, or better than veterinary students when asked to create discharge handouts for select veterinary neurological conditions? A total of 24 professionals and educators graded handouts based on content, clarity, client education, empathy, and professionalism for two canine neurological conditions (seizures and intervertebral disc disease). Each condition had handouts created by a high-performing student, a random/unknown student whose work was deemed to represent the average student at our institution, ChatGPT 4.0, and Google Bard. The high-performing student's handout scored higher in several categories compared with AI-generated handouts and scored statistically higher overall. Specifically, the high-performing student's seizure handout scored significantly higher in accuracy/completeness and client education than the Bard handout. Both student handouts scored significantly higher for empathy and client support than the AI tools. For the intervertebral disc disease handouts, the AI-generated handouts scored higher in clarity and organization than the random student handout, with the high-performing student's handout scoring higher in empathy and client support over the Bard-generated handout. Upon the conclusion of grading, reviewers completed a survey asking them to guess the authorship of each handout. Veterinary educators and professionals could not distinguish between gAI- and student-developed client handouts. However, the findings suggest that students have the potential to outperform current gAI technology in multiple areas, including conveying empathy.
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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 | 0.010 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".