Surveyed veterinary students in Australia find ChatGPT practical and relevant while expressing no concern about artificial intelligence replacing veterinarians
Open accessAbstract
Background: Chat Generative Pre‐trained Transformer (ChatGPT) is a freely available online artificial intelligence (AI) program capable of understanding and generating human‐like language. This study assessed veterinary students’ perceptions about ChatGPT in education and practice. It compared perceptions about ChatGPT between students who had completed a critical analysis task and those who had not. Methods: This cross‐sectional study surveyed 498 Doctor of Veterinary Medicine (DVM) students at The University of Sydney, Australia. Second‐year DVM students researched a veterinary pathogen and then completed a critical analysis of ChatGPT (version 3.5) output for the same pathogen. A survey based on the Technology Acceptance Model was then delivered to all DVM students from all years of the programme, collecting data using Likert‐style, categorical and free‐text items. Results: Over 75% of the 100 respondents reported having used ChatGPT. The students found ChatGPT's output relevant and practical for their use but perceived it as inaccurate. They perceived ChatGPT output to be more useful for veterinary students than for pet owners or veterinarians. Those who had completed the critical analysis assignment had a more positive view of ChatGPT's practicality for veterinary students but noted its authoritative tone even when delivering inaccurate information. Over 50% of the students agreed that information about tools such as ChatGPT should be included in the veterinary curriculum. Students agreed that veterinarians should embrace AI but disagreed that AI would eventually replace the need for veterinarians. Conclusions: A critical appraisal of outputs from AI tools such as ChatGPT may help prepare future veterinarians for the effective use of these tools.
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Although the number of respondents exceeded the required sample size, the response rate was lower compared with similar studies, which may limit the generalizability of the findings.
“First, although the number of respondents exceeded the required sample size, the response rate was lower compared with similar studies [19, 21, 22, 24, 25, 29, 31, 34, 37, 40, 62], which may limit the generalizability of the findings.”
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Cites
(1932) · Medical Entomology and Zoology · Cited by 9,568 · see citing works
Technology Acceptance Model 3 and a Research Agenda on Interventions
Venkatesh, V., & Bala, H. (2008) · Decision Sciences · doi:10.1111/j.1540-5915.2008.00192.x · Cited by 8,159 · see citing works
(1985) · Cited by 6,185 · see citing works
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087 · see citing works
Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079 · see citing works
Best practices for missing data management in counseling psychology
Schlomer, G. L., Bauman, S., & Card, N. A. (2010) · Journal of Counseling Psychology · doi:10.1037/a0018082 · Cited by 1,835 · see citing works
Gadermann, A., Guhn, M., & Zumbo, B. D. (2020) · PubMed · doi:10.7275/n560-j767 · Cited by 1,233 · see citing works
Exploring Students’ Perceptions of ChatGPT: Thematic Analysis and Follow-Up Survey
Shoufan, A. (2023) · IEEE Access · doi:10.1109/access.2023.3268224 · Cited by 499 · see citing works
Vahdat, A., Alizadeh, A., Quach, S., & Hamelin, N. (2020) · Australasian Marketing Journal (AMJ) · doi:10.1016/j.ausmj.2020.01.002 · Cited by 310 · see citing works
The role of ChatGPT in scientific communication: writing better scientific review articles.
(2023) · PubMed Central · Cited by 289 · see citing works
Alawida, M., Mejri, S., Mehmood, A., Chikhaoui, B., & Abiodun, O. I. (2023) · Information · doi:10.3390/info14080462 · Cited by 211 · see citing works
Zimmerman, D. W., & Zumbo, B. D. (1993) · Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · doi:10.1037/h0078850 · Cited by 203 · see citing works
ChatGPT in physics education: A pilot study on easy-to-implement activities
Bitzenbauer, P. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13176 · Cited by 181 · see citing works
Caruccio, L., Cirillo, S., Polese, G., Solimando, G., Sundaramurthy, S., & Tortora, G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121186 · Cited by 158 · see citing works
Using ChatGPT for language editing in scientific articles
Kim, S.-G. (2023) · Maxillofacial Plastic and Reconstructive Surgery · doi:10.1186/s40902-023-00381-x · Cited by 153 · see citing works
Ali, K., Barhom, N., Tamimi, F., & Duggal, M. (2024) · European Journal of Dental Education · doi:10.1111/eje.12937 · Cited by 152 · cited 1× · see citing works
“7 Such concerns arise from reports that ChatGPT can pass examinations designed for veterinary 8 and dentistry students 9 and produce essays with fake references.”
Chatbot usage intention analysis: Veterinary consultation
Huang, D.-H., & Chueh, H.-E. (2020) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2020.09.002 · Cited by 138 · see citing works
Dahlkemper, M. N., Lahme, S. Z., & Klein, P. (2023) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.19.010142 · Cited by 126 · see citing works
Das, D., Kumar, N., Longjam, L. A., Sinha, R., Deb Roy, A., Mondal, H., & Gupta, P. (2023) · Cureus · doi:10.7759/cureus.36034 · Cited by 116 · see citing works
The unmet potential of artificial intelligence in veterinary medicine
Basran, P. S., & Appleby, R. B. (2022) · American Journal of Veterinary Research · doi:10.2460/ajvr.22.03.0038 · Cited by 55 · see citing works
Cohen, E. B., & Gordon, I. K. (2022) · Veterinary Radiology & Ultrasound · doi:10.1111/vru.13171 · Cited by 50 · see citing works
Kim, E., Fischetti, A. J., Sreetharan, P., Weltman, J. G., & Fox, P. R. (2022) · Veterinary Radiology & Ultrasound · doi:10.1111/vru.13062 · Cited by 43 · see citing works
Fraiwan, M., & Abutarbush, S. M. (2020) · Journal of Equine Veterinary Science · doi:10.1016/j.jevs.2020.102973 · Cited by 42 · see citing works
Are ChatGPT and other pretrained language models good parasitologists?
Šlapeta, J. R. (2023) · Trends in Parasitology · doi:10.1016/j.pt.2023.02.006 · Cited by 29 · see citing works
Self-citationMedical students’ perceptions of the impact of artificial intelligence in radiology
Galán, G. C., & Sendra‐Portero, F. (2022) · Radiología (English Edition) · doi:10.1016/j.rxeng.2021.03.008 · Cited by 20 · see citing works
Australian perspectives on artificial intelligence in veterinary practice
Currie, G. M., Hespel, A., & Carstens, A. (2023) · Veterinary Radiology & Ultrasound · doi:10.1111/vru.13234 · Cited by 12 · see citing works
Veterinarians are reluctant to recommend virtual consultations to a fellow veterinarian
Sigesmund, D., Coe, J. B., Khosa, D. K., & Moore, I. C. (2023) · Journal of the American Veterinary Medical Association · doi:10.2460/javma.23.02.0090 · Cited by 9 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Pham, T. D., Karunaratne, N., Exintaris, B., Liu, D., Lay, T., Yuriev, E., & Lim, A. (2025) · Medical Education · doi:10.1111/medu.15746 · Cited by 100 · see citing works
A Systematic Review of User Attitudes Toward GenAI: Influencing Factors and Industry Perspectives
Chen, J., Wei, X., Xie, Q., Hu, A., Qiao, Y., Wan, R., & Liu, Y. (2025) · Journal of Intelligence · doi:10.3390/jintelligence13070078 · Cited by 36 · see citing works
Tortella, F., Palese, A., Turolla, A., Castellini, G., Pillastrini, P., Landuzzi, M. G., Cook, C., Galeoto, G., Giovannico, G., Rodeghiero, L., et al. (2025) · BMC Medical Education · doi:10.1186/s12909-025-07176-w · Cited by 28 · see citing works
“Moreover, greater knowledge and positive attitudes toward AI Chat-bots are correlated with increased academic use [26], while perceived ease of use and usefulness are significant predictors of their future adoption [31, 33].”
InfluentialIntegrating artificial intelligence into veterinary education: student perspectives
de Brito, C., Redondo, J. I., Tadeo, I., Bataller, E., García-Roselló, M., Madoz, I. C., & Terrado, J. (2025) · Frontiers in Veterinary Science · doi:10.3389/fvets.2025.1641685 · Cited by 17 · see citing works
Basil, M., Ahmed, W., Hajeomar, R., Strawbridge, J., Lynch, M. R., & Mukhalalati, B. (2026) · BMC Medical Education · doi:10.1186/s12909-025-08527-3 · Cited by 13 · see citing works
Artemiou, E., Hooper, S. E., Dascanio, L., Schmidt, M., & Gilbert, G. (2025) · Frontiers in Veterinary Science · doi:10.3389/fvets.2024.1504598 · Cited by 11 · see citing works
Bertin, F., Lawrence, J., Niessen, S., Pinard, C. J., Reagan, K. L., & Rentko, V. T. (2026) · Journal of Veterinary Internal Medicine · doi:10.1093/jvimsj/aalaf045 · Cited by 9 · see citing works
Curriculum framework for artificial intelligence literacy in veterinary education
Huang, Y.-T., & Chu, C. P. (2026) · Frontiers in Veterinary Science · doi:10.3389/fvets.2026.1801756 · Cited by 5 · see citing works
Oropesa, A. L., Mendo-Lázaro, S., Gonzalez, A., del Barco, B. L., & Tapia, J. A. (2026) · Frontiers in Veterinary Science · doi:10.3389/fvets.2026.1816115 · Cited by 3 · see citing works
Luo, X., Wang, B., Li, Y., Liu, S., Li, H. L., Ren, Y., Yang, W., Lam, K., Ali, S. R., Sharp, G., et al. (2025) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.70034 · Cited by 2 · see citing works
Jiang, X. (2025) · Cureus · doi:10.7759/cureus.88480 · Cited by 2 · see citing works
Sousa, S. A., Bukhari, S. S. U. H., Tang, M., Man, Y. K., Hobi, S., & Steagall, P. V. M. (2026) · Journal of Veterinary Medical Education · doi:10.3138/jvme-2025-0198 · Cited by 2 · see citing works
Eryol, A. (2026) · Frontiers in Veterinary Science · doi:10.3389/fvets.2026.1861384 · Cited by 1 · see citing works
Widmer, J. F., Silva, L. C., Grahofer, A., Zanolari, P., Unger, L., Lutz, B., Schüpbach, G., & Nathues, H. (2026) · doi:10.2196/preprints.102422 · Cited by 1 · see citing works
Formación universitaria temprana en producción animal con ChatGPT: Efecto del perfil del estudiante
Machado, C. F., Iglesias, J. L., Tejedo, M. E., Williams, K., Nadin, L. B., & Rodriguez, M. (2025) · Archivos Latinoamericanos de Producción Animal · doi:10.53588/alpa.330206
Aplicaciones de la Inteligencia Artificial en la Educación Agrícola Superior.
Cervantes-Gómez, L. A., Santos-Hernández, A. F., & Sahagún-García, A. (2026) · Dilemas contemporáneos Educación Política y Valores · doi:10.46377/dilemas.v13i2.4843
Assessing the Role of Large Language Models in Veterinary Dentistry Client Communication
Baykal, B., & Okur, S. (2026) · Journal of Veterinary Dentistry · doi:10.1177/08987564261424454 · Cited by 1 · see citing works
Zhang, H., Li, A., Li, Y., Nabi, F., Dong, H., & Ma, Y. (2026) · Journal of Veterinary Science · doi:10.4142/jvs.25194
Alan, S., Büyük, G. D., & Alan, S. Ç. (2026) · Veterinary Medicine and Science · doi:10.1002/vms3.71056
Ramírez, A. S., Quintana-Suárez, M. A., Pérez-García, E., Carrascosa, C., Conde-Felipe, M. M., Velázquez, E. S., & Jaber, J. R. (2026) · Applied Sciences · doi:10.3390/app16189297
冼琼珍, Chen, S., Bai, Y., Cui, K., Liu, L., Wang, W., Zhou, S., & Zhan, X. (2026) · Animals · doi:10.3390/ani16213379
Elsohaby, I., Wong, W. H., & Barrs, V. R. (2026) · Frontiers in Veterinary Science · doi:10.3389/fvets.2026.1991159
- Authors
- Worthing, K. A., Roberts, M., & Šlapeta, J.
- Affiliations
- The University of Sydney (AU)
- Corresponding author
- Worthing, Kate A.
- Venue
- Veterinary Record Open
- Publisher
- Wiley
- Published
- 2024-06
- Volume
- 11
- Issue
- 1
- Pages
- e280-e280
- ISSN
- 2052-6113 (ISSN-L)
- 2399-2050
- PMC
- PMC11162838
- OpenAlex
- W4399474904
- Cited by
- 21
- References
- 25
- Citations by year
- 2026: 12 · 2025: 8 · 2024: 1
- FWCI
- 1.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Veterinary Record Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Speech and Hearing › Veterinary Practice and Education Studies
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- ChatGPT, technology acceptance, veterinary curriculum, AI accuracy
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