Artificial intelligence in academic writing and clinical pharmacy education: consequences and opportunities
Open accessAbstract
The current academic debate on the use of artificial intelligence (AI) in research and teaching has been ongoing since the launch of ChatGPT in November 2022. It mainly focuses on ethical considerations, academic integrity, authorship and the need for new legal frameworks. Time efficiencies may allow for more critical thinking, while ease of pattern recognition across large amounts of data may promote drug discovery, better clinical decision making and guideline development with resultant consequences for patient safety. AI is also prompting a re-evaluation of the nature of learning and the purpose of education worldwide. It challenges traditional pedagogies, forcing a shift from rote learning to more critical, analytical, and creative thinking skills. Despite this opportunity to re-think education concepts for pharmacy curricula several universities around the world have banned its use. This commentary summarizes the existing debate and identifies the consequences and opportunities for clinical pharmacy research and education.
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Cites
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · see citing works
Frenk, J., Chen, L., Bhutta, Z. A., Cohen, J., Crisp, N., Evans, T., Fineberg, H., Garcia, P., Ke, Y., Kelley, P., et al. (2010) · The Lancet · doi:10.1016/s0140-6736(10)61854-5 · Cited by 5,960 · see citing works
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584 · see citing works
Artificial intelligence: A powerful paradigm for scientific research
Xu, Y., Liu, X., Cao, X., Huang, C., Liu, E., Qian, S., Liu, X., Wu, Y., Dong, F., Qiu, C.-W., et al. (2021) · The Innovation · doi:10.1016/j.xinn.2021.100179 · Cited by 1,726 · see citing works
New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023) · Sustainability · doi:10.3390/su151612451 · Cited by 1,089 · see citing works
Ouyang, F., Zheng, L., & Jiao, P. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-10925-9 · Cited by 831 · see citing works
International Pharmaceutical Federation (FIP)
(2014) · International Year Book and Statesmen's Who's Who · doi:10.1163/1570-6664_iyb_sim_org_38971 · Cited by 364 · see citing works
World Intellectual Property Organization (WIPO)
(1998) · Yearbook of the United Nations/United Nations yearbook collection · doi:10.18356/2751f549-en · Cited by 332 · see citing works
Objective structured clinical examination: the assessment of choice
Zayyan, M. (2011) · Oman Medical journal · doi:10.5001/omj.2011.55 · Cited by 295 · see citing works
Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., & Siemens, G. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100138 · Cited by 152 · see citing works
The impact of Artificial Intelligence in academia: Views of Turkish academics on ChatGPT
Livberber, T., & Ayvaz, S. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e19688 · Cited by 103 · see citing works
Friend or foe? Exploring the implications of large language models on the science system
Fecher, B., Hebing, M., Laufer, M., Pohle, J., & Sofsky, F. (2025) · AI & SOCIETY · doi:10.1007/s00146-023-01791-1 · Cited by 78 · see citing works
Digital pharmacists: the new wave in pharmacy practice and education
Silva, R. D. O. S., de Araújo, D. C. S. A., Menezes, P. W. D. S., Neves, E. R. Z., & de Lyra, D. P. (2022) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-021-01365-5 · Cited by 73 · see citing works
The perils of artificial intelligence in academic publishing
Gendron, Y., Andrew, J., & Cooper, C. (2022) · Critical Perspectives on Accounting · doi:10.1016/j.cpa.2021.102411 · Cited by 68 · see citing works
A review of integrating AI-based chatbots into flipped learning: new possibilities and challenges
Lo, C. K., & Hew, K. F. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1175715 · Cited by 67 · see citing works
Assertion‐reason multiple‐choice testing as a tool for deep learning: a qualitative analysis
Williams, J. B. (2005) · Assessment & Evaluation in Higher Education · doi:10.1080/02602930500352857 · Cited by 49 · see citing works
A Review of Entrustable Professional Activities in Pharmacy Education
Abeyaratne, C., & Galbraith, K. J. (2021) · American Journal of Pharmaceutical Education · doi:10.5688/ajpe8872 · Cited by 37 · see citing works
Bramley, A. L., Thomas, C. J., McKenna, L., & Itsiopoulos, C. (2020) · Nursing and Health Sciences · doi:10.1111/nhs.12774 · Cited by 26 · see citing works
A A Global Comparison of Initial Pharmacy Education Curricula: An Exploratory Study
Arakawa, N., Bruno, A., & Bates, I. P. (2020) · INNOVATIONS in pharmacy · doi:10.24926/iip.v11i1.2093 · Cited by 24 · see citing works
Croft, H., Gilligan, C., Rasiah, R., et al. (2019) · Currents in Pharmacy Teaching and Learning · doi:10.1016/j.cptl.2019.11.015 · Cited by 21 · see citing works
Norms for Academic Writing in the Era of Advanced Artificial Intelligence
Friederich, S., & Symons, J. (2023) · Digital Society · doi:10.1007/s44206-023-00079-7 · Cited by 20 · see citing works
Blending team-based learning and game-based learning in pharmacy education
Khalafalla, F. G., & Alqaysi, R. (2021) · Currents in Pharmacy Teaching and Learning · doi:10.1016/j.cptl.2021.06.013 · Cited by 16 · see citing works
Transformative potential of Artificial Intelligence in pharmacy practice
Raza, M. A., & Aziz, S. (2023) · Saudi Pharmaceutical Journal · doi:10.1016/j.jsps.2023.101706 · Cited by 12 · see citing works
Addressing Challenges in Skills-based Education Through Innovation and Collaboration
Dula, C. A. C., & Porter, A. L. (2021) · American Journal of Pharmaceutical Education · doi:10.5688/ajpe8788 · Cited by 8 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [6]PTV News. Cambridge, other top universities ban use of ChatGPT. 4 March, 2023. https://ptvnews.ph/cambridge-other-top-universities-ban-use-of-chatgpt/ Accessed 19 Jan 2024.
Cited by
Sorted by citations
Balalle, H., & Pannilage, S. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.101299 · Cited by 137 · see citing works
Campbell, N., & Kalabalik-Hoganson, J. (2025) · International Journal of Clinical Pharmacy · doi:10.1007/s11096-025-01947-7 · Cited by 5 · see citing works
Abdellatif, M. S., Alshehri, M. A., Lamouchi, A., Rahmath, M., & Nemt-allah, M. A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1809174
“…communication (Barrett and Pack, 2023; Ou et al., 2024; Pratiwi et al., 2025), while AI-enhanced reference management and literature mapping tools like Scite AI, Connected Papers, and Litmaps complement traditional platforms such as Zotero and Mendeley (Pratiwi et al., 2025; Weidmann, 2024).”
Ayanda, A. O. (2026) · International Journal of Applied Resilience and Sustainability · doi:10.70593/deepsci.0203003
“Most classical deep learning models are typically black box and users might have difficulty ascertaining the reason why some outputs are obtained [1,11-14].”
- Authors
- Weidmann, A. E.
- Affiliations
- Universität Innsbruck (AT)
- Corresponding author
- Weidmann, Anita Elaine
- Venue
- International Journal of Clinical Pharmacy
- Published
- 2024-03-12
- Volume
- 46
- Issue
- 3
- Pages
- 751-754
- ISSN
- 2210-7703 (ISSN-L)
- 2210-7711
- PMC
- PMC11133206
- OpenAlex
- W4392692922
- Cited by
- 42
- References
- 24
- Citations by year
- 2026: 15 · 2025: 15 · 2024: 11 · 2022: 1
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- International Journal of Clinical Pharmacy (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Library of the University of Innsbruck (University of Innsbruck) (submittedVersion, cc-by)
- Funders
- University of Innsbruck and Medical University of Innsbruck
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Keywords
- artificial intelligence, ChatGPT, academic writing, clinical pharmacy education, academic integrity, authorship, AI ethics, artificial intelligence in drug discovery, clinical decision-making, patient safety, critical thinking, pharmacy curriculum
- MeSH terms
- Pharmacy Research, Artificial Intelligence, Curriculum, Education, Pharmacy, Humans, Writing
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