Artificial intelligence in pharmacology education across undergraduate health professions programmes: a scoping review

Wan Salleh, Wan Muhamad Salahudin and Abd. Rahim, Nour El Huda (2026) Artificial intelligence in pharmacology education across undergraduate health professions programmes: a scoping review. International Journal of Care Scholars, 9 (3). pp. 145-163. ISSN 2600-898X

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Abstract

Background: This scoping review aimed to map how AI tools have been used in undergraduate pharmacology education across medicine, pharmacy, nursing, dentistry, and allied health; how their performance on pharmacology assessments; their instructional applications; the perceptions and engagement of students and educators; and to identify the gaps and future directions reported in the literature. Methods: We searched four databases from January 2016 to March 2026. Eligibility followed the Population–Concept–Context framework of undergraduate health professions students or educators, AI tools applied in pharmacology education and any global setting. Two authors independently screened records and extracted data. Results: From an initial pool of 455 records identified according to the eligibility criteria, 52 studies across 28 countries were selected for the final data extraction. Most were published from 2023 onwards. Large language models (LLMs), particularly the GPT family, dominated the evidence base, achieving pharmacology accuracy rates of approximately 87–93% on standardised licensing examinations, though performance varied on assessments requiring clinical reasoning and pharmacological integration. Instructional applications were promising but remained at the proof-of-concept stage. Student attitudes were broadly positive, while formal institutional integration remains poorly established. Recurrent gaps included methodological limitations, limited faculty readiness, and the absence of AI standards in pharmacology accreditation frameworks. Conclusion: These findings highlight that AI is entering undergraduate pharmacology education faster than institutions are adapting to it. The educators and students showed markedly different patterns of readiness and engagement. Realising its potential will require a structured, faculty-governed approach to AI literacy taught alongside it.

Item Type:	Article (Review)
Uncontrolled Keywords:	Artificial intelligence (AI), Pharmacology, Education, Medical, Pharmacy, Nursing
Subjects:	L Education > L Education (General) R Medicine > R Medicine (General) T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Medicine Kulliyyah of Medicine > Department of Basic Medical
Depositing User:	dr wan muhamad salahudin wan salleh
Date Deposited:	24 Sep 2026 09:44
Last Update:	24 Sep 2026 09:44
Queue Number:	2026-09-Q5233
URI:	http://irep.iium.edu.my/id/eprint/131459
Indexed In:	Google Scholar, MyCite

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