Evidence map›Paper›PMID 40465181›Full record

ArticleInternational journal of clinical pharmacy2025

Students' acceptance and use of generative AI in pharmacy education: international cross-sectional survey based on the extended unified theory of acceptance and use of technology.

Mohamed Hassan Elnaem, Betul Okuyan, Naeem Mubarak, Abrar K Thabit, Merna Mahmoud AbouKhatwa, Diana Laila Ramatillah, AbdulMuminu Isah, Ali Azeez Al-Jumaili, Nor Ilyani Mohamed Nazar

Abstract read
In one paragraph

Article in International journal of clinical pharmacy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Mohamed Hassan ElnaemSchool of Pharmacy and Pharmaceutical Sciences, Ulster University, Coleraine, BT52 ISA, UK. m.elnaem@ulster.ac.uk.ORCID http://orcid.org/0000-0003-0873-6541
Betul OkuyanDepartment of Clinical Pharmacy, Faculty of Pharmacy, Marmara University, Istanbul, Turkey.
Naeem MubarakDepartment of Pharmacy Practice, Lahore Medical and Dental College, University of Health Sciences, Lahore, Pakistan.
Abrar K ThabitPharmacy Practice Department, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia.
Merna Mahmoud AbouKhatwaDepartment of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, Alexandria University, Alexandria, 5372066, Egypt.
Diana Laila RamatillahFaculty of Pharmacy, Universitas 17 Agustus 1945, Jakarta, Jakarta, Indonesia.
AbdulMuminu IsahDepartment of Clinical Pharmacy and Pharmacy Management, University of Nigeria, Nsukka, 410001, Enugu State, Nigeria.
Ali Azeez Al-JumailiCollege of Pharmacy, University of Baghdad College of Pharmacy, Baghdad, Iraq.
Nor Ilyani Mohamed NazarKulliyyah of Pharmacy, International Islamic University Malaysia (IIUM), Kuantan, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenerative artificial intelligence (GenAI) has significant potential implications for pharmacy education, but its ethical, practical, and pedagogical implications have not been fully explored.

aimThis international study evaluated pharmacy students' acceptance and use of GenAI tools using the Extended Unified Theory of Acceptance and Use of Technology (UTAUT).

methodA cross-sectional survey of pharmacy students from nine countries during the first half of 2024 assessed GenAI usage patterns, curricular integration, and acceptance via the Extended UTAUT framework. After appropriate translation and cultural adaptation, exploratory factor analysis (EFA) identified key adoption factors.

resultsA total of 2009 responses were received. ChatGPT and Quillbot were the tools most frequently utilised. EFA identified three key dimensions: Utility-Driven Adoption, Affordability and Habitual Integration, and Social Influence. Students rated performance and effort expectancy highly, highlighting their perceived usefulness and ease of use of GenAI tools. In contrast, habit and price value received lower ratings, indicating barriers to habitual use and affordability concerns. Gender disparities were noted, with males demonstrating significantly higher acceptance (p < 0.001). Additionally, country-specific differences were evident, as Malaysia reported a high performance expectancy, while Egypt exhibited low facilitating conditions. Over 20% indicated an over-reliance on GenAI for assignments, raising ethical concerns. Significant gaps were observed, such as limited ethical awareness-only 10% prioritised legal and ethical training-and uneven curricular integration, with 60% reporting no formal exposure to Generative AI.

conclusionFindings reveal critical gaps in ethical guidance, equitable access, and structured GenAI integration in pharmacy education. A proactive, context-specific strategy is essential to align technological innovation with pedagogical integrity.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelEducation, PharmacyStudents, PharmacyAdultCross-Sectional StudiesCurriculumFemaleHumansInternationalityMaleSurveys and QuestionnairesYoung AdultGenerative AIPharmacy educationPharmacy studentsTechnology acceptanceUTAUT framework

Identifiers

PMID40465181
PMCPMC12335392

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.