Evidence map›Paper›PMID 42384579›Full record

ArticlePharmacology research & perspectives2026

Mapping Current Use of Artificial Intelligence in Pharmacology Education via a Scoping Review.

Werner Cordier, Princess Ijeoma

Abstract readScoping Review
In one paragraph

Article in Pharmacology research & perspectives, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

2 authors.

Werner CordierDepartment of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa.ORCID https://orcid.org/0000-0002-5744-9285
Princess IjeomaDepartment of Pharmacology, School of Medicine, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa.ORCID https://orcid.org/0009-0005-1596-0105

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pharmacology education, often reputed as complex, overtly didactic and decontextualised, may benefit from artificial intelligence-supported strategies. However, current guidance is fragmented across disciplines and contexts, thus weakening evidence-based curricular implementation. This scoping review mapped existing research to identify applications, strengths, limitations, and areas for future development. A double-blinded screening process facilitated by Covidence yielded 17 eligible studies from four databases. Studies mostly comprised cross-sectional studies from high-income countries in the medical context, with generative artificial intelligence being predominant (ChatGPT-3.5 and ChatGPT-4.0). Research comprised assessment (n = 12), paedagogy (n = 4), curriculum design (n = 1), and programme evaluation (n = 1). Most studies assessed tools' ability to answer examinations, with mixed success depending on the version, question type, and inclusion of context in prompt engineering. Few studies incorporated students, limiting insights into learning impact. Zero-shot prompting was mostly used, limited further by unclear design frameworks, which may bias outcomes considering downstream inefficiencies. Current research prioritises the performance of artificial intelligence, rather than its integration or impact in learning, which reduces its applicability for curriculum design and competency development. Although promising, the impact is limited, requiring clearer instructional design and rationalisation within the education ecosystem. Although there is considerable potential for pharmacology education, research requires greater structure, longitudinal design, and incorporation of students to inform clear impact. Purposeful, context-aligned implementation and continuous evaluation are needed to ensure ethical, valid, and meaningful use in pharmacology education. To support future research, recommendations are provided for practical reporting, scientific design, and impact measurement.

Indexed as

Artificial IntelligencePharmacologyCurriculumEducational MeasurementGenerative Artificial IntelligenceHumansartificial intelligenceassessmentcurriculum designgenerative artificial intelligenceinstructional designpaedagogypharmacology education

Identifiers

PMID42384579
PMCPMC13322406

What OpenQuestion holds

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LicenceCC BY-NC
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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.