Artificial intelligence in pharmacy education: A scoping review of current integration & global perceptions
Bibliographic record
Abstract
BACKGROUND: The rapid evolution of artificial intelligence (AI) is reshaping healthcare, including pharmacy, requiring AI-proficient pharmacy graduates. This necessitates an understanding of how AI is utilized in pharmacy education. This scoping review aims to summarize current literature on AI in pharmacy education, including its implementation and perceptions among students and faculty, and examine the alignment of these applications with accreditation standards to inform future curriculum development. METHODS: A literature search was performed across PubMed, Scopus, Embase, CINAHL, and Google Scholar, for studies on AI in pharmacy education. Articles were categorized as innovation or perception studies. Innovation studies underwent thematic analysis to identify practical applications, while perception studies captured AI familiarity and willingness for curricular integration. AI applications were mapped to the Accreditation Council for Pharmacy Education (ACPE) and Canadian Council for Accreditation of Pharmacy Programs (CCAPP) standards. RESULTS: Twenty articles (10 innovation, 10 perception) were included. Faculty utilized AI for evaluation, assessment, and reflective writing analysis. Students used AI for personalized learning, enhancing communication, and problem-based learning. Some studies reported high AI familiarity; others showed limited knowledge. Nevertheless, a strong willingness to integrate AI into pharmacy education was observed, with students desiring more AI-focused curricula. Mapping AI applications to accreditation standards demonstrated that AI integration can support educational outcomes and competency requirements. CONCLUSIONS: The findings highlight potential AI applications in pharmacy education, underscoring the need to incorporate AI into pharmacy curricula. Alignment with accreditation standards suggests that AI integration addresses evolving professional needs and maintains quality standards for pharmacy programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".