ArticleResearch square2026
AI-Based Assessment Tools in Pharmacy Education.
Article in Research square, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Purpose: Integrating artificial intelligence (AI) into pharmacy education represents a shift in assessment practice, offering opportunities to enhance efficiency, objectivity, and personalization in evaluating student competencies. As Doctor of Pharmacy (PharmD) programs in North America increasingly adopt AI-powered tools, understanding their capabilities, effectiveness, and limitations has become critical for educators and administrators. AI-based assessment tools in pharmacy education were examined by synthesizing their technical features, pedagogical effectiveness, implementation challenges, ethical considerations, and future directions. Method: A focused literature search was conducted in Google Scholar and PubMed using keywords including AI-based assessment, artificial intelligence, pharmacy education, automated grading, ChatGPT, GPT-4, machine learning, adaptive learning, computer-based testing, and virtual OSCE. From 600 initial results, 222 unique records remained after deduplication and were ranked by relevance; the 30 most relevant articles published between 2004 and 2026 were selected for detailed synthesis using a structured data-extraction approach. Results: Four categories of AI assessment tools emerged: generative AI tools for grading and feedback; automated assessment systems for objective scoring; adaptive learning platforms for personalized progression; and virtual assessment environments, including virtual objective structured clinical examinations (OSCEs). GPT-4 showed substantial agreement with human raters, and automated short-answer grading correlated highly with human examiners. Recurring challenges included algorithmic transparency, data privacy, academic-integrity risks, validation requirements, and faculty-training needs. Conclusion: AI-based assessment tools show promise in pharmacy education, offering greater efficiency and objectivity while maintaining acceptable accuracy across assessment types. Successful implementation requires attention to validation, ethics, faculty development, and integration with existing curricula and accreditation frameworks. Future work should prioritize longitudinal effectiveness, bias mitigation, and pharmacy-specific AI assessment frameworks.
Indexed as
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What OpenQuestion holds
Registered trials
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.