Generative artificial intelligence in pharmacy education

Abstract

Introduction: Generative artificial intelligence (Gen-AI) is rapidly influencing processes within healthcare and education. Potential opportunities of adopting Gen-AI within pharmacy education are being put forward to improve learning outcomes and enhance competency development. In parallel, dilemmas and challenges present which require addressing.

Aims: To analyse evidence in literature and assess practitioners' views on use of Gen-AI in pharmacy education

Background: Generative artificial intelligence (Gen-AI) is rapidly influencing processes within healthcare and education. Potential opportunities of adopting Gen-AI within pharmacy education are being put forward to improve learning outcomes and enhance competency development. In parallel, dilemmas and challenges present which require addressing. The aim was to analyse evidence in literature and assess practitioners' views on the use of Gen-AI in pharmacy education. Methods: Using the PRISMA framework, a systematic literature review was conducted from open access journal resources published between 2021 and 2025 on Google Scholar, ResearchGate and PubMed. A thematic analysis was undertaken. A focus group discussion was organised with pharmacists and academics (n=4) to identify their perspectives and compare with the literature findings. Results: Literature review resulted in 250 articles, which were screened and led to 73 articles that were included in the thematic assessment. The themes identified (occurrence in articles) were: academic support required (53%), ethical considerations (47%), assessment design and exam preparation (33%), educator preparedness (27%), equity of access and fairness (19%). The focus group discussion agreed with the themes identified and provided a stronger insight on 1) the ethical aspects, particularly related to confidentiality of data included in Gen-AI software and the concern that students and users require awareness of ethical use 2) need to support users (students and academics) to use Gen-AI as a tool to generate powerful outcomes whilst retaining the critical thinking competence development. Conclusions: The findings expose opportunities for the application of Gen-AI in pharmacy education whilst highlighting reflections about concerns and limitations. Developing policy frameworks and guidelines supports institutional readiness so as to support the development of pharmacy graduates who are ready to leverage opportunities of Gen-AI within ethical and purposeful contexts.