Perceptions of generative AI in medical education: a qualitative descriptive SWOT analysis with a descriptive quantitative component among undergraduate medical students

Abstract

Generative artificial intelligence (GAI) is rapidly transforming education, reshaping how students access knowledge and engage with learning. In medical education specifically, GAI tools present both significant opportunities and notable challenges, raising questions about their impact on clinical reasoning, information reliability, and professional development. While interest in GAI integration in health professions education is growing, evidence on how medical students themselves perceive and critically appraise these tools remains limited. This study aimed to examine third-year undergraduate medical students’ perceptions of GAI tools in learning and education using the SWOT framework within a structured medical informatics workshop setting. A qualitative descriptive study with a descriptive quantitative component design was employed, combining descriptive quantitative analysis of closed-ended survey items with thematic content analysis of open-ended responses structured around the SWOT framework. Data were collected during a required medical informatics workshop using an online survey instrument. Consecutive sampling was used to recruit all eligible female third-year undergraduate medical students at a single institution in Saudi Arabia. A total of 117 students participated. Among them, 68% reported prior use of GAI tools, with “Poe” being used by 52% of students. Thematic content analysis revealed 17 themes across the SWOT domains, encompassing students’ perceptions of GAI’s educational benefits and limitations, national and institutional opportunities, and concerns related to safety, ethics, and workforce implications. The findings suggest that curriculum design should extend beyond the technical integration of GAI tools to embed structured opportunities for developing digital literacy, critical appraisal skills, and reflective engagement with the ethical and professional dimensions of AI in healthcare. These findings further highlight the need to revisit existing medical education competency frameworks to incorporate AI literacy and ethical GAI use as core graduate competencies. Student perceptions offer a valuable foundation for educators and institutions developing governance guidelines and curricula for the responsible integration of GAI in medical education.