Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Aug 14, 2025
Date Accepted: Feb 27, 2026
Date Submitted to PubMed: Feb 27, 2026
Artificial Intelligence in Health Professions Education: A Qualitative Study of Student Experiences
ABSTRACT
Background:
Artificial intelligence (AI) is gaining importance in various sectors, including education and healthcare. Its growing use in vocational training raises questions about its use by students, particularly in the healthcare field. It is crucial to study the impact of this technology on their learning methods.
Objective:
This qualitative study aimed to identify the most widely used AI tools among health profession students at the University of Ottawa, describe their usage habits, identify the tools that help them acquire new knowledge and develop skills, and explore what these students consider to be the best strategies for raising awareness and training their peers on the proper use of AI in learning environments.
Methods:
A qualitative study was conducted at the University of Ottawa with students from ten health professions who had used AI in their learning. Data were collected via semi-structured interviews and an online survey. An inductive thematic analysis within an interpretive paradigm was used to identify key themes emerging from the data.
Results:
Participants emphasized the growing role of AI in their educational experiences. Main themes were adoption patterns, usage habits, and critical assessment of AI tools. AI was mostly seen as complementary, but valued for improving efficiency, knowledge acquisition, and problem-solving. The most widely used tool was ChatGPT, which was adopted out of curiosity, peer influence, and to improve work efficiency.
Conclusions:
This research highlights students' use of artificial intelligence tools. Although AI is perceived as a valuable complement to medical training, its limitations require further research to support its effective implementation and evaluate long-term educational outcomes.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.