AI in Medical Education
April 1, 2025
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1 min read
What problem are we solving?
Health professional learners are encountering AI in clinical and academic settings, but training is uneven and often not integrated into interprofessional practice contexts.
What we are building
A structured set of learning objectives, teaching sessions, and assessment-aligned materials integrated into an interprofessional course, paired with a scholarship-of-teaching evaluation strategy.
Methods
- Pre- and Post-session, assignment, and end-of-course survey instruments (paired)
- Descriptive and inferential analysis to characterize perceived competence, attitudes, and readiness
- Focus on practical competencies (interpretation, limitations, safety, and team-based application)
My role
Co-design of curriculum elements, survey/evaluation design, analysis plan, and manuscript development.
Current status
Data collection complete/ongoing (depending on cohort); analysis and manuscript drafting in progress.
Outputs
- Teaching materials
- Conference abstracts & talks (done)
- Scholarship-of-teaching manuscript (in progress)
Authors
Ehsan Misaghi
(he/him)
Clinician-Scientist Trainee
Ehsan Misaghi is an MD/PhD Candidate at the University of Alberta working at the intersection of ophthalmology, genetics, and artificial intelligence.
His research focuses on inherited retinal disease and genotype–phenotype correlations in ocular disease, with an emphasis on mechanistic insight and translational relevance.
Alongside research, he builds and evaluates practical AI tools for clinical and educational settings, and he leads medical AI education, research, and community-building through the AI in Medical Systems Society (AIMSS) and related initiatives.
His goal is to advance rigorous, clinically useful research and translate it into improved diagnostics, care pathways, and responsible innovation.