My teaching spans three overlapping domains:

• AI literacy for health professionals: practical tool fluency + critical appraisal + responsible use (privacy, integrity, transparency, “no harm” framing)
• Clinical & scientific reasoning: structured approaches to uncertainty (differentials/hypotheses, evidence hygiene, reflection and justification)
• Research skills and mentorship: experimental design, data interpretation, academic writing, and reproducible analysis workflows (wet lab + computational)

I teach across formal coursework, workshops, small-group learning, and longitudinal mentorship of trainees in research and clinical contexts, and have been nominated for and awarded multiple teaching awards.

• Medical AI education: evaluation of AI systems, limitations/failure modes, bias, dataset shift, and how to read AI papers critically
• AI-enabled research workflows: literature synthesis, analysis support, writing support, and responsible documentation of AI assistance
• Health professions education: authentic learning, reflective practice, and small-group inquiry/discovery learning
• Mentorship and supervision: coaching trainees through research projects (idea → design → execution → write-up) and clinical learning (reasoning, professionalism, communication)

• Course teaching: AI in Medicine / AI in Healthcare (AIMSS) and AI literacy
• Small-group facilitation: discovery learning with medical students
• Workshops: research skills and responsible AI use (e.g., institutional workshops)
• Mentorship: longitudinal formal and informal supervision of diverse trainees in research and clinical settings

• Graduate Teaching + Learning Program (GTLP), University of Alberta: Level 1 (Foundations) and Level 2 (Practicum)
• Ongoing iterative improvement using learner feedback, observed learning gaps, and refinement of scaffolding/assessment alignment

I teach at the intersection of health professions education, research training, and applied AI. My goal is to develop learners who are both capable and cautious: able to use modern tools to improve efficiency and creativity while consistently validating outputs, understanding limitations, and protecting patient and research integrity.

My teaching is grounded in authentic tasks that mirror real clinical and research work—literature synthesis, drafting and revision, data interpretation, and reflective decision-making under uncertainty. I prioritize workflow literacy (how to actually do the work well), over novelty. Learners practice prompt scoping, tool selection, verification against primary sources, and transparent documentation of AI assistance.

I also teach through mentorship. In research settings, I coach trainees through experimental design, analysis choices, and communication of results; in clinical learning environments, I emphasize structured reasoning, professionalism, and collaborative practice. Across contexts, I aim to build durable judgment: the ability to integrate evidence, tools, and team input to make safe, defensible decisions.