I teach at the intersection of health professions education, biomedical science, and responsible AI, helping learners build durable judgment for clinical and research work.

What I teach in each of these themes:

• AI literacy for health professionals: tool fluency + critical appraisal + responsible use (privacy, integrity, transparency)
• Clinical & scientific reasoning: structured thinking under uncertainty (hypotheses, evidence-based experimentation, justification)
• Research skills & mentorship: experimental design, data interpretation, academic writing, and reproducible workflows (wet lab + computational)

How I teach:

• Psychological safety + structured facilitation (learners reason out loud, revise hypotheses, and learn through dialogue)
• Authentic tasks (literature synthesis, interpretation, drafting/revising, evidence-tracing)
• Verification-first AI use (outputs treated as hypotheses; claims traced to primary sources; transparent documentation)

Teaching Philosophy:

My goal is to develop learners who are 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. In practice, this means learners don’t just “get answers,” they practice how to check them, how to justify decisions, and what would change their mind.

Mentorship:

I also teach through longitudinal mentorship. In research settings, I coach trainees from idea → design → execution → analysis → communication; in clinical learning environments, I emphasize structured reasoning, professionalism, and collaborative practice.

Teaching Development:

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