WHAT’S POSSIBLE
Create active learning from AI explanation
A ready‑to‑use active‑learning vignette grounded in the cited study
Read & practise ↗Source: npj Digital Medicine · F2GREYBRAIN FIELDNOTES
Your many roles.
One method underneath.
One stream shows you something you can make this week. The other looks at what is being claimed, and what the evidence actually supports. Both end at a skill you can practise.
WHAT’S POSSIBLE · A useful workflowWHAT’S ADVANCING · Evidence in context
THE EDITORIAL LIBRARY · RECENT FIELDNOTES
What changed. What you can do about it.
THE EVERGREEN PRACTICE LIBRARY
Choose a role. Practise a skill.
Eight exercises that are always here, whichever hat you are wearing today.
EVERGREEN PRACTICE · THE TEACHER
Turn an explanation into active learning
You know the subject. Find new ways to make it memorable: a case, a visual, a question that gets the whole room thinking.
The moment you recognise
A concept to teach. An audience you want to bring into the conversation.
Try it with this synthetic material
Teaching source: Random allocation reduces systematic baseline differences between groups. Blinding can reduce some measurement and performance biases. Neither guarantees that a result applies to every population.
Make something useful
Create a short teaching analogy, a synthetic viva question and a worked answer grounded only in the source. Include a misconception check and a question the learner must answer independently.
Your check: Separate supplied facts from suggestions. Open the source, identify what is missing and record what you changed.
TWO MINUTES · NOTHING TO SIGN UP FORYour first win
Your first win
starts here.
An AI draft can overclaim confidently, in fluent English, with no warning that it has done so. This is the check that keeps the doctor in the loop.
No sign-up. You keep the template.
THE JOURNAL CLUB LABGuided demo · fictional case
- 01Read the source
- 02Spot the leap
- 03Keep the method
First, what does the source actually say?
FICTIONAL STUDY BRIEF
48 residents attended a journal-club workshop. Their average appraisal quiz score rose from 6/10 before the workshop to 8/10 immediately afterwards.
No comparison group. No later follow-up. Patient outcomes were not measured.
Now imagine handing this to an AI and asking for a summary for tomorrow’s journal club. Your job is what comes next: checking what it hands back.
This example is prewritten. No live AI request or real study is involved.
Turn a useful habit
into something you can show.
Self-paced foundations. Private capstone work. An exam and assessed progression towards the IIHMR Bangalore certificate programme. Eligibility and programme terms apply.
Explore the learning and certificate pathway ↗