At a glance
- Source
- Beane (2018). Shadow Learning: Building Robotic Surgical Skill When Approved Means Fail. Administrative Science Quarterly, 64(1), 87–123. doi:10.1177/0001839217751692
- How it was done
- Two studies: two years of observation at five hospitals, comparing robotic with traditional surgery, and interviews at 13 leading teaching hospitals. Case study. A close look at one or a few organizations, based on interviews and observation.
- Who took part
- Surgical trainees and surgeons at five hospitals, plus interviews at 13 teaching hospitals.
- Where
- Hospitals in the United States.
“The radically different practice of robotic surgery greatly limited trainees’ role in the work, making approved methods ineffective.”
What it found
- The usual way of learning, taking on more of the real work under supervision, worked well in traditional surgery.
- With the robot, trainees’ part in the work shrank sharply, and the approved training methods stopped working.
- A minority of trainees still became competent. They specialised early, rehearsed on their own, and operated with less supervision than the rules required. The author calls this “shadow learning”.
- The result was fewer capable experts than the profession needed.
When the result holds
The mechanism does not depend on surgery. It applies wherever a new technology shrinks the junior’s part in real work, so the junior loses the practice.
A later study measured a loss of skill among experienced doctors, not trainees, within three months of AI being introduced. See endoscopists’ skill after working with AI.
Limits
- The study is about surgical robots, not AI software. Applying it to office work is a reasoned comparison, not a tested result.
- It describes what trainees did to cope. It does not test what an employer should do instead.
Practices that use this study
This study supports:
- Check now and then what staff can do without the AI On a sample of cases, have people do the work without the AI and compare the result with their usual work. Start with the people who check AI output and the people still learning the job, and set the tools that learners use to give hints, not answers. Do later · Effort: medium (weeks, a few people)
- Keep the entry-level tasks that newcomers learn on Before you automate routine work, check whether it is how juniors learn the job. If it is, keep some of it for them, or replace the learning another way. Do later · Effort: medium (weeks, a few people)
This study limits or contradicts: