UCSB Professor Explores AI’s Impact on Training

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Photo Courtesy of Matt Beane

Xander Apicella

Matt Beane, a professor in UCSB’s Technology Management Program, spoke about what artificial intelligence is costing the workforce in a TED talk last November.

Beane’s career began with consulting, but after he saw a sociometric badge designed by the MIT Media Lab, he became inspired to apply to graduate school to learn how to use technology to help teams perform better.

While attending graduate school at MIT’s Sloan School of Management, Beane’s interests crystallized.

“I did a number of research projects, all of them on the implications for robotics on collaborative work,”  he said. These project led him to his dissertation, which involved cataloging the effects of AI on education of young doctors in residency. This was also the subject of his recent TED talk.

In his TED talk, he explained the process behind his research.

“I started with a big, open question: How do we learn to work with intelligent machines?” said Matt Beane in an interview with The Bottom Line.

To explore that question, Beane described the case of a surgeon in training, Kristen. First, he outlines a scenario where the surgeon in training, performs surgery on a prostate patient with the help of a seasoned doctor. She watches the experienced surgeon, helping as he goes, but she finishes up the procedure, gaining valuable hands-on time with a procedure she had studied. In surgery, they call this type of progression “see one, do one, teach one.”

Then, he compared this experience to a similar one. In this scenario, the experienced surgeon, Kristen’s mentor, worked with a robotic AI surgery apparatus via a console. Kristen only had a few minutes of control throughout the procedure. Her hands-on experience was sacrificed in favor of a quicker, safer procedure.

From a logistical standpoint, this may seem like a positive because the surgery was completed more efficiently. However, since Kristen only received a few minutes of hands-on learning in the second case, it is unlikely that she can replicate this procedure. As a result, she won’t be as sure of herself when she has to do such a procedure solo.

Beane believes that the hands-on experience Kristen is losing is the most vital kind of learning.

In his TED talk, he points out that worker surveys show that most workers learned key skills on the job, not in formal training.

His dissertation explores what is lost when mentorship is sacrificed for efficiency. Although he studied surgery most, he also wanted to know in what other instances AI was hindering learning.

After connecting with researchers studying similar topics, a pattern emerged.

“No matter the industry, the work, the AI, the story was the same. Organizations were trying harder and harder to get results from AI, and they were peeling learners away from expert work as they did it,” said Beane.

“Startup managers were outsourcing their customer contact, future cops had to learn to deal with crime forecasts without expert support, junior bankers were getting cut out of complex analysis, and new professors had to build online courses without help,” he said.

These sacrifices are made in the name of efficiency, but the comprehensive approach to learning a new skill is sacrificed.

“McKinsey estimates that between half a billion and a billion of us are going to have to adapt to AI in our daily work by 2030,” Beane said. “AI is a powerful and lucrative tool, but it blocks traditional learning methods, creating a vacuum where new techniques must be found to survive.”

Despite the struggles faced by the majority, a small minority of students in Beane’s study thrived despite the lack of support.

“Residents got involved in robotic surgery in medical school at the expense of their generalist education,” he said. “They spent hundreds of extra hours with simulators and recordings of surgery when you were supposed to learn in the OR. And, maybe most importantly, they found ways to struggle through live procedures with limited expert supervision.”

Beane has termed these strategies “shadow learning.” Shadow learning involves pursuing an objective with unsanctioned methods in order to gain hands-on experience required for that position. Beane believes this behavior should be a catalyst for brainstorming future solutions because shadow learning is an unsustainable strategy.

Formal education has begun to address this issue, but more ideas are needed.

“There are a number of groups interested in not just studying, but building systems to help groups of people learn things virtually,” Beane said. “Easy examples are MOOCs, Udacity, or edX, or Coursera, or Khan Academy. So why don’t we take those tools that we’re building — that are even using AI — and drag them over to how a master interacts with an apprentice on the job.”

This type of transition — from an environment where technology drives people apart to one where it brings them together — is vital for developing environments conducive for professional learning.

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