Creating an AI-Savvy Workforce for a Strong Future

"China and other countries are investing heavily in developing artificial intelligence [AI] expertise," says MITRE's Michael Balazs. "The United States has to do likewise to stay competitive in the international arena."

To keep the nation competitive in this increasingly influential arena, MITRE's working to create an AI-savvy workforce—starting at the college level—through an initiative known as Generation AI Nexus (Gen AI). Our Gen AI instructional design team works with professors to integrate lessons on AI into their courses.

The Gen AI team recently worked with professors at Marymount University in Arlington, Virginia, to introduce AI-inspired lessons in two very different disciplines—healthcare and fashion merchandising.

Balazs leads the Gen AI initiative. The idea, he says, is to expose every student—regardless of their field of study—to AI multiple times in their academic journey. "We're not only trying to create a talent pool around AI, we're also working to ensure that everyone in the workforce has an awareness of AI and how it might be applied in their field."

Computer systems now perform tasks that normally require human intelligence. They permeate our daily existence. Companies use AI to track and predict our preferences and then create targeted advertising. Apple's Siri uses machine learning (a subset of AI) to improve the software's ability to respond to our questions. And driver-assistance systems like automatic braking are showing up in newer-model cars.

"The premise is that if we really want to shift the entire economy—not just an industry—we need to educate everyone who's going to be part of that economy," Balazs says.

Given a McKinsey & Company prediction that there will be a shortfall of up to 250,000 data scientists in the United States in a decade, the need for these scientists is clear.

Getting a Look at AI's Analytical Powers

MITRE launched Gen AI in May 2018. Since then, we've partnered with colleges and universities nationwide to incorporate AI lessons into their courses. We then make those lessons available to other schools wishing to use them.

Our partnership with Marymount University demonstrates how we work with professors to create lessons that meet their objectives. 

Uma Kelekar, professor of healthcare management, wanted a lesson that would help nursing and business students learn how AI could be used to test hypotheses and evaluate relationships between different data factors. Kelekar chose to focus on patient visits to the Emergency Department (ED) and the factors that may lead to the prescription of opioids.

Since pain is one of the major conditions ED patients exhibit, researchers have begun exploring how the prescription of pain-relieving drugs to these patients may be contributing to the nation's opioid epidemic, an issue MITRE is also researching and working to address.

The lesson-planning process was a collaborative effort among Kelekar and Gen AI team members Joe Garner, an instructional designer, and Ali Zaidi, a data scientist.

"We work together to decide what the students need to learn, how it will be taught, and how learning will be measured," Garner explains. "It's known as 'backward design,' because you design instruction with the end in mind. Once we identify the desired outcome, we decide where the lesson best fits into the curriculum, help the instructor engage students, and determine if the learning occurred."

Among the hypotheses Kelekar wanted to test: Did prescription patterns vary with the level of pain the patient reported? Did they vary by gender? Were private-pay patients more likely to receive medication prescriptions than publicly funded ones?

Zaidi ran analyses to test those hypotheses and created graphic depictions of the results.

"The visualizations were very helpful," Kelekar says. "They made it easier for the students to see relationships among the variables—such as how opioid prescription correlated with reported pain levels, gender, ethnicity, or the number of chronic conditions the patient is dealing with."

Applying AI to Fashion Merchandising Strategies

Across campus, fashion merchandising professor Jennifer Yang and data science professor Nathan Green combined their expertise to incorporate lessons on AI into Yang's fashion research and forecasting course.

Yang needed lessons that would help her students understand that fashion merchandising is not merely a subjective exercise. "I wanted them to see that there's a data-driven way to conduct product research, and that AI can be very helpful in informing any decisions or recommendations they might make. They may never have to actually crunch the numbers, but they do have to be able to interpret them."

She and Green explored factors that may correlate to higher sales or ratings of women's shoes. Using his own data science expertise, Green captured and organized data on 500 shoes from a popular online merchandiser's site. Zaidi then prepared the data for analysis, programmed machine-learning models to perform that analysis, and created visualizations of the model's outputs for the students to use.

"We basically said, 'These are the outputs of the data analysis. What conclusions would you draw from this information?'" Green adds. "By engaging in that process, I think the students developed basic data analysis skills."

"I love this project because it demonstrates the value of AI for students who are in non-data science disciplines," Zaidi says. "If they can become familiar with how AI works now, they'll be much better equipped to do their jobs—and more competitive in their field."

"I think everyone wants to do cross-discipline work, but getting it started can be difficult," Green says.  "MITRE's curriculum development expertise helps bridge the gap between technical and non-technical fields."

Spreading the Learning Through Gen AI

As an organization that works in the public interest, one of our goals is to make the Gen AI lessons broadly available.

"We're compiling the lesson plans, the code, the data sets, the visualizations, the machine-learning models we developed—everything—so that professors from participating schools wishing to use or adapt the lessons will have what they need to teach them in their own classrooms," Zaidi explains.

"As Gen AI spreads and grows in the coming years, we're hoping these lessons will become a resource for students and teachers across the country. MITRE is committed to expanding awareness about the need to up the United States' game in the AI arena. That can only make our nation more competitive globally."

—by Marlis McCollum

Check out the Generation AI website to learn more and sign up to become a partner school.