A new academic program developed at the Massachusetts Institute of Technology aims to teach US Air and Space Forces personnel to understand and use artificial intelligence technologies. In a recent study that program researchers recently shared in the IEEE Limitations in Education At the conference, the program’s researchers found that this approach was effective and well-received by employees with diverse professional backgrounds and roles.
The project seeks to contribute to artificial intelligence Educational researchparticularly on ways to broadly maximize learning outcomes for people from a variety of educational backgrounds.
Experts at MIT Open Learning have built a curriculum for three general types of military personnel—leaders, developers, and users—using MIT’s current Educational materials and resources. They also created new, more experimental training courses aimed at leaders of the Air and Space Forces.
Then, MIT scientists led a research study to analyze the content, evaluate the experiences and outcomes of individual learners during the 18-month trial, and suggest innovations and ideas that would ultimately enable the program.
They used interviews and several questionnaires, given to both program learners and staff, to assess how 230 Air and Space Forces personnel interacted with the course materials. They also collaborated with MIT faculty to conduct a content gap analysis and determine how to further improve curricula to address desired skills, knowledge, and mindsets.
Ultimately, the researchers found that military personnel responded positively to hands-on learning. value asynchronous and time-saving learning experiences to fit their busy schedules; He highly commended the team-based experience, and learning by making content, but sought content that included more professional and soft skills. Learners also wanted to know how AI can be directly applied to their daily work and the broader mission of the Air and Space Forces. They were also interested in more opportunities to interact with others, including peers, coaches, and AI experts.
Based on these findings, the team is working to increase educational content and add new technical features to the portal for the next iteration of the study, which is currently underway and will extend through 2023.
“We’re delving deeper into broadening what we believe the learning opportunities are driven by our research questions and also by understanding the science of learning around this kind of project size and complexity. But ultimately we’re also trying to deliver some real transformative value to the Air Force and Department of Defense. This work is making an impact in The real world to them, and that’s really exciting,” says principal investigator Cynthia Brizel, MIT Dean for Digital Learning, Director of MIT RAISE (Artificial Intelligence Responsible for Social Empowerment and Education), and Head of the Personal Robots Research Group at the Media Lab.
Building learning journeys
At the beginning of the project, the Air Force gave the program team a set of profiles that captured the educational backgrounds and job functions of six primary categories of Air Force personnel. The team then created three original models that it used to build “learning journeys” – a series of training programs designed to impart a set of AI skills to each profile.
The Lead-Drive archetype is the individual who makes strategic decisions; Create-Embed archetype is a technical agent who implements AI solutions; The Facilitate-Employ prototype is an end user of AI-enhanced tools.
Convincing the lead-drive model of the importance of this program was a priority, says lead author Andrés Felipe Salazar-Gomez, a research scientist at MIT Open Learning.
“Even within the Department of Defense, leaders have been questioning whether training in AI is worth it,” he explains. “First we needed to change the mindset of the leaders so they would allow learners, developers and other users to go through this training. At the end of the pilot, we found that they had adopted this training. They had a different mindset.”
The three learning journeys, spanning six to 12 months, included a combination of current AI courses and materials from MIT Horizon, the MIT Lincoln Lab, the MIT Sloan School of Management, the Computer Science and Artificial Intelligence Laboratory (CSAIL), and the Media Lab. and MITx MicroMasters. Most modules have been delivered entirely online, either synchronously or asynchronously.
Each learning journey included different content and formats based on users’ needs. For example, the Create-Embed journey included a five-day in-person training course taught by a research scientist at Lincoln Laboratory that provided a deep dive into the subject matter of AI technology, while the Facilitate-Employment journey included self-paced, asynchronous, material-based learning experiences. MIT Horizon designed for a more general audience.
The researchers also created two new courses for the Lead-Drive group. One of them, a concurrent online course called The Future of Leadership: Human-AI Collaboration in the Workforce, developed in collaboration with Esme Learning, was built on leaders’ desire for more training on ethics, human-centered AI design, and more human-centered content. . – Cooperation with the workforce. The researchers also crafted a three-day, experiential, in-person training course called Learning Machines: Arithmetic, Ethics, and Politics that immersed leaders in a construction-style learning experience where teams worked together on a series of hands-on activities with Autonomous robots which culminated in an escape room-style culmination competition that brought it all together.
Brizel says the machine learning course has been hugely successful.
“At MIT, we learn through teamwork and through teamwork. We thought, What if we let executives learn about AI this way?” she explained. “We’ve found that the engagement is much deeper, and they gain a stronger intuition about what makes these technologies work and what it takes to implement them responsibly and aggressively. And I think this will provide profound information about how we think about the implementation education of these types of disruptive technologies in the future.”
Collect feedback and improve content
During the study, the MIT researchers checked on the learners using questionnaires to get their feedback on the content, teaching methods, and techniques used. They also have MIT faculty analyze each learning journey to identify educational gaps.
In general, the researchers found that learners wanted more opportunities to engage, either with peers through team-based activities or with faculty and experts through concurrent components of online courses. And while most employees found the content interesting, they wanted to see more examples that applied directly to their day-to-day work.
Now in the second iteration of the study, the researchers are using this feedback to improve learning journeys. They are designing knowledge checks that will be part of the asynchronous, self-driving courses to help learners engage with the content. They are also adding new tools to support live Q&A events with AI experts and help build more community among learners.
The team is also looking to add specific examples from the Department of Defense across the tutorial modules, and include a scenario-based workshop.
“How do you raise the skills of a 680,000-strong workforce across diverse work roles, all levels, and at scale? This is an MIT-sized problem, and we’re benefiting from the global work MIT has been doing on open education since 2013 — democratizing “By leveraging our research partnership with MIT, we are able to research optimal teaching methods for our workforce with focused pilots,” says Major John Radovan, Deputy Director of the DAF-MIT AI Accelerator. Then we can multiply the positive unexpected results and focus on the lessons learned. This is how you accelerate positive change for the sake of our pilots and parents.”
As the study progresses, the program team is increasing their focus on how this training program can reach a broader reach.
says Kathleen Kennedy, senior director at MIT Horizon and executive director of the MIT Institute’s Center for Collective Intelligence. “But the challenge now is to scale this up so that individual learners get what they need and stay engaged. This will certainly help to see how the different MIT platforms can be used with other types of large groups.”
Andres F. Salazar-Gomez et al, Design and Implementation of an Artificial Intelligence Learning Program for Learners with Widely Diverse Backgrounds, 2022 IEEE Frontiers in Education (FIE) Conference (2022). doi: 10.1109/FIE56618.2022.9962632
Massachusetts Institute of Technology
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