Author: Amanda Diehl | MIT Schwarzman College of Computing<\/p>\n
This summer, the MIT Schwarzman College of Computing welcomed faculty from colleges and universities across Greater Boston, South Carolina, West Virginia, and Texas to campus for the inaugural AI Educators Pilot, a weeklong workshop aimed at expanding how artificial intelligence is taught across disciplines and learning environments.\u00a0<\/p>\n
Inspired by MIT class C01\/C51 (Modeling with Machine Learning)<\/a>, a course developed through the\u00a0Common Ground for computing and AI education<\/a> that focuses on helping students understand and apply foundational AI and machine learning concepts to problem-solving in their own disciplines, the workshop gave educators an opportunity to explore how its materials and teaching methods could be adapted for their classrooms.\u00a0<\/p>\n \u201cThe broader goal is to expand AI education to more students by investing in training for instructors,\u201d says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Panasonic Professor of Electrical Engineering and Computer Science (EECS).<\/p>\n \u201cWe want to empower students to become critical thinkers about AI, not just users of the technology,\u201d says Asu Ozdaglar, deputy dean of academics for the MIT Schwarzman College and department head of EECS.<\/p>\n A collaborative model for expanding AI education<\/strong><\/p>\n Bringing the program to life required broad collaboration across the college, including support from leadership, staff, and contributions from more than half a dozen instructors in fields ranging from finance and computer science to sustainability. Together, they helped shape a workshop that paired core technical concepts with examples and teaching materials adaptable to a range of classroom settings.<\/p>\n \u201cI have not seen an effort quite like it \u2014 this many dedicated instructors assembling materials of this richness, all to equip the educators who serve their students,\u201d says Saurabh Amin, the Edmund K. Turner Professor in Civil Engineering and faculty director of the AI Educators Pilot. Amin is also co-director of the Operations Research Center, which is jointly housed within the MIT Schwarzman College of Computing and MIT Sloan School of Management.<\/p>\n With support provided by Jake and Robin Reynolds, the pilot brought together 19 participants in July from Allen University, Babson College, Brandeis University, Marshall University, the University of Massachusetts at Lowell, the University of North Texas, and Wentworth Institute of Technology. Working alongside MIT faculty and instructors, participants explored the pedagogy behind Modeling with Machine Learning through a mix of demos, videos, and exercises, and collaborated in hands-on activities focused on translating the course\u2019s materials and methods to their own classrooms.<\/p>\n \u201cThis opportunity has been very timely because we are starting an AI and data science program in my department,\u201d says Wenjin Zhou, assistant professor of computer science at UMass Lowell. \u201cWe\u2019ve already been thinking about: How do we teach our next generation of computer scientists within the area of AI? How do we integrate AI in the teaching? I wanted to learn more about how other people are doing it, and especially answer the question: If AI can create tools for anyone now, what does a computer scientist do?\u201d<\/p>\n Moving beyond the black box<\/strong><\/p>\n When it comes to AI, Amin notes, there is no shortage of high-quality material. What is usually missing is context: Opportunities for instructors and students to connect AI concepts to specific disciplines, problems, and ways of thinking. Those connections are often built through dialogue and reasoning, rather than by presenting AI as a fixed set of ideas to be received. But instructor capacity remains one of the scarcest resources.<\/p>\n \u201cWhat is scarce are educators prepared to teach AI as more than a fixed body of concepts and tools, to ground it in their own field, help students use it with judgment, and demystify it, so students do not just apply models but learn to question, adapt, and build with them,\u201d explains Amin.<\/p>\n Shen Shen, an EECS lecturer and one of the workshop instructors, adds, \u201cHow do we make sure that machine learning is not just a black box, nor this magic piece of\u00a0new technology?\u00a0You can think of it as a tool,\u00a0or\u00a0a new framing to help you solve the problem in your specific domain.\u201d<\/p>\n From pilot workshop to educator network<\/strong><\/p>\n Participants ended the week by reflecting on which workshop materials and teaching approaches they planned to adapt for their disciplines and courses. Their feedback will help shape future iterations of the pilot and support the development of a broader network of educators committed to expanding AI education across diverse learning environments.<\/p>\n Weijie Pang, an assistant professor of computer science at the Wentworth Institute of Technology who attended the workshop, looks most forward to ongoing community building activities. \u201cThis is a really valuable opportunity to communicate with other faculty from different majors and areas. I can see what other universities are doing and what we can learn from each other,\u201d she says.<\/p>\n \u201cIt’s helpful to know that everybody within different disciplines at different universities is struggling with the same questions of how we can best serve our students as the technology is changing. Hopefully, we can set them up for success by being a little bit more forward and anticipatory of what the AI use is going to be,\u201d says Dylan Cashman, an assistant professor of computer science at Brandeis University.<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"