MSU secures NSF grant to foster AI careers for diverse high school students
Mississippi State University has recently been awarded a grant from the National Science Foundation that will help forge pathways to Artificial Intelligence careers for diverse high school students.
This developing and testing innovations project is funded by the Innovative
Technology
Experiences for Students and Teachers (ITEST) program, which supports projects that build an understanding of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics and information and communication technology careers.
Leading the program and research are Dr. Yan
Sun, Associate Professor of Instructional Technology; Dr. Lisa Thomas, Assistant Professor and the Director of Mentoring; Dr. Jingdao Chen, Assistant Professor specializing in robotics, computer vision, and artificial intelligence; Dr. Zhiqian Chen, Assistant Professor specializing in machine learning with an emphasis on dynamics behaviors over graphs and networks; and Dr. Mariah Morgan, Associate Extension Professor specializing in 4-H STEM, early childhood technology, and 4-H Robotics.
Due to the nature of the grant, the program will serve mostly minority students and traditionally underserved populations, Sun noted. Additionally, Sun remarked that the recruiting process will include students of the neurodiverse population, females, and other minority students.
“For this particular grant, we wanted to focus on high school computer science teachers and also high school students,” said Sun. “For this particular project, they wanted us to particularly target the
diverse student population, especially those traditionally underserved populations.”
The program will provide thirty high school students from schools in an approximate one-hundred mile radius the chance to experience a week-long camp where they will design, build, and learn about artificial intelligence (AI) and Machine Learning. The students will work with select computer science teachers from surrounding high schools and will be housed on-campus at MSU for the duration of the camp.
“Each year we will have five high school computer science teachers come in and they will be responsible for six students,” explained Sun. “They will come in for a weeklong summer camp with us, and then after the summer camp, they will go back to their school. Then, we will have computer science undergraduate students trained to service the mentors, and then they'll go follow them to their school district for the whole year, meaning, after the summer, they will go back for the fall semester and then the spring semester.”
The camp will focus on teaching students to use necessary programs for a career in AI and Machine Learning, as well as the knowledge necessary to build an intelligent system.
“In the summer camp, we will give them enough [of a] knowledge base, and we will use a Google Teachable Machine and we will also use computer vision (Cv)...they will come in, we will show them how to clock image data, and how to use Teachable Machine to train the model, and after they train the Machine Learning model, they with use the Opencv to build smart or intelligent systems,” said Sun.
Sun noted that because this project is AI focused, a big part of what they hope to teach in relation to Machine Learning and AI is that the models can be fooled. Human bias plays a role in teaching the machines to acknowledge certain things the way they do. Human bias in Machine Learning occurs when algorithms produce results that are systemically prejudiced due to erroneous assumptions in the Machine Learning process. This bias can lead to incorrect predictions or decisions that discriminate against certain groups. The project will be specific to the students' interests.
“One example is, they can decide to build a sorting machine. Let's say, they decide to build a machine intelligent enough to sort nuts and bolts mixed together into two different bowls,” Sun explained. “They can collect different nut and bolt images and go to Teachable Machine to train the model. After they train the model, the model can be used in Computer Vision… and then, they can build a machine that can tell which is a nut and a bolt and put them in different bowls.”
In addition to being specific to students' interests, the project will also provide them with knowledge and experience that is central to the work.
“Because it is open ended,” Sun remarked, “we'll give them enough foundation knowledge on how to work with Teachable Machine and Opencv, and then they can, based on their life experience, come up with their own idea of what kind of machine they want to build.”
The project is designed to give students a step into the world of AI and Machine Learning and provide them with real-world experience that can help them in their future endeavors, noted Thomas.
“We want to make it real-world compatible,” said Thomas, “so that they can then take that knowledge and if they want to pursue business or commerce, for example, they can think about what they can build that would be useful out in the marketplace.”