Interdisciplinary K-12 Control Education in Biomedical and Public Health Applications. Issue 17 (2022)

• Record Type: Journal Article
• Authors: Duncan, Dominique; Garner, Rachael; Bennett, Alexis; Sinclair, Michael; Cruz, Glendy Ramirez-De La; Pasik-Duncan, Bozenna
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 17(2022)
• Page Start: 242
• Page End: 248
• Publication Date: 2022
• Subjects: Control education -- biomedical control systems -- stochastic control -- machine learning -- artificial intelligence -- databases -- medical applications; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.09.286
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: There is a lack of control education at the K-12 level, and introducing high school students to the wide range of control applications and methods needed for these problems will increase the number of students who later plan to pursue science, technology, engineering, and mathematics (STEM) education and control research or careers. Through our established partnerships with schools in California and Kansas, we have developed educational tools to positively influence the learning and career paths of young high school (HS) students from underserved districts and underrepresented minority (URM) groups. Through the development of new curricula, teaching modules, and shared resources that are made broadly available to other school districts, this work has the potential to impact a large number of K-12 teachers and students. We have been working to train HS teachers to create connections between the STEM subjects they teach students with advanced topics, such as artificial intelligence (AI), that are often taught only post-HS. It creates a context where students can see how their STEM skills can be applied and a pathway of continuing STEM education from secondary to post-secondary. As a result, there is the potential to improve student interest and self-efficacy in STEM and to encourage them to pursue STEM careers. Furthermore, AI is key to understanding and making use of data and solving many control problems. We aim to help students learn the fundamentals of how AI algorithms are applied in contemporary neuroscience.