Preparing High School Students for Careers in Machine Learning through Mentored Scientific Research
Artificial Intelligence (AI) is quickly becoming ubiquitous in STEM and across industries, and the education field is grappling with how best to teach AI concepts to K12 audiences. Simultaneously, the AI professional community suffers from a lack of diversity that excludes women and people of color from a dynamic section of the economy and a path for upward mobility. Equally important, a lack of diverse perspectives can risk automating discriminatory practices based on biased algorithms and biased data sets. For over a decade, the Science Research Mentoring Program, a STEM workforce development initiative of the American Museum of Natural History, has provided New York City high school students from underserved populations with the experience of working closely with scientists, increasing access to science fields and careers through research opportunities and mentorship. In this project, the Museum, in partnership with the Massachusetts Institute of Technology, will undertake a three-year research project to innovate within this well-established program by creating opportunities for high school students to learn and apply machine learning (ML), a subset of AI, to scientific problems in the natural sciences. The project responds to the imperative to prepare a diverse student body for a workplace that will require a sophisticated understanding of AI and ML by advancing students' skills and knowledge of ML, promoting awareness of AI and computationally-demanding STEM careers, and fostering positive dispositions towards these fields. This project is funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.Through this project, 120 students will participate in a four-week Summer Institute specifically focused on developing skills and understanding of ML applications and careers, and 30 of these students will subsequently work in research labs over the academic year with scientists who use ML. A design-based research approach will be used to develop and refine the Summer Institute informed by a team consisting of science educators, scientists, AI experts, and program alumni. Student participants will be recruited specifically from partner organizations that primarily serve Black, Hispanic/Latinx, and first-generation college-bound students. A mixed-method research study will gather data on students' acquisition of ML knowledge and skills, attitudes toward and perceptions of AI, and AI career awareness and interest using pre-post survey instruments, artifact review, semi-structured observations, and interviews. Research findings will be disseminated through conferences, articles, and social media. The curriculum and research tools will be publicly available and readily scalable, leveraging the New York City Science Research Mentoring Consortium, a network of 24 research and cultural institutions serving 500 students annually. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.