Recruitment Strategies for Master's Degree in AI among High Achieving Low-Income Engineering Students

The unprecedented growth in the use of AI and its related technologies will put a tremendous stress on US institutions to produce the required number of technologically prepared workers to fill critically important job openings. In the US, low-income and URM students participate less vigorously in STEM-related fields; the problem is even more serious in post-baccalaureate level degrees. To address the future needs of the nation, we must increase the number of low-income students in STEM, with special attention to AI related technologies, to fill the millions of technology job openings. This paper will report on the impact of a NSF SSTEM project in which we combined (a) a mentorship model for talented, low-income students to develop a sense of self-efficacy and belongingness along with (b) a model of curricular and co-curricular supports (e.g., including engagement with AI technologies and research) and (c) limited financial assistance, all of which have increased the low-income student success in completing both their BS degree in engineering and their MS degree in AI, and addressing a national need. [For the full proceedings, see ED656038.]