  The growing field of machine learning (ML) and artificial intelligence (AI)
presents a unique and unexplored case within persistence research, meaning it
is unclear how past findings from engineering will apply to this developing
field. We conduct an exploratory study to gain an initial understanding of
persistence in this field and identify fruitful directions for future work. One
factor that has been shown to predict persistence in engineering is belonging;
we study belonging through the lens of confidence, and discuss how attention to
social belonging confidence may help to increase diversity in the profession.
In this research paper, we conduct a small set of interviews with students in
ML/AI courses. Thematic analysis of these interviews revealed initial
differences in how students see a career in ML/AI, which diverge based on
interest and programming confidence. We identified how exposure and initiation,
the interpretation of ML and AI field boundaries, and beliefs of the skills
required to succeed might influence students' intentions to persist. We discuss
differences in how students describe being motivated by social belonging and
the importance of close mentorship. We motivate further persistence research in
ML/AI with particular focus on social belonging and close mentorship, the role
of intersectional identity, and introductory ML/AI courses.
