Promoting Student Persistence in AI

Studying the factors that influence student pathways in AI careers

While engineering education has spent decades studying student career pathways in order to improve retention, when new subdisciplines emerge, they tend to be initially dominated by few demographic groups. To improve persistence of AI students, this project, in collaboration with Dr. Alison Olechowski (University of Toronto) and Dr. James Magarian (MIT), this project investigates how students decide whether or not to pursue a career in AI after taking an introductory class.

Publications

  1. Advancing a Model of Students’ Intentional Persistence in Machine Learning and Artificial Intelligence
    Sharon Ferguson, Katherine Mao, James Magarian, and 1 more author
    American Society of Engineering Education Annual Conference, 2022
  2. "Just a little bit on the outside for the whole time": Social belonging confidence and the persistence of Machine Learning and Artificial Intelligence students
    Katherine Mao, Sharon Ferguson, James Magarian, and 1 more author
    American Society of Engineering Education Annual Conference, 2023
  3. Social Capital and Persistence in Computer Science of Google’s Computer Science Summer Institute (CSSI) Students
    Marjan Naghshbandi, Sharon Ferguson, and Alison Olechowski
    American Society of Engineering Education Annual Conference, 2024