"Just a little bit on the outside for the whole time": Social belonging confidence and the persistence of Machine Learning and Artificial Intelligence students
Project Overview
The document examines the role of generative AI in education, particularly focusing on its applications in Machine Learning (ML) and Artificial Intelligence (AI) programs. It highlights the significance of fostering social belonging and confidence among students to improve persistence in these fields. Key factors influencing student retention include exposure to ML/AI concepts, mentorship opportunities, and the balance between technical and non-technical skill development. The research underscores the challenges faced by underrepresented groups, notably women, and stresses the necessity for greater diversity within the AI domain to reduce biases in AI-generated outputs. The findings advocate for enhanced educational strategies aimed at promoting inclusivity and retention in ML/AI programs, ultimately aiming to create a more equitable learning environment that supports all students in their educational journeys and prepares them for future careers in AI.
Key Applications
Research study on social belonging confidence in ML/AI students
Context: University-level ML/AI courses, targeting undergraduate and graduate students
Implementation: Qualitative study with semi-structured interviews and thematic analysis of student experiences
Outcomes: Identified importance of social belonging and mentorship on persistence; highlighted barriers for women and underrepresented minorities
Challenges: Low diversity in the field; reliance on self-motivated learning; intimidation from male-dominated environments
Implementation Barriers
Social/Cultural Barrier
Low levels of social belonging confidence among women and minority students in ML/AI, combined with a male-dominated culture in ML/AI courses that can discourage participation from these groups, leading to feelings of isolation and intimidation.
Proposed Solutions: Promoting mentorship opportunities, creating inclusive environments that foster a sense of belonging, and encouraging diverse role models and mentorship programs to help students relate to their mentors.
Skill Barrier
Technical confidence gap, particularly in programming skills, which affects persistence and engagement in ML/AI.
Proposed Solutions: Providing multiple introductory programming courses tailored to diverse skill levels, and integrating application-oriented assignments.
Project Team
Katherine Mao
Researcher
Sharon Ferguson
Researcher
James Magarian
Researcher
Alison Olechowski
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Katherine Mao, Sharon Ferguson, James Magarian, Alison Olechowski
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai