Advancing a Model of Students' Intentional Persistence in Machine Learning and Artificial Intelligence
Project Overview
The document explores the integration of generative AI in education, particularly its transformative potential in enhancing learning experiences and improving student engagement, especially in STEM fields where underrepresented groups often face challenges. It presents a model for understanding students' intentional persistence in Machine Learning (ML) and Artificial Intelligence (AI), identifying key predictors such as confidence in professional roles, a desire for social benefit, and strong interpersonal skills. Research findings underscore the importance of fostering diversity and inclusivity within these fields, as well as the ethical implications of ML and AI. The document recommends that educators cultivate students' interpersonal skills and emphasize ethical considerations to support persistence and belonging among all students. Overall, generative AI is framed as a valuable tool not only for improving educational outcomes but also for promoting a more equitable and supportive environment in the pursuit of knowledge in AI and ML.
Key Applications
Interventions to Enhance Student Persistence and Belonging in AI/CS Education
Context: Higher education settings, specifically targeting undergraduate and graduate students enrolled in ML/AI and computer science courses, including women and underrepresented minority students in STEM fields.
Implementation: Surveys and social-belonging interventions implemented in various computer science courses to foster inclusion, support, and understand factors influencing student persistence and retention.
Outcomes: Identified factors associated with students' persistence and improved sense of belonging, resulting in higher retention rates among participants.
Challenges: Limited sample sizes, self-selection bias, resistance to change in traditional teaching methods, and potential skepticism from faculty regarding the effectiveness of interventions.
Implementation Barriers
Diversity and Inclusion
The lack of diversity in the field of AI and ML leads to biased algorithm design and perpetuates societal inequalities. This is compounded by low confidence levels, particularly among women and minorities, negatively affecting persistence in ML/AI.
Proposed Solutions: Encourage diversity in the educational pipeline and promote initiatives targeting underrepresented groups. Additionally, encourage development of interpersonal skills and build professional role confidence through targeted training.
Student Engagement
Students often enroll in ML/AI courses for popularity rather than genuine interest, which may not lead to long-term persistence. There is a need to emphasize the meaningful societal impacts of ML/AI and its ethical implications.
Proposed Solutions: Educators should highlight the significance of ML/AI in real-world applications and its ethical considerations to foster genuine interest.
Cultural
Resistance to the integration of AI in traditional education settings, including skepticism from faculty and administrators about its efficacy, presents a barrier to implementation.
Proposed Solutions: Provide training and workshops for educators to demonstrate AI's benefits; involve faculty in the development process to gain their buy-in.
Technical
Lack of infrastructure and technical support for implementing AI tools in educational environments poses a significant challenge.
Proposed Solutions: Invest in infrastructure upgrades and provide technical support; collaborate with IT departments to ensure smooth implementation.
Project Team
Sharon Ferguson
Researcher
Katherine Mao
Researcher
James Magarian
Researcher
Alison Olechowski
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Sharon Ferguson, Katherine Mao, 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