Training Next Generation AI Users and Developers at NCSA
Project Overview
The document examines the FoDOMMaT summer research program at the National Center for Supercomputing Applications, University of Illinois Urbana-Champaign, which trains undergraduate students in artificial intelligence (AI) and machine learning (ML) through hands-on research experiences. It emphasizes the importance of collaboration between students and mentors, the development of open-source ML tools, and the assessment of learning outcomes. The program has proven successful in enhancing students' technical skills and knowledge while promoting diversity within the field. Additionally, it fosters a supportive research community that encourages innovation and collaboration. Key applications of generative AI highlighted in the program include developing tools that can assist in various educational contexts, showcasing how AI can personalize learning experiences and improve engagement. Overall, the findings indicate that such programs not only equip students with essential skills but also contribute to a more inclusive environment in AI education, ultimately preparing them for future challenges in technology and research.
Key Applications
FoDOMMaT (Future of Discovery: Training Students to Build and Apply Open Source Machine Learning Models and Tools)
Context: A summer REU program for undergraduate students interested in AI, ML, and data science.
Implementation: Students work on research projects under the guidance of mentors with expertise in AI/ML and their respective research disciplines.
Outcomes: Students develop open-source AI/ML models, gain hands-on research experience, and improve their readiness for graduate school or careers in industry.
Challenges: Need for prior software development and machine learning exposure; ensuring diverse recruitment; balancing mentorship roles.
Implementation Barriers
Resource Barrier
Limited funding and resources for strategic growth in AI education programs.
Proposed Solutions: Develop partnerships with industry and research organizations to secure additional funding and resources.
Diversity Barrier
Challenges in attracting underrepresented groups to the program.
Proposed Solutions: Implement outreach initiatives to promote the program among diverse student populations.
Project Team
Daniel S. Katz
Researcher
Volodymyr Kindratenko
Researcher
Olena Kindratenko
Researcher
Priyam Mazumdar
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Daniel S. Katz, Volodymyr Kindratenko, Olena Kindratenko, Priyam Mazumdar
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