Artificial Intelligence for Scientific Research: Authentic Research Education Framework
Sergey V Samsonau, Aziza Kurbonova, Lu Jiang, Hazem Lashen, Jiamu Bai, Theresa Merchant, Ruoxi Wang, Laiba Mehnaz, Zecheng Wang, Ishita Patil
TL;DR
The paper addresses the challenge of implementing authentic research education (AREd) in real-world science settings by proposing a principled framework and a concrete NYU program (AIfSR) that pairs student teams with science labs to co-create AI solutions. The approach uses a hybrid AREd model with cross-disciplinary teams, a startup-like governance, and collaboration with researchers to formulate and deliver usable AI-enhanced workflows. It details organizational structure, roles, project lifecycles, tools, and knowledge transfer processes to overcome barriers such as limited instructor time and data access. The contributions include a scalable design for AREd programs, practical implementation details, and a demonstration of benefits to both students and collaborating scientists. The work supports broader adoption of AREd in universities and highlights how AI can meaningfully advance scientific research.
Abstract
We report a framework that enables the wide adoption of authentic research educational methodology at various schools by addressing common barriers. The guiding principles we present were applied to implement a program in which teams of students with complementary skills develop useful artificial intelligence (AI) solutions for researchers in natural sciences. To accomplish this, we work with research laboratories that reveal/specify their needs, and then our student teams work on the discovery, design, and development of an AI solution for unique problems using a consulting-like arrangement. To date, our group has been operating at New York University (NYU) for seven consecutive semesters, has engaged more than a hundred students, ranging from first-year college students to master's candidates, and has worked with more than twenty projects and collaborators. While creating education benefits for students, our approach also directly benefits scientists, who get an opportunity to evaluate the usefulness of machine learning for their specific needs.