Artificial Intelligence for Scientific Research: Authentic Research Education Framework

TL;DR

This paper presents a framework that integrates authentic research experiences into education by involving students in developing AI solutions for scientific research, benefiting both students and researchers.

Contribution

It introduces a scalable educational framework that connects student teams with research labs to collaboratively develop AI solutions for scientific problems.

Findings

Engaged over 100 students across seven semesters.

Collaborated on more than 20 research projects.

Provided practical AI solutions to scientific research needs.

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.