S. V. Samsonau, A. Kurbonova, L. Jiang, H. Lashen, J. Bai, T. Merchant, R. Wang, L. Mehnaz, Z. Wang, and I. Patil, arXiv:2210.08966 [cs.CY] 1 May 2023
Student groups of complementary skills developing artificial intelligence solutions for natural sciences -- an authentic research education approach suitable for wide adoption
We report a methodology in which students gain experience in authentic research by developing artificial intelligence (AI) solutions for researchers in natural sciences. 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. In order to accomplish this, we work with research laboratories that reveal/specify the needs they have, and then our student teams work on the discovery, design, and development of an AI solution for unique problems using a consulting-like arrangement. Our design addresses common barriers which appear in most existing authentic research education approaches and thus is suitable for wide adoption at various schools. To date, our group has been operating at New York University (NYU) for five consecutive semesters and has engaged more than seventy students, ranging from first-year college students to master's candidates, and worked on more than 15 projects with 14 collaborators.