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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2210.08966 |
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| _version_ | 1866914860126699520 |
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| author | Samsonau, Sergey V Kurbonova, Aziza Jiang, Lu Lashen, Hazem Bai, Jiamu Merchant, Theresa Wang, Ruoxi Mehnaz, Laiba Wang, Zecheng Patil, Ishita |
| author_facet | Samsonau, Sergey V Kurbonova, Aziza Jiang, Lu Lashen, Hazem Bai, Jiamu Merchant, Theresa Wang, Ruoxi Mehnaz, Laiba Wang, Zecheng Patil, Ishita |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_08966 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence for Scientific Research: Authentic Research Education Framework Samsonau, Sergey V Kurbonova, Aziza Jiang, Lu Lashen, Hazem Bai, Jiamu Merchant, Theresa Wang, Ruoxi Mehnaz, Laiba Wang, Zecheng Patil, Ishita Computers and Society 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. |
| title | Artificial Intelligence for Scientific Research: Authentic Research Education Framework |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2210.08966 |