| NSF Org: |
DUE Division Of Undergraduate Education |
| Recipient: |
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| Initial Amendment Date: | May 26, 2022 |
| Latest Amendment Date: | June 14, 2024 |
| Award Number: | 2142783 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Jennifer Lewis
jenlewis@nsf.gov (703)292-7340 DUE Division Of Undergraduate Education EDU Directorate for STEM Education |
| Start Date: | June 1, 2022 |
| End Date: | May 31, 2026 (Estimated) |
| Total Intended Award Amount: | $263,253.00 |
| Total Awarded Amount to Date: | $279,253.00 |
| Funds Obligated to Date: |
FY 2024 = $16,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
210 N 4TH ST FL 4 SAN JOSE CA US 95112-5569 (408)924-1400 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
1 Washington Sq San Jose CA US 95192-1000 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
HSI-Hispanic Serving Instituti, IUSE |
| Primary Program Source: |
04002425DB NSF STEM Education |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.076 |
ABSTRACT
This project aims to serve the national interest by improving college-level education in artificial intelligence (AI). Advances in AI will likely improve transportation, education, healthcare, and other societal issues. It is important that college students gain skills in AI to prepare them to be future leaders and innovators. However, current AI education lacks both broad multidisciplinary participation and diversity. The project plans to develop and implement strategies for improving education in AI that are available to all students, not just those enrolled in computer science programs. To do so, the project team hopes to develop materials that identify social problems and teach students to apply AI concepts and methods to address these problems. As a result, this project may better prepare today?s college students to enter the STEM workforce of the future.
The project intends to develop AI learning modules to stimulate AI learning in students? communities. The students will be trained how to identify social problems. The instructors will teach students AI concepts and applications through hands-on AI labs. The students will learn how to propose AI-powered solutions to address social issues considering both benefits and risks. The interdisciplinary AI For Social Good (AI4SG) modules will be implemented in the programs of management information systems, geography, and computer science at three California State University campuses. The team plans to host an annual workshop to showcase student projects and disseminate best practices of AI4SG education. The project will use both quantitative and qualitative research methods and hopes to generate evidence on how AI4SG education, through culturally responsive computing, can impact motivation, learning outcomes, innovation, and equity gaps. This project is supported by the NSF IUSE: EHR Program, which supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. Additional support is provided by the NSF IUSE:HSI program, which seeks to enhance undergraduate STEM education, broaden participation in STEM, and build capacity at HSIs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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