Award Abstract # 2206884
RET Site: Research Experience for Teachers in Interdisciplinary AI

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: UNIVERSITY OF CALIFORNIA, SAN DIEGO
Initial Amendment Date: July 27, 2022
Latest Amendment Date: July 27, 2022
Award Number: 2206884
Award Instrument: Standard Grant
Program Manager: Allyson Kennedy
aykenned@nsf.gov
 (703)292-8905
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: February 1, 2023
End Date: January 31, 2027 (Estimated)
Total Intended Award Amount: $517,554.00
Total Awarded Amount to Date: $517,554.00
Funds Obligated to Date: FY 2022 = $517,554.00
History of Investigator:
  • Garrison Cottrell (Principal Investigator)
    gary@cs.ucsd.edu
  • Virginia de Sa (Co-Principal Investigator)
  • Amy Eguchi (Co-Principal Investigator)
  • Taylor Berg-Kirkpatrick (Co-Principal Investigator)
Recipient Sponsored Research Office: University of California-San Diego
9500 GILMAN DR
LA JOLLA
CA  US  92093-0021
(858)534-4896
Sponsor Congressional District: 50
Primary Place of Performance: University of California-San Diego
Computer Science and Engineering
La Jolla
CA  US  92093-0404
Primary Place of Performance
Congressional District:
50
Unique Entity Identifier (UEI): UYTTZT6G9DT1
Parent UEI:
NSF Program(s): RSCH EXPER FOR UNDERGRAD SITES,
CSforAll-Computer Sci for All,
RES EXP FOR TEACHERS(RET)-SITE,
IIS Special Projects
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7218
Program Element Code(s): 113900, 134Y00, 135900, 748400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Artificial Intelligence (AI) is having an increasing impact on everyday life, from smart speakers and digital assistants to medical discoveries and judicial sentencing. Therefore, the ethical and social implications of AI technologies make it imperative that all citizens understand both the positive and negative impacts on the future of society. This new RET site at the University of California San Diego (UCSD) will provide research opportunities for high school teachers to deepen their understanding of the field of AI while developing materials to use in their classrooms. Teachers will be primarily recruited from high schools in districts serving students who are underrepresented in STEM and from low socio-economic backgrounds. The six-week summer program will include a two-week boot camp to prepare teachers to participate in an intensive AI research project across a range of applications. During the academic year, teachers will continue to engage with research faculty through monthly dinner seminars where they will exchange ideas and discuss the latest updates in AI research.

The intellectual focus of this RET Site from UCSD is Interdisciplinary Artificial Intelligence, with a focus on the applications of Deep Learning. The high school computer science and math teachers, mostly from the Computer Science Teachers Association San Diego Chapter (CSTA SD), which is headquartered at UCSD, will participate in a two-week summer ?boot camp?, followed by four weeks of intensive research with AI faculty and graduate students from Computer Science and Engineering, Cognitive Science, and Psychology. Additional objectives are to improve the ability of UCSD faculty to communicate ideas to the public through collaborating with and learning from teacher participants. The team also strives to use ideas from participants that can be incorporated into teaching UCSD AI systems. Finally, the main goal of the site is that teachers understand the ethical implications and current challenges of AI to promote awareness, discussion, and excitement among their students.

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.

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page