NSF Org:	CNS Division Of Computer and Network Systems
Recipient:	CALIFORNIA STATE UNIVERSITY, DOMINGUEZ HILLS FOUNDATION
Initial Amendment Date:	July 17, 2022
Latest Amendment Date:	July 17, 2022
Award Number:	2219589
Award Instrument:	Standard Grant
Program Manager:	Subrata Acharya acharyas@nsf.gov  (703)292-2451 CNS  Division Of Computer and Network Systems CSE  Directorate for Computer and Information Science and Engineering
Start Date:	September 1, 2022
End Date:	August 31, 2025 (Estimated)
Total Intended Award Amount:	$191,698.00
Total Awarded Amount to Date:	$191,698.00
Funds Obligated to Date:	FY 2022 = $191,698.00
History of Investigator:	Sanaz Rahimi Moosavi (Principal Investigator) srahimimoosavi@csudh.edu Ali Jalooli (Co-Principal Investigator)
Recipient Sponsored Research Office:	California State University-Dominguez Hills Foundation 1000 E VICTORIA ST CARSON CA  US  90747-0001 (310)243-2852
Sponsor Congressional District:	44
Primary Place of Performance:	California State University-Dominguez Hills Foundation 1000 East Victoria Street Carson CA  US  90747-0001
Primary Place of Performance Congressional District:	44
Unique Entity Identifier (UEI):	MWEPWP3T6XL5
Parent UEI:
NSF Program(s):	CISE MSI Research Expansion
Primary Program Source:	010V2122DB R&RA ARP Act DEFC V
Program Reference Code(s):	102Z, 9102
Program Element Code(s):	173Y00
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

This project will implement a computer tutor for low literacy Hispanic breast cancer survivors. Breast cancer is the leading cause of cancer-related deaths in Hispanics, and although research has shown that education can greatly mitigate stress and improve quality of life, few educational interventions for this population exist. The computer tutor resulting from this project will mimic a human tutor that teaches about breast cancer survivorship skills and about breast cancer in general. Because tutoring involves conversation with the survivor, it is possible that they reveal sensitive personal information; therefore, it is important to encrypt the information in the tutoring session to prevent any privacy breaches. And for the tutoring component to be effective, special attention must be paid to the utilization of natural language processing models as well as models of behavior that are observed in the target population when tutoring and/or interacting with technology. For the privacy component to be effective, techniques that can encrypt and decrypt data at high speeds will be explored to make the interaction fluid. To date, computer tutors that converse with their students have been tried mainly with a highly literate population in college settings, so their impact on low literacy Hispanics is unknown. Therefore, this project will advance our understanding of the impact of designing artificial intelligence powered tutors to address diversity and disparities in the access to information by a subset of low literacy individuals, as well as our understanding of privacy preserving algorithms that work in real-time with complex natural language processing models. More broadly, project outcomes will facilitate access to information for minority populations and will serve to build research capacity and train minority students in the participating teaching-oriented institutions.

The project will be carried out with two objectives in mind. First, development of a novel intelligent computer tutoring system that is customized so that it can effectively query and interact with Hispanic breast cancer survivors by adapting existing content that was created for this population in prior research. Because it has been shown that both the language of many adult Hispanics, and target population interactions with technology, are more nuanced than previously thought, our first objective also involves training natural language algorithms and designing interactions that model those of Hispanic breast cancer survivors. The second objective is to develop privacy-preserving algorithms that utilize robust end-to-end encrypted communication and can encrypt and decrypt distributed data in real time at a speed that does not hinder the interactions with the computer tutor. The contributions of this development process will be threefold: (1) to understand the role of culture and education in the interaction between low literacy Hispanic breast cancer survivors and intelligent tutoring systems; (2) to develop a framework that facilitates the implementation of intelligent tutoring systems for minority populations; and (3) to develop accurate and low latency privacy preserving mechanisms for NLP model training and dialogue interfaces.

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.

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