2443387
Project Grant
Overview
Grant Description
Career: Self-directed human-LLM coordination for language learning and information seeking.
Effective seeking of information is a crucial aspect of daily life that relies heavily on language skills.
In societies with a diverse population, many people are not proficient in the majority language.
People with less majority-language proficiency face challenges communicating their needs in daily activities such as medical consultations, discussing workplace benefits with employers, exploring housing, and others.
A typical solution pairs these individuals with professional interpreters or bilingual volunteers.
Such support is often expensive and unsustainable.
To fill the gap, this project will develop innovative tools based on AI large language models (LLMs) to help people advance their language abilities for effective information seeking.
Users without technical expertise in computers or AI will be guided to design their own personalized tutoring systems.
The resulting digital tutor will assist its user in creating learning plans, practicing strategies, and tracking their progress in advancing their language proficiency.
The study will generate rich data, metrics, and benchmarks for language learning for real-life information-seeking practices.
The project will advance the science of human-computer interaction, language science and education, and natural language processing.
The process of designing and using their own digital tutor will increase users' understanding of AI (AI literacy), and both the power and limitations of LLMs.
The tutoring systems will assist users in navigating various information-seeking scenarios, ultimately improving the person's language proficiency and quality of life.
This project provides a novel approach to tackling the challenges faced by people with less majority-language proficiency.
They will go through a process wherein they A) design, utilize, and refine an AI-enabled tutoring system that guides their language use for situational purposes, and B) advance their language proficiency along a learning path tailored by themselves to their needs.
The experience will stimulate the individual's critical thinking regarding actions to take at the different stages of their language learning.
Using the system provides the individual with feedback to adjust their learning to improve outcomes.
There are four sets of research activities involved in the lifecycle of this project.
Activity 1 employs experience sampling to pinpoint individuals' current practices and challenges in language use for daily information seeking.
It helps pinpoint the user's essential needs in a fine-grained manner.
Activity 2 compiles a bank of system building blocks, setting the stage for each individual-as-designer's assembly and customization of their tutoring system.
Activity 3 engages each individual-as-learner-and-designer in crafting, using, and refining their tutoring system.
It will generate longitudinal data to track the progress of a person's language use in daily information seeking, as well as their coordination with the tutoring system.
Activity 4 initiates an open data program to uncover value trade-offs in interactions between participants and large language models.
It will enable the exploration of emerging ways to align model-generated content with individuals' core values.
Together, these efforts will generate essential knowledge, novel methods and datasets, functioning tool-kits, and educational materials for enhancing language minorities' information seeking through effective language use.
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.
Subawards are not planned for this award.
Effective seeking of information is a crucial aspect of daily life that relies heavily on language skills.
In societies with a diverse population, many people are not proficient in the majority language.
People with less majority-language proficiency face challenges communicating their needs in daily activities such as medical consultations, discussing workplace benefits with employers, exploring housing, and others.
A typical solution pairs these individuals with professional interpreters or bilingual volunteers.
Such support is often expensive and unsustainable.
To fill the gap, this project will develop innovative tools based on AI large language models (LLMs) to help people advance their language abilities for effective information seeking.
Users without technical expertise in computers or AI will be guided to design their own personalized tutoring systems.
The resulting digital tutor will assist its user in creating learning plans, practicing strategies, and tracking their progress in advancing their language proficiency.
The study will generate rich data, metrics, and benchmarks for language learning for real-life information-seeking practices.
The project will advance the science of human-computer interaction, language science and education, and natural language processing.
The process of designing and using their own digital tutor will increase users' understanding of AI (AI literacy), and both the power and limitations of LLMs.
The tutoring systems will assist users in navigating various information-seeking scenarios, ultimately improving the person's language proficiency and quality of life.
This project provides a novel approach to tackling the challenges faced by people with less majority-language proficiency.
They will go through a process wherein they A) design, utilize, and refine an AI-enabled tutoring system that guides their language use for situational purposes, and B) advance their language proficiency along a learning path tailored by themselves to their needs.
The experience will stimulate the individual's critical thinking regarding actions to take at the different stages of their language learning.
Using the system provides the individual with feedback to adjust their learning to improve outcomes.
There are four sets of research activities involved in the lifecycle of this project.
Activity 1 employs experience sampling to pinpoint individuals' current practices and challenges in language use for daily information seeking.
It helps pinpoint the user's essential needs in a fine-grained manner.
Activity 2 compiles a bank of system building blocks, setting the stage for each individual-as-designer's assembly and customization of their tutoring system.
Activity 3 engages each individual-as-learner-and-designer in crafting, using, and refining their tutoring system.
It will generate longitudinal data to track the progress of a person's language use in daily information seeking, as well as their coordination with the tutoring system.
Activity 4 initiates an open data program to uncover value trade-offs in interactions between participants and large language models.
It will enable the exploration of emerging ways to align model-generated content with individuals' core values.
Together, these efforts will generate essential knowledge, novel methods and datasets, functioning tool-kits, and educational materials for enhancing language minorities' information seeking through effective language use.
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.
Subawards are not planned for this award.
Funding Goals
THE GOAL OF THIS FUNDING OPPORTUNITY, "FACULTY EARLY CAREER DEVELOPMENT PROGRAM", IS IDENTIFIED IN THE LINK: HTTPS://WWW.NSF.GOV/PUBLICATIONS/PUB_SUMM.JSP?ODS_KEY=NSF22586
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
College Park,
Maryland
20742-5100
United States
Geographic Scope
Single Zip Code
Related Opportunity
College Park University Of Maryland was awarded
Project Grant 2443387
worth $340,217
from the Division of Information and Intelligent Systems in August 2025 with work to be completed primarily in College Park Maryland United States.
The grant
has a duration of 5 years and
was awarded through assistance program 47.070 Computer and Information Science and Engineering.
The Project Grant was awarded through grant opportunity Faculty Early Career Development Program.
Status
(Ongoing)
Last Modified 6/20/25
Period of Performance
8/1/25
Start Date
7/31/30
End Date
Funding Split
$340.2K
Federal Obligation
$0.0
Non-Federal Obligation
$340.2K
Total Obligated
Activity Timeline
Additional Detail
Award ID FAIN
2443387
SAI Number
None
Award ID URI
SAI EXEMPT
Awardee Classifications
Public/State Controlled Institution Of Higher Education
Awarding Office
490502 DIV OF INFOR INTELLIGENT SYSTEMS
Funding Office
490502 DIV OF INFOR INTELLIGENT SYSTEMS
Awardee UEI
NPU8ULVAAS23
Awardee CAGE
0UB92
Performance District
MD-04
Senators
Benjamin Cardin
Chris Van Hollen
Chris Van Hollen
Modified: 6/20/25