Skip to the ledger

GrantClerk

NSF award 2632220 · University of South Carolina at Columbia

I-Corps: Translation Potential of a Generative Artificial Intelligence (GenAI) Result Comparison Tool for Evaluating Foundation Model Outputs

Final report due

29 December 2027

in 437 days · The award ends on 31 August 2027.

GrantClerk is a private service. We are not NSF or any government agency.

Dates this award owes

NSF emails you about this too.

  • Decide about an extension3 May 2027in 197 days

    award ending 2027-08-31 · Research.gov

  • Extension request17 Jul 2027in 272 days

    award ending 2027-08-31 · Research.gov

  • No-cost extension notice21 Aug 2027in 307 days

    award ending 2027-08-31 · Research.gov

  • Final report due29 Dec 2027in 437 days

    award ending 2027-08-31 · Research.gov

  • Project outcomes report due29 Dec 2027in 437 days

    award ending 2027-08-31 · Research.gov

  • Award is closed out29 Dec 2027in 437 days

    award ending 2027-08-31 · Research.gov

Annual report dates come from the budget period on your award notice. That date is not public, so it is not shown here.

How we got this date

  1. NSF lists this award's end date as 31 August 2027.
  2. NSF rules say the final report is due no later than 120 days after that.
  3. So the final report is due by 29 December 2027.

Read the rule on Research.gov

Get a note before each report date

Add this award to your desk. We email you before each date and keep the dates in one place.

Renews each year until you cancel. Cancel any time from your account.

Already a member? Sign in

Award card

Award number
2632220
Period of support
1 Sep 2026 – 31 Aug 2027
Amount
$50,000
Awardee
University of South Carolina at Columbia, Columbia, SC
Program
I-Corps
Directorate
TIP

Abstract

This I-Corps project is based on the development of an evaluation framework for comparing results from Generative Artificial Intelligence (GenAI) systems. GenAI models are rapidly being deployed for complex data analyses in domains like health, finance, law and chemical manufacturing. Data analysis in high-stakes domains necessitates robust and reproducible evaluation methods. However, users today often create…

Is this right?

We copy this from the public NSF award record. Your award notice and NSF always win. If a date looks wrong, check Research.gov.