NSF award 2507128 · University of Georgia Research Foundation Inc

Teacher-Involved Adaptive Learning with Explainable Generative AI

Final report due

29 December 2028

in 803 days · The award ends on 31 August 2028.

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 2028in 563 days
award ending 2028-08-31 · Research.gov
This one is ours, not NSF’s.
Extension request17 Jul 2028in 638 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “at least 45 days prior to the end date”
No-cost extension notice21 Aug 2028in 673 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “at least ten calendar days prior to the end date”
Final report due29 Dec 2028in 803 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “no later than 120-days following the end date of the award”
Project outcomes report due29 Dec 2028in 803 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “no later than 120 days following the end date”
Award is closed out29 Dec 2028in 803 days
award ending 2028-08-31 · Research.gov
PAPPG 24-1: “financially closed out 120 days after their expiration date”

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

NSF lists this award's end date as 31 August 2028.
NSF rules say the final report is due no later than 120 days after that.
So the final report is due by 29 December 2028.

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: 2507128
Period of support: 1 Sep 2025 – 31 Aug 2028
Amount: $841,392
Awardee: University of Georgia Research Foundation Inc, Athens, GA
Program: Cyberlearn & Future Learn Tech
Directorate: EDU

Abstract

Adaptive learning systems provide personalized education experience to students based on their individual strengths and needs. However, traditional adaptive learning systems suffer from several challenges: (1) it is time-consuming to create custom assessment materials for different students, and (2) there is often not enough data to effectively train recommendation systems that guide learning. This project will…

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.