NSF Org:	DRL Division of Research on Learning in Formal and Informal Settings (DRL)
Recipient:	THE LELAND STANFORD JUNIOR UNIVERSITY
Initial Amendment Date:	September 21, 2023
Latest Amendment Date:	September 21, 2023
Award Number:	2337772
Award Instrument:	Standard Grant
Program Manager:	Amy Baylor abaylor@nsf.gov  (703)292-5126 DRL  Division of Research on Learning in Formal and Informal Settings (DRL) EDU  Directorate for STEM Education
Start Date:	October 1, 2023
End Date:	September 30, 2025 (Estimated)
Total Intended Award Amount:	$200,000.00
Total Awarded Amount to Date:	$200,000.00
Funds Obligated to Date:	FY 2023 = $200,000.00
History of Investigator:	Dorottya Demszky (Principal Investigator) ddemszky@stanford.edu Janet Carlson (Co-Principal Investigator)
Recipient Sponsored Research Office:	Stanford University 450 JANE STANFORD WAY STANFORD CA  US  94305-2004 (650)723-2300
Sponsor Congressional District:	16
Primary Place of Performance:	Stanford University 450 JANE STANFORD WAY STANFORD CA  US  94305-2004
Primary Place of Performance Congressional District:	16
Unique Entity Identifier (UEI):	HJD6G4D6TJY5
Parent UEI:
NSF Program(s):	ITEST-Inov Tech Exp Stu & Teac
Primary Program Source:	1300PYXXDB H-1B FUND, EDU, NSF
Program Reference Code(s):	092Z, 7914
Program Element Code(s):	722700
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.076

ABSTRACT

While Artificial intelligence (AI) has the potential to improve both science, technology, engineering and mathematics (STEM) teaching practice and students' overall classroom experiences, it is critical to better understand how teachers can more easily adapt it within their classrooms. In particular, supporting AI-driven tool adoption in resource-poor schools is crucial to address educational inequities. This RAPID project addresses an urgent need to facilitate integration of AI technologies into schools to maximize benefits while reducing the burden on teachers? time. Specifically, the goal of this project is to understand how instructional coaches can implement AI teacher feedback tools, leveraging the advantages of such tools (cost effectiveness, scalability, customizability, data-based and privacy) and mitigating technical and time barriers to adoption. The findings and products of this project will support professional learning organizations as well as district-based coaches and teachers interested in automated feedback, and has the potential to significantly increase the quality of instruction at various types of institutions.

The time-sensitive research will involve interviewing highly-skilled coaches to develop scaffolding resources by leveraging existing collaborations with two teacher professional learning programs. Working with coaches and teachers who serve grade 4-8 math classrooms with a large percentage of marginalized students, the project will design generalizable coaching cycles and conversational routines that take advantage of information from automated feedback, while designing for different coaching models, different coaching contexts, and teachers with varying aptitudes for technology. The study incorporates an interview phase, a design phase for coaching cycles and routines, and a pilot phase, with room for iteration and emphasis on dissemination of the findings. Overall, the study will provide insights into how AI-driven feedback can be integrated into teacher coaching, contributing to knowledge about the challenges and opportunities of implementing AI within existing instructional processes. Ultimately, this project will help uncover how AI can be harnessed to enhance teacher effectiveness and student learning in real-world educational settings in a scalable way. This proposal was received in response to the Dear Colleague Letter (DCL): Rapidly Accelerating Research on Artificial Intelligence in K-12 Education in Formal and Informal Settings (NSF 23-097) and funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.

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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