NSF Org:	DRL Division of Research on Learning in Formal and Informal Settings (DRL)
Recipient:	SOUTHERN METHODIST UNIVERSITY
Initial Amendment Date:	July 11, 2024
Latest Amendment Date:	July 11, 2024
Award Number:	2341948
Award Instrument:	Standard Grant
Program Manager:	Leilah Lyons llyons@nsf.gov  (703)292-8637 DRL  Division of Research on Learning in Formal and Informal Settings (DRL) EDU  Directorate for STEM Education
Start Date:	August 1, 2024
End Date:	July 31, 2028 (Estimated)
Total Intended Award Amount:	$1,296,683.00
Total Awarded Amount to Date:	$1,296,683.00
Funds Obligated to Date:	FY 2024 = $1,296,683.00
History of Investigator:	Candace Walkington (Principal Investigator) cwalkington@smu.edu Neil Heffernan (Co-Principal Investigator) Ryan Baker (Co-Principal Investigator) Shiting Lan (Co-Principal Investigator) Tiffini Pruitt-Britton (Co-Principal Investigator)
Recipient Sponsored Research Office:	Southern Methodist University 6425 BOAZ ST RM 130 DALLAS TX  US  75205-1902 (214)768-4708
Sponsor Congressional District:	24
Primary Place of Performance:	Southern Methodist University 6425 BOAZ LANE DALLAS TX  US  75205-1902
Primary Place of Performance Congressional District:	24
Unique Entity Identifier (UEI):	D33QGS3Q3DJ3
Parent UEI:	S88YPE3BLV66
NSF Program(s):	ITEST-Inov Tech Exp Stu & Teac
Primary Program Source:	1300PYXXDB H-1B FUND, EDU, NSF
Program Reference Code(s):	092Z, 093Z, 8212, 8244
Program Element Code(s):	722700
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.076

ABSTRACT

This Developing and Testing innovations project aims to explore the impacts and opportunities that emerge when using Generative Artificial Intelligence (GenAI) to construct personalized algebra problems for 7th grade students. Middle school algebra is targeted because research has shown that algebra courses can be a major barrier to future engagement with Science, Technology, Engineering and Math (STEM) and Information and Communication Technology (ICT) careers. The project builds on prior work that established that personalized algebra interventions that connect math to students? interest in areas like sports or video games could increase both core algebraic knowledge and STEM career interest for students with low academic achievement. Algebra performance is gatekeeper to further successful STEM study and careers, making it an ideal target for increasing the number and diversity of learners who pursue STEM study and roles in the STEM workforce. Further, this project will gain a better understanding of how to employ AI to support teachers, developing new models for how to make teaching more efficient and effective while still preserving the teachers' agency. This project is 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.

The project has identified two distinct GenAI-enabled personalization strategies to explore: a ?Greater Depth? approach that trains the AI to generate new personalized problems that are meaningful to student experiences with their interest areas, versus a ?Smaller Grain Size? approach that uses AI to modify the presentation of existing problems to more superficially reflect student interest areas but be easily scalable by teachers. These strategies likely have different affordances for both students and teachers, which will be investigated by the research. Specifically, the research questions will investigate the impact of AI-generated word problems (with and without AI-generated images) on student math performance, study the tradeoffs for both students and teachers for the two different GenAI personalization strategies, investigate changes in how teachers understand student interests and posing math story problems as a result of using GenAI, and investigate how teachers can use prompt engineering to produce personalized problems. The project will use participatory approaches to engage teachers in iterative cycles of design, increasing the likelihood that the approach will draw upon learners? funds of knowledge. The intellectual merit of this proposal lies in its exploration of employing GenAI to scale up personalization in mathematics ? it will document the opportunities, constraints, and barriers involved in using this technology for both teachers and students. It will also add to our understanding of the impact of personalization on student engagement in mathematics. Given the fact that algebra often functions as a gatekeeper to more advanced STEM courses, the potential broader impacts of this project are large ? any improvements in learner engagement in mathematics can have lasting benefits for learners? STEM trajectories, and online learning platforms where GenAI can be leveraged are widely-used in mathematics. The curriculum and models developed by this project will be made freely available and implementable via the ASSISTments platform, thus scaling up the reach of this work to tens of thousands of learners across the United States.

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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