SBIR Phase II: The Smart Study Recommendations Engine

Award ID: 1951222

This Small Business Innovation Research (SBIR) Phase II project may improve student achievement. Within U.S. higher education institutions, $47 billion per year is spent on academic support for roughly 22 million students. When a child struggles after a lecture ends, help has historically been delivered by tutors and homework hotlines; Those avenues can be inadequate in closing learning gaps for students after they exit the classroom. The Smart Study Recommendations Engine is expected to further democratize homework assistance and help with studies outside the classroom. As a peer to peer platform, the technology may shrink the cost of personalized homework and out of classroom assistance, enabling students to proceed at their own time and pace. This platform seeks to especially impact students from economically- or socially-challenged backgrounds. The goal of the project is to help make academic success more attainable, common, and inclusive for all students everywhere. The technology is initially being deployed in U.S. colleges and universities, with the goal of achieving a global impact. This Small Business Innovation Phase II project harvests data from internet study resources, analyzes the resources to surface predictive insights, and automatically delivers wide-ranging, peer-reviewed, personalized study materials to help students close learning gaps, without requiring the students to perform complex internet searches. The project will also provide students with the ability to connect with capable peers who can provide additional support by listening to their issues and providing deeper subject clarity. The company is using machine learning as the underlying technology to enable the Smart Study Recommendations Engine. 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.

NSF Program Director: Benaiah Schrag