SBIR Phase I: Empowering Educators with AI During Distance Learning (COVID-19)

Award ID: 2035129

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is that it will provide new tools to evaluate and enhance teaching of higher-order critical thinking. This project will leverage advanced machine learning models to generate critical-thinking questions for any text a student reads, analyze their written response, give them immediate feedback on how to refine their thinking, and ultimately provide data-driven insights to their teachers. The capability to automatically interpret open-ended responses using artificial intelligence (AI) is a rich area for the educational community. This project will deepen the education community's knowledge in this area and apply the findings to K-12 education. In particular, the results may lead to a new evaluation architecture relying less on multiple-choice questions and related techniques, enhancing education with a method to efficiently evaluate and provide feedback with open-ended and short-response questions. This project will address the problem of using multiple-choice assessments to assess learning and move to more accurate ways to ascertain the nuances of learning. This Small Business Innovation Research (SBIR) Phase I project focuses on automated short-answer scoring to solve a pressing and largely unsolved problem. Most work in automated scoring focuses on longer essay grading, which is more relevant for higher education. At this time, there are no general-purpose algorithms available for short responses. Adding to the technical complexity is the fact that this projects machine learning approach needs to work for any book or text, and include questions written by any user. This project will improve the accuracy and detail of assessments, particularly with written responses regarding texts new to the reader. This project will require deep-learning natural language processing (NLP) technology to fully model language representation. The proposed system will ingest the subject text and the associated question before evaluating the written response without prior submissions used as training data. The project prototype will be developed for English Language Arts instruction prior to broader deployment across other disciplines. 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: Rajesh Mehta