SBIR Phase II: Virtual Learning Assistants for Open Response Assessments

This SBIR Phase II project focuses on creating scalable Virtual Learning Assistant technology for automatic educational assessments using open response questions. Educational researchers and experts believe that the best pedagogies responsible for improving students' learning outcomes involve (i) open response questions assessments and (ii) one-to-one instructional tutoring. Students learn better when they are given an opportunity to construct answers in their own words instead of selecting from multiple choices and when they receive immediate guidance and coaching. However, these two pedagogies are very time consuming and expensive to implement, making them very difficult to scale. The proposed project will apply the most advanced technologies such as Artificial Intelligence and Natural Language Processing to solve both these problems. Students will benefit from the interactive formative assessment that engages them in a natural language conversation. This innovation is applicable across the grade levels in K-12, higher education, and adult learning and across the subject areas including the sciences. It will facilitate implementation of more rigorous academic standards and make online education more effective. This innovation will improve students' learning outcomes, save teachers' time and reduce the cost of delivering high quality engaging education at a large scale. This project will create a new type of virtual assistant technology that is exclusively focused on education. The proposed Virtual Learning Assistant (VLA) will advance the conversational AI technology to create pedagogically rich learning and assessment environments for any topic in a content area. The VLA is uniquely distinct from general purpose virtual assistants in its ability to evaluate an open response answer instead of merely serving information. This project will investigate and create various algorithms for processing natural language input arising in an educational setting across different subjects or topics. The resulting mobile and web based product will allow teachers to create new high-quality assessment items with minimal input and assign them to their students. When a student answers a question, the VLA will analyze it instantly for linguistic syntax and semantics using statistical and deterministic knowledge representations. The VLA will generate not only a numerical score reflecting the accuracy of the answer, but also a qualitative feedback that will guide the student towards conceptual mastery of the topic. As part of this Phase II research, a pilot study will be conducted each year involving teachers and students to study the efficacy of the VLA and its scalability. 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.