SBIR Phase I: Qalaxia: skill-aware query engine for K12 Classrooms

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This SBIR Phase I project builds a new query engine for K12 that addresses four major shortcomings in existing automated question-answering systems. The shortcomings prevent both students and teachers from fully leveraging the power of such systems. Existing systems 1) cannot handle raw student queries like - Ten Ford Fiestas cost $146,000. How much does each Ford Fiesta cost?, 2) do not take into account students' skill-levels to return results that is within the scope of their understanding, 3) do not have a community vetting and feedback mechanism that allows systems to learn the quality of their automated answers, and 4) discard valuable insights obtainable from students' queries that can be used to inform future classroom instruction. The query engine, which is free to use, addresses all four shortcomings, improves classroom instruction and closes the achievement gap. It empowers students of all abilities and from all communities to obtain automated answers from the AI technology and regular answers from the community of users. The community of users on the platform range from students to teachers to experts from the industry fulfilling their corporate social responsibilities. The platform is compliant with federal and state privacy regulations and is projected to earn significant Annual Recurring Revenue from corporations and EdTech companies seeking to build their brand among the K12 community. This project develops new skill-aware AI technology that automatically maps student's raw queries into topics, sub-topics and intent. The technology considers the user skill-levels and automatically constructs an answer extracted from the most relevant educational resources, with highest preference given to teachers' own resources. The technology does something more if the detected topic falls under Algebra and the intent of the query is 'How do I solve this?' It generates, on-the-fly, additional scaffolding instructions for solving the problem. This approach extends to disciplines beyond Algebra. In addition, Phase I research leverages the latest advancements in Machine Learning, Natural Language Processing and Topic Detection to extract and track skill-levels, as well as strengths and weaknesses of students for teacher's benefit. Finally, the project will also demonstrate the efficacy of the technology in terms of queries made, relevant responses generated and its impact in terms of Algebra math outcomes.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.