SBIR Phase I: Revolutionizing Learning Through AI Augmented Debate Centered Instruction

The broader/commercial impact of this SBIR Phase I project is to transform education by developing an Artificial Intelligence (AI) enhanced debate-centered learning platform. This innovative technology addresses the crucial need for cultivating 21st-century skills in students, with a focus on critical thinking, communication, collaboration, and self-regulation. The platform provides personalized and engaging debate experiences, aiming to democratize access to high-quality enrichment education, especially for underserved communities. The innovation applies cutting-edge AI techniques to educational contexts, including multi-agent language systems, graph neural networks, and mixture of expert models, thereby enhancing scientific understanding. The target market includes K-12 schools, universities, and workplace learning programs seeking cost-effective solutions to improve student outcomes and career readiness. The technology offers a unique competitive advantage through its innovative integration of AI and debate pedagogy, providing a scalable solution for enhancing student outcomes and career readiness across diverse educational settings. The business model focuses on scalable, subscription-based software distribution, with the potential for rapid adoption across educational institutions which aims to transform how students learn to think critically, debate with evidence and reasoning, and engage with complex ideas. This Small Business Innovation Research (SBIR) Phase I project aims to develop a novel Artificial Intelligence (AI) powered platform for debate-centered instruction, addressing the complex challenge of enhancing student learning outcomes through advanced technology integration in education. The research objectives include developing an AI-powered Learning Management System for debate education, creating advanced natural language processing algorithms for argument analysis and feedback, and designing a scalable infrastructure for personalized learning experiences. The proposed research will employ a multi-faceted approach, combining techniques from machine learning, educational data mining, and cognitive science. Methods include developing and training specialized language models for debate contexts, implementing knowledge graph technologies for efficient information retrieval, and creating adaptive learning algorithms that respond to individual student growth over time. The research will investigate innovative approaches for ethical AI implementation in educational contexts, focusing on transparent oversight, bias mitigation, and privacy protection while maximizing educational benefits. Anticipated technical results include a functional prototype of the Augmented Debate-Centered Instruction platform, demonstrating improved learning outcomes in critical thinking and debate skills. The research aims to advance the field of AI in education by tackling unique challenges in debate instruction, such as real-time argument evaluation and personalized feedback generation while providing insights into optimizing human-AI collaboration in complex learning environments. 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.