I-Corps: Translation Potential of Advanced AI Solutions to Transform Education, Research, and Institutional Efficiency

The broader impact/commercial potential of this I-Corps project is the development of an innovative initiative poised to transform the educational landscape through artificial intelligence. This technology aims to improve learning experiences by providing personalized, efficient, and accessible educational tools for students, educators, institutions, researchers, and publishers.

By leveraging advanced artificial intelligence (AI), the proposed tool enhances operational efficiency, accelerates research innovation, and democratizes access to educational resources. Central to its mission is the mitigation of inequality among students, faculty, and researchers by removing prevalent barriers to accessing information and learning resources.

This technology is particularly beneficial in improving retention rates among low-income and marginalized groups, who often face disproportionate challenges in educational settings. The commercial potential lies in its wide applicability across various educational settings, including K-12 schools, higher education institutions, professional training programs, and academic publishing.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of intelligent agents designed to cater to the specific needs of users within the academic ecosystem. The artificial intelligence (AI) tools include emotionally aware assistants for students, automated grading systems for educators, and data-driven insights for institutional decision-making.

The project builds on a decade of AI research, including advancements in adaptive learning systems, data analytics, and user experience design, integrating these advances into practical, user-focused solutions. The technology incorporates a robust Software-as-a-Service platform with a container architecture for efficient load balancing and resource allocation.

It utilizes a dual approach for its AI models, incorporating Application Programming Interface (API) based models for advanced natural language tasks and open-source tools for textual and multimodal analysis. The tool showcases significant intellectual advantages, such as flexibility in model selection, fine-tuned domain-specific models, and comprehensive review capabilities.

This approach promises to enhance educational outcomes and operational efficiencies, ultimately contributing to a more educated and technologically adept society.

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.