Creativity Assessment
This research aims to develop an automated creativity assessment using various AI/ML methodologies including Poly-Encoder, LLM-based regression methods, and pair-wise comparison-based ranking techniques. The project focuses on evaluating the creativity of student-generated ideas in scientific research settings, providing insights into the effectiveness of different assessment approaches and their potential applications in fostering creativity in learning environments.
This research develops an automated creativity assessment using various AI/ML methodologies including Poly-Encoder, LLM-based regression methods, and pairwise comparison-based ranking techniques.
The project focuses on evaluating the creativity of student-generated ideas in scientific research settings, providing insights into the effectiveness of different assessment approaches and their potential applications in fostering creativity in learning environments.
Team: Phillip Gregory, Sam Grouchnikov, Stanley Nurnberger, Dr. Jiho Noh
Funding: Interdisciplinary Seed Grant, Kennesaw State University