AI-Based Automation for Classroom Discourse Analysis (ADAS)
This project aims to develop an Automated Discourse Analysis (ADAS) to enhance science classroom discourse by accurately distinguishing between knowledge construction and reproduction, crucial for fostering scientific reasoning. The project seeks to overcome current limitations in AI's ability to recognize epistemic work in educational settings. Within this project scope, we expand training data, optimize various AI models for discourse analysis, measure the models' confidence in predictions, and pilot the system with diverse secondary science teachers. The goal is to provide teachers with reliable, evidence-based feedback to improve discourse patterns and reduce achievement gaps, ultimately supporting complex pedagogical reasoning in science education.
This project develops an Automated Discourse Analysis System (ADAS) to enhance science classroom discourse by accurately distinguishing between knowledge construction and reproduction — crucial for fostering scientific reasoning.
The project addresses current limitations in AI’s ability to recognize epistemic work in educational settings. Within scope, we expand training data, optimize various AI models for discourse analysis, measure model confidence, and pilot the system with diverse secondary science teachers.
Goal: Provide teachers with reliable, evidence-based feedback to improve discourse patterns and reduce achievement gaps, ultimately supporting complex pedagogical reasoning in science education.
Team: Mukhesh Raghava Katragadda, Raymond Carl, Dr. Soon Lee, Dr. Jiho Noh
Funding: Interdisciplinary Seed Grant, Kennesaw State University