Our Mission

One significant challenge in education is mentoring teachers, especially pre-service teachers preparing to graduate. Current instructional coaching by mentors is time-consuming, resource-intensive, and subjective, leading to inconsistent assessments. The mentor-to-student ratio intensifies this problem, reducing accuracy and fairness. An automated system is needed to provide efficient, objective, and continuous feedback, improving teaching quality and student outcomes.

This project aims to develop an AI-powered teacher improvement system that leverages audio and video analysis. Technical challenges include accurate transcription, gesture recognition, multilingual support, model integration, and secure data handling. Non-technical challenges involve privacy, ethics, legal compliance, pedagogical validity, and teacher adoption. The project is a complex computing problem involving multiple stakeholders, interdisciplinary expertise, and significant educational impact.

Expected benefits include timely and objective feedback for teachers, reducing bias, supporting professional development, and preventing burnout. Organizations benefit through scalable evaluations, resource savings, and improved teaching quality. Society gains a culture of continuous improvement, diversity in teaching, and better-educated students. Globally, the system supports UN SDG #4, enabling scalable, low-cost, AI-driven teaching evaluation applicable worldwide.