AI-BASED SIGN LANGUAGE RECOGNITION AND ITS IMPACT ON DEAF EDUCATION
Keywords:
Artificial Intelligence (AI), Sign Language Recognition (SLR), Deaf Education, Deep Learning, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM).Abstract
Sign language serves as the primary mode of communication for the deaf and hard-of-hearing community. However, communication barriers between deaf students and hearing educators continue to hinder inclusive education. Recent advancements in Artificial Intelligence (AI), particularly in Computer Vision and Deep Learning, have enabled the development of automated Sign Language Recognition (SLR) systems. This research proposes an AI-based real-time sign language recognition framework designed to enhance accessibility in deaf education. The system integrates Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to recognise both static and dynamic gestures. Experimental evaluation demonstrates improved recognition accuracy (96.3%) and reduced latency (under 200 ms), leading to measurable improvements in classroom engagement and comprehension levels. The results indicate that AI-driven SLR systems can significantly enhance learning experiences, reduce dependency on human interpreters, and promote inclusive education.
