Special Needs Classroom Assessment Using a Sign Language Communicator (CASC) Based on Artificial Intelligence (AI) Techniques

Abstract

This research focuses on deaf students in the United Arab Emirates. The proposed classroom assessment using sign language communicator (CASC) for special needs students (SN) in the United Arab Emirates is based on artificial intelligence (AI) tools. This research provides essential services for teaching evaluations, learning outcome assessments, and the development of learning environments. CASC model is composed of two models. The first model converts the speech to a sign language, which contains a speech recognizer, sign language recognizer. The second model converts the sign language to written text. This model generates a report for students' understanding and class evaluation in advance before ending the course based on the sign language recognition and image processing tools. This model will have a significantly positive impact on SN students' success and on effective lecturing and optimizing teaching and learning in the classroom. The accuracy of the model is 92%. The analysis of the student's feedback in real-time provides effective instructional strategies.